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accounts analysis</title> <meta name="description" content="Search results for: twitter accounts analysis"> <meta name="keywords" content="twitter accounts analysis"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="twitter accounts analysis"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 28253</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: twitter accounts analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28253</span> Emotions in Health Tweets: Analysis of American Government Official Accounts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Garc%C3%ADa%20L%C3%B3pez">García López</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Government Departments of Health have the task of informing and educating citizens about public health issues. For this, they use channels like Twitter, key in the search for health information and the propagation of content. The tweets, important in the virality of the content, may contain emotions that influence the contagion and exchange of knowledge. The goal of this study is to perform an analysis of the emotional projection of health information shared on Twitter by official American accounts: the disease control account <em>CDCgov</em>, National Institutes of Health, <em>NIH</em>, the government agency <em>HHSGov</em>, and the professional organization <em>PublicHealth</em>. For this, we used Tone Analyzer, an International Business Machines Corporation (IBM) tool specialized in emotion detection in text, corresponding to the categorical model of emotion representation. For 15 days, all tweets from these accounts were analyzed with the emotional analysis tool in text. The results showed that their tweets contain an important emotional load, a determining factor in the success of their communications. This exposes that official accounts also use subjective language and contain emotions. The predominance of emotion joy over sadness and the strong presence of emotions in their tweets stimulate the virality of content, a key in the work of informing that government health departments have. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotions%20in%20tweets" title="emotions in tweets">emotions in tweets</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20detection%20in%20the%20text" title=" emotion detection in the text"> emotion detection in the text</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20information%20on%20Twitter" title=" health information on Twitter"> health information on Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=American%20health%20official%20accounts" title=" American health official accounts"> American health official accounts</a>, <a href="https://publications.waset.org/abstracts/search?q=emotions%20on%20Twitter" title=" emotions on Twitter"> emotions on Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=emotions%20and%20content" title=" emotions and content"> emotions and content</a> </p> <a href="https://publications.waset.org/abstracts/95743/emotions-in-health-tweets-analysis-of-american-government-official-accounts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95743.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28252</span> Fake Accounts Detection in Twitter Based on Minimum Weighted Feature Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20ElAzab">Ahmed ElAzab</a>, <a href="https://publications.waset.org/abstracts/search?q=Amira%20M.%20Idrees"> Amira M. Idrees</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20A.%20Mahmoud"> Mahmoud A. Mahmoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Hesham%20Hefny"> Hesham Hefny</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fake%20accounts%20detection" title="fake accounts detection">fake accounts detection</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20algorithms" title=" classification algorithms"> classification algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter%20accounts%20analysis" title=" twitter accounts analysis"> twitter accounts analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20based%20techniques" title=" features based techniques"> features based techniques</a> </p> <a href="https://publications.waset.org/abstracts/41564/fake-accounts-detection-in-twitter-based-on-minimum-weighted-feature-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28251</span> Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niddal%20Imam">Niddal Imam</a>, <a href="https://publications.waset.org/abstracts/search?q=Vassilios%20G.%20Vassilakis"> Vassilios G. Vassilakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> After analysing messages posted by health-related spam campaigns in Twitter Arabic hashtags, we found that these campaigns use unique hijacked accounts (we call them adversarial hijacked accounts) as adversarial examples to fool deployed ML-based spam detectors. Existing ML-based models build a behaviour profile for each user to detect hijacked accounts. This approach is not applicable for detecting spam in Twitter hashtags since they are computationally expensive. Hence, we propose an adversary-aware ML-based detector, which includes a newly designed feature (avg posts) to improve the detection of spam tweets posted by the adversarial hijacked accounts at a tweet-level in trending hashtags. The proposed detector was designed considering three key points: robustness, adaptability, and interpretability. The new feature leverages the account’s temporal patterns (i.e., account age and number of posts). It is faster to compute compared to features discussed in the literature and improves the accuracy of detecting the identified hijacked accounts by 73%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Twitter%20spam%20detection" title="Twitter spam detection">Twitter spam detection</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20examples" title=" adversarial examples"> adversarial examples</a>, <a href="https://publications.waset.org/abstracts/search?q=evasion%20attack" title=" evasion attack"> evasion attack</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20concept%20drift" title=" adversarial concept drift"> adversarial concept drift</a>, <a href="https://publications.waset.org/abstracts/search?q=account%20hijacking" title=" account hijacking"> account hijacking</a>, <a href="https://publications.waset.org/abstracts/search?q=trending%20hashtag" title=" trending hashtag"> trending hashtag</a> </p> <a href="https://publications.waset.org/abstracts/157771/towards-an-adversary-aware-ml-based-detector-of-spam-on-twitter-hashtags" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157771.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28250</span> The Role of Twitter Bots in Political Discussion on 2019 European Elections</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomai%20Voulgari">Thomai Voulgari</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasilis%20Vasilopoulos"> Vasilis Vasilopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonis%20Skamnakis"> Antonis Skamnakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is to investigate the effect of the European election campaigns (May 23-26, 2019) on Twitter achieving with artificial intelligence tools such as troll factories and automated inauthentic accounts. Our research focuses on the last European Parliamentary elections that took place between 23 and 26 May 2019 specifically in Italy, Greece, Germany and France. It is difficult to estimate how many Twitter users are actually bots (Echeverría, 2017). Detection for fake accounts is becoming even more complicated as AI bots are made more advanced. A political bot can be programmed to post comments on a Twitter account for a political candidate, target journalists with manipulated content or engage with politicians and artificially increase their impact and popularity. We analyze variables related to 1) the scope of activity of automated bots accounts and 2) degree of coherence and 3) degree of interaction taking into account different factors, such as the type of content of Twitter messages and their intentions, as well as the spreading to the general public. For this purpose, we collected large volumes of Twitter accounts of party leaders and MEP candidates between 10th of May and 26th of July based on content analysis of tweets based on hashtags while using an innovative network analysis tool known as MediaWatch.io (https://mediawatch.io/). According to our findings, one of the highest percentage (64.6%) of automated “bot” accounts during 2019 European election campaigns was in Greece. In general terms, political bots aim to proliferation of misinformation on social media. Targeting voters is a way that it can be achieved contribute to social media manipulation. We found that political parties and individual politicians create and promote purposeful content on Twitter using algorithmic tools. Based on this analysis, online political advertising play an important role to the process of spreading misinformation during elections campaigns. Overall, inauthentic accounts and social media algorithms are being used to manipulate political behavior and public opinion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence%20tools" title="artificial intelligence tools">artificial intelligence tools</a>, <a href="https://publications.waset.org/abstracts/search?q=human-bot%20interactions" title=" human-bot interactions"> human-bot interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=political%20manipulation" title=" political manipulation"> political manipulation</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networking" title=" social networking"> social networking</a>, <a href="https://publications.waset.org/abstracts/search?q=troll%20factories" title=" troll factories"> troll factories</a> </p> <a href="https://publications.waset.org/abstracts/129315/the-role-of-twitter-bots-in-political-discussion-on-2019-european-elections" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129315.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28249</span> Effects of Twitter Interactions on Self-Esteem and Narcissistic Behaviour</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leena-Maria%20Alyedreessy">Leena-Maria Alyedreessy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Self-esteem is thought to be determined by ones’ own feeling of being included, liked and accepted by others. This research explores whether this concept may also be applied in the virtual world and assesses whether there is any relationship between Twitter users' self-esteem and the amount of interactions they receive. 20 female Arab participants were given a survey asking them about their Twitter interactions and their feelings of having an imagined audience to fill out and a Rosenberg Self-Esteem Assessment to complete. After completion and statistical analysis, results showed a significant correlation between the feeling of being Twitter elite, the feeling of having a lot of people listening to your tweets and having a lot of interactions with high self-esteem. However, no correlations were detected for low-self-esteem and low interactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=twitter" title="twitter">twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=self-esteem" title=" self-esteem"> self-esteem</a>, <a href="https://publications.waset.org/abstracts/search?q=narcissism" title=" narcissism"> narcissism</a>, <a href="https://publications.waset.org/abstracts/search?q=interactions" title=" interactions"> interactions</a> </p> <a href="https://publications.waset.org/abstracts/7501/effects-of-twitter-interactions-on-self-esteem-and-narcissistic-behaviour" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7501.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28248</span> Survey on Arabic Sentiment Analysis in Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarah%20O.%20Alhumoud">Sarah O. Alhumoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Mawaheb%20I.%20Altuwaijri"> Mawaheb I. Altuwaijri</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarfa%20M.%20Albuhairi"> Tarfa M. Albuhairi</a>, <a href="https://publications.waset.org/abstracts/search?q=Wejdan%20M.%20Alohaideb"> Wejdan M. Alohaideb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Large-scale data stream analysis has become one of the important business and research priorities lately. Social networks like Twitter and other micro-blogging platforms hold an enormous amount of data that is large in volume, velocity and variety. Extracting valuable information and trends out of these data would aid in a better understanding and decision-making. Multiple analysis techniques are deployed for English content. Moreover, one of the languages that produce a large amount of data over social networks and is least analyzed is the Arabic language. The proposed paper is a survey on the research efforts to analyze the Arabic content in Twitter focusing on the tools and methods used to extract the sentiments for the Arabic content on Twitter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networks" title=" social networks"> social networks</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a> </p> <a href="https://publications.waset.org/abstracts/20049/survey-on-arabic-sentiment-analysis-in-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20049.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">576</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28247</span> A Framework for Analyzing Public Interaction of Saudi Universities on Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sahar%20Al-Qahtani">Sahar Al-Qahtani</a>, <a href="https://publications.waset.org/abstracts/search?q=Rabeeh%20Ayaz%20Abbasi"> Rabeeh Ayaz Abbasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Naif%20Radi%20Aljohani"> Naif Radi Aljohani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many universities use social media platforms as new communication channels to disseminate information and promptly communicate with their audience. As Twitter is one of the widely used social media platforms, this research aims to explore the adaption and utilization of Twitter by universities. We propose a framework called 'Social Network Analysis for Universities on Twitter' (SNAUT) to analyze the usage of Twitter by universities and to measure their interaction with public. The study includes a sample of around 110,000 tweets from 36 Saudi universities, including both public and private universities. Using SNAUT, we can (1) investigate the purpose of using Twitter by universities, (2) determine the broad topics discussed by them, and (3) identify the groups closely associated with the universities. The results show that most of the Saudi universities (whether public or private) actively use Twitter. Results also reveal that public universities respond to public queries more frequently, but private universities stand out more in terms of information dissemination using retweets and diverse hashtags. Finally, we develop a ranking mechanism in SNAUT for ranking universities based on their social interaction with the public on Twitter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=social%20media" title="social media">social media</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=universities" title=" universities"> universities</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20education" title=" higher education"> higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=Saudi%20Arabia" title=" Saudi Arabia"> Saudi Arabia</a> </p> <a href="https://publications.waset.org/abstracts/121965/a-framework-for-analyzing-public-interaction-of-saudi-universities-on-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121965.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28246</span> The Paralinguistic Function of Emojis in Twitter Communication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasmin%20Tantawi">Yasmin Tantawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mary%20Beth%20Rosson"> Mary Beth Rosson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In response to the dearth of information about emoji use for different purposes in different settings, this paper investigates the paralinguistic function of emojis within Twitter communication in the United States. To conduct this investigation, the Twitter feeds from 16 population centers spread throughout the United States were collected from the Twitter public API. One hundred tweets were collected from each population center, totaling to 1,600 tweets. Tweets containing emojis were next extracted using the “emot” Python package; these were then analyzed via the IBM Watson API Natural Language Understanding module to identify the topics discussed. A manual content analysis was then conducted to ascertain the paralinguistic and emotional features of the emojis used in these tweets. We present our characterization of emoji usage in Twitter and discuss implications for the design of Twitter and other text-based communication tools. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer-mediated%20communication" title="computer-mediated communication">computer-mediated communication</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20analysis" title=" content analysis"> content analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=paralinguistics" title=" paralinguistics"> paralinguistics</a>, <a href="https://publications.waset.org/abstracts/search?q=sociology" title=" sociology"> sociology</a> </p> <a href="https://publications.waset.org/abstracts/107115/the-paralinguistic-function-of-emojis-in-twitter-communication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">160</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28245</span> Twitter's Impact on Print Media with Respect to Real World Events</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Basit%20Shahzad">Basit Shahzad</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullatif%20M.%20Abdullatif"> Abdullatif M. Abdullatif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent advancements in Information and Communication Technologies (ICT) and easy access to Internet have made social media the first choice for information sharing related to any important events or news. On Twitter, trend is a common feature that quantifies the level of popularity of a certain news or event. In this work, we examine the impact of Twitter trends on real world events by hypothesizing that Twitter trends have an influence on print media in Pakistan. For this, Twitter is used as a platform and Twitter trends as a base line. We first collect data from two sources (Twitter trends and print media) in the period May to August 2016. Obtained data from two sources is analyzed and it is observed that social media is significantly influencing the print media and majority of the news printed in newspaper are posted on Twitter earlier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=twitter%20trends" title="twitter trends">twitter trends</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=effectiveness%20of%20trends" title=" effectiveness of trends"> effectiveness of trends</a>, <a href="https://publications.waset.org/abstracts/search?q=print%20media" title=" print media"> print media</a> </p> <a href="https://publications.waset.org/abstracts/70912/twitters-impact-on-print-media-with-respect-to-real-world-events" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70912.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">258</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28244</span> Analysis of Urban Population Using Twitter Distribution Data: Case Study of Makassar City, Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuyun%20Wabula">Yuyun Wabula</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20J.%20Dewancker"> B. J. Dewancker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past decade, the social networking app has been growing very rapidly. Geolocation data is one of the important features of social media that can attach the user's location coordinate in the real world. This paper proposes the use of geolocation data from the Twitter social media application to gain knowledge about urban dynamics, especially on human mobility behavior. This paper aims to explore the relation between geolocation Twitter with the existence of people in the urban area. Firstly, the study will analyze the spread of people in the particular area, within the city using Twitter social media data. Secondly, we then match and categorize the existing place based on the same individuals visiting. Then, we combine the Twitter data from the tracking result and the questionnaire data to catch the Twitter user profile. To do that, we used the distribution frequency analysis to learn the visitors’ percentage. To validate the hypothesis, we compare it with the local population statistic data and land use mapping released by the city planning department of Makassar local government. The results show that there is the correlation between Twitter geolocation and questionnaire data. Thus, integration the Twitter data and survey data can reveal the profile of the social media users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geolocation" title="geolocation">geolocation</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution%20analysis" title=" distribution analysis"> distribution analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20mobility" title=" human mobility"> human mobility</a> </p> <a href="https://publications.waset.org/abstracts/55451/analysis-of-urban-population-using-twitter-distribution-data-case-study-of-makassar-city-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55451.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">314</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28243</span> Official Game Account Analysis: Factors Influence Users' Judgments in Limited-Word Posts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shanhua%20Hu">Shanhua Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social media as a critical propagandizing form of film, video games, and digital products has received substantial research attention, but there exists several critical barriers such as: (1) few studies exploring the internal and external connections of a product as part of the multimodal context that gives rise to readability and commercial return; (2) the lack of study of multimodal analysis in product’s official account of game publishers and its impact on users’ behaviors including purchase intention, social media engagement, and playing time; (3) no standardized ecologically-valid, game type-varying data can be used to study the complexity of official account’s postings within a time period. This proposed research helps to tackle these limitations in order to develop a model of readability study that is more ecologically valid, robust, and thorough. To accomplish this objective, this paper provides a more diverse dataset comprising different visual elements and messages collected from the official Twitter accounts of the Top 20 best-selling games of 2021. Video game companies target potential users through social media, a popular approach is to set up an official account to maintain exposure. Typically, major game publishers would create an official account on Twitter months before the game's release date to update on the game's development, announce collaborations, and reveal spoilers. Analyses of tweets from those official Twitter accounts would assist publishers and marketers in identifying how to efficiently and precisely deploy advertising to increase game sales. The purpose of this research is to determine how official game accounts use Twitter to attract new customers, specifically which types of messages are most effective at increasing sales. The dataset includes the number of days until the actual release date on Twitter posts, the readability of the post (Flesch Reading Ease Score, FRES), the number of emojis used, the number of hashtags, the number of followers of the mentioned users, the categorization of the posts (i.e., spoilers, collaborations, promotions), and the number of video views. The timeline of Twitter postings from official accounts will be compared to the history of pre-orders and sales figures to determine the potential impact of social media posts. This study aims to determine how the above-mentioned characteristics of official accounts' Twitter postings influence the sales of the game and to examine the possible causes of this influence. The outcome will provide researchers with a list of potential aspects that could influence people's judgments in limited-word posts. With the increased average online time, users would adapt more quickly than before in online information exchange and readings, such as the word to use sentence length, and the use of emojis or hashtags. The study on the promotion of official game accounts will not only enable publishers to create more effective promotion techniques in the future but also provide ideas for future research on the influence of social media posts with a limited number of words on consumers' purchasing decisions. Future research can focus on more specific linguistic aspects, such as precise word choice in advertising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=engagement" title="engagement">engagement</a>, <a href="https://publications.waset.org/abstracts/search?q=official%20account" title=" official account"> official account</a>, <a href="https://publications.waset.org/abstracts/search?q=promotion" title=" promotion"> promotion</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20game" title=" video game"> video game</a> </p> <a href="https://publications.waset.org/abstracts/159347/official-game-account-analysis-factors-influence-users-judgments-in-limited-word-posts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159347.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">76</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28242</span> A Structured Mechanism for Identifying Political Influencers on Social Media Platforms: Top 10 Saudi Political Twitter Users</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Alsolami">Ahmad Alsolami</a>, <a href="https://publications.waset.org/abstracts/search?q=Darren%20Mundy"> Darren Mundy</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Hernandez-Perez"> Manuel Hernandez-Perez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social media networks, such as Twitter, offer the perfect opportunity to either positively or negatively affect political attitudes on large audiences. The existence of influential users who have developed a reputation for their knowledge and experience of specific topics is a major factor contributing to this impact. Therefore, knowledge of the mechanisms to identify influential users on social media is vital for understanding their effect on their audience. The concept of the influential user is related to the concept of opinion leaders' to indicate that ideas first flow from mass media to opinion leaders and then to the rest of the population. Hence, the objective of this research was to provide reliable and accurate structural mechanisms to identify influential users, which could be applied to different platforms, places, and subjects. Twitter was selected as the platform of interest, and Saudi Arabia as the context for the investigation. These were selected because Saudi Arabia has a large number of Twitter users, some of whom are considerably active in setting agendas and disseminating ideas. The study considered the scientific methods that have been used to identify public opinion leaders before, utilizing metrics software on Twitter. The key findings propose multiple novel metrics to compare Twitter influencers, including the number of followers, social authority and the use of political hashtags, and four secondary filtering measures. Thus, using ratio and percentage calculations to classify the most influential users, Twitter accounts were filtered, analyzed and included. The structured approach is used as a mechanism to explore the top ten influencers on Twitter from the political domain in Saudi Arabia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Twitter" title="Twitter">Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=influencers" title=" influencers"> influencers</a>, <a href="https://publications.waset.org/abstracts/search?q=structured%20mechanism" title=" structured mechanism"> structured mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=Saudi%20Arabia" title=" Saudi Arabia"> Saudi Arabia</a> </p> <a href="https://publications.waset.org/abstracts/130517/a-structured-mechanism-for-identifying-political-influencers-on-social-media-platforms-top-10-saudi-political-twitter-users" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130517.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28241</span> Text Mining of Twitter Data Using a Latent Dirichlet Allocation Topic Model and Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sidi%20Yang">Sidi Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Haiyi%20Zhang"> Haiyi Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is a microblogging platform, where millions of users daily share their attitudes, views, and opinions. Using a probabilistic Latent Dirichlet Allocation (LDA) topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood. The primary goal of this paper is to explore text mining, extract and analyze useful information from unstructured text using two approaches: LDA topic modelling and sentiment analysis by examining Twitter plain text data in English. These two methods allow people to dig data more effectively and efficiently. LDA topic model and sentiment analysis can also be applied to provide insight views in business and scientific fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title="text mining">text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20model" title=" topic model"> topic model</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a> </p> <a href="https://publications.waset.org/abstracts/95281/text-mining-of-twitter-data-using-a-latent-dirichlet-allocation-topic-model-and-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95281.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">179</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28240</span> A Structured Mechanism for Identifying Political Influencers on Social Media Platforms Top 10 Saudi Political Twitter Users </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Alsolami">Ahmad Alsolami</a>, <a href="https://publications.waset.org/abstracts/search?q=Darren%20Mundy"> Darren Mundy</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Hernandez-Perez"> Manuel Hernandez-Perez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social media networks, such as Twitter, offer the perfect opportunity to either positively or negatively affect political attitudes on large audiences. A most important factor contributing to this effect is the existence of influential users, who have developed a reputation for their awareness and experience on specific subjects. Therefore, knowledge of the mechanisms to identify influential users on social media is vital for understanding their effect on their audience. The concept of the influential user is based on the pioneering work of Katz and Lazarsfeld (1959), who created the concept of opinion leaders' to indicate that ideas first flow from mass media to opinion leaders and then to the rest of the population. Hence, the objective of this research was to provide reliable and accurate structural mechanisms to identify influential users, which could be applied to different platforms, places, and subjects. Twitter was selected as the platform of interest, and Saudi Arabia as the context for the investigation. These were selected because Saudi Arabia has a large number of Twitter users, some of whom are considerably active in setting agendas and disseminating ideas. The study considered the scientific methods that have been used to identify public opinion leaders before, utilizing metrics software on Twitter. The key findings propose multiple novel metrics to compare Twitter influencers, including the number of followers, social authority and the use of political hashtags, and four secondary filtering measures. Thus, using ratio and percentage calculations to classify the most influential users, Twitter accounts were filtered, analyzed and included. The structured approach is used as a mechanism to explore the top ten influencers on Twitter from the political domain in Saudi Arabia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=twitter" title="twitter">twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=influencers" title=" influencers"> influencers</a>, <a href="https://publications.waset.org/abstracts/search?q=structured%20mechanism" title=" structured mechanism"> structured mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=Saudi%20Arabia" title=" Saudi Arabia "> Saudi Arabia </a> </p> <a href="https://publications.waset.org/abstracts/135822/a-structured-mechanism-for-identifying-political-influencers-on-social-media-platforms-top-10-saudi-political-twitter-users" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135822.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28239</span> Collision Theory Based Sentiment Detection Using Discourse Analysis in Hadoop</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anuta%20Mukherjee">Anuta Mukherjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Saswati%20Mukherjee"> Saswati Mukherjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title="sentiment analysis">sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20theory" title=" collision theory"> collision theory</a>, <a href="https://publications.waset.org/abstracts/search?q=discourse%20analysis" title=" discourse analysis"> discourse analysis</a> </p> <a href="https://publications.waset.org/abstracts/36495/collision-theory-based-sentiment-detection-using-discourse-analysis-in-hadoop" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36495.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">535</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28238</span> Unsupervised Sentiment Analysis for Indonesian Political Message on Twitter </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Abdillah">Omar Abdillah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mirna%20Adriani"> Mirna Adriani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we perform new approach for analyzing public sentiment towards the presidential candidate in the 2014 Indonesian election that expressed in Twitter. In this study we propose such procedure for analyzing sentiment over Indonesian political message by understanding the behavior of Indonesian society in sending message on Twitter. We took different approach from previous works by utilizing punctuation mark and Indonesian sentiment lexicon that completed with the new procedure in determining sentiment towards the candidates. Our experiment shows the performance that yields up to 83.31% of average precision. In brief, this work makes two contributions: first, this work is the preliminary study of sentiment analysis in the domain of political message that has not been addressed yet before. Second, we propose such method to conduct sentiment analysis by creating decision making procedure in which it is in line with the characteristic of Indonesian message on Twitter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20sentiment%20analysis" title="unsupervised sentiment analysis">unsupervised sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=political%20message" title=" political message"> political message</a>, <a href="https://publications.waset.org/abstracts/search?q=lexicon%20based" title=" lexicon based"> lexicon based</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20behavior%20understanding" title=" user behavior understanding"> user behavior understanding</a> </p> <a href="https://publications.waset.org/abstracts/20304/unsupervised-sentiment-analysis-for-indonesian-political-message-on-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20304.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">480</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28237</span> Short Text Classification for Saudi Tweets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asma%20A.%20Alsufyani">Asma A. Alsufyani</a>, <a href="https://publications.waset.org/abstracts/search?q=Maram%20A.%20Alharthi"> Maram A. Alharthi</a>, <a href="https://publications.waset.org/abstracts/search?q=Maha%20J.%20Althobaiti"> Maha J. Althobaiti</a>, <a href="https://publications.waset.org/abstracts/search?q=Manal%20S.%20Alharthi"> Manal S. Alharthi</a>, <a href="https://publications.waset.org/abstracts/search?q=Huda%20Rizq"> Huda Rizq</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is one of the most popular microblogging sites that allows users to publish short text messages called 'tweets'. Increasing the number of accounts to follow (followings) increases the number of tweets that will be displayed from different topics in an unclassified manner in the timeline of the user. Therefore, it can be a vital solution for many Twitter users to have their tweets in a timeline classified into general categories to save the user’s time and to provide easy and quick access to tweets based on topics. In this paper, we developed a classifier for timeline tweets trained on a dataset consisting of 3600 tweets in total, which were collected from Saudi Twitter and annotated manually. We experimented with the well-known Bag-of-Words approach to text classification, and we used support vector machines (SVM) in the training process. The trained classifier performed well on a test dataset, with an average F1-measure equal to 92.3%. The classifier has been integrated into an application, which practically proved the classifier’s ability to classify timeline tweets of the user. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corpus%20creation" title="corpus creation">corpus creation</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20text%20classification" title=" short text classification"> short text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a> </p> <a href="https://publications.waset.org/abstracts/130952/short-text-classification-for-saudi-tweets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130952.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">155</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28236</span> Social Media Mining with R. Twitter Analyses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diana%20Codat">Diana Codat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tweets' analysis is part of text mining. Each document is a written text. It's possible to apply the usual text search techniques, in particular by switching to the bag-of-words representation. But the tweets induce peculiarities. Some may enrich the analysis. Thus, their length is calibrated (at least as far as public messages are concerned), special characters make it possible to identify authors (@) and themes (#), the tweet and retweet mechanisms make it possible to follow the diffusion of the information. Conversely, other characteristics may disrupt the analyzes. Because space is limited, authors often use abbreviations, emoticons to express feelings, and they do not pay much attention to spelling. All this creates noise that can complicate the task. The tweets carry a lot of potentially interesting information. Their exploitation is one of the main axes of the analysis of the social networks. We show how to access Twitter-related messages. We will initiate a study of the properties of the tweets, and we will follow up on the exploitation of the content of the messages. We will work under R with the package 'twitteR'. The study of tweets is a strong focus of analysis of social networks because Twitter has become an important vector of communication. This example shows that it is easy to initiate an analysis from data extracted directly online. The data preparation phase is of great importance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20R" title=" language R"> language R</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networks" title=" social networks"> social networks</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter "> Twitter </a> </p> <a href="https://publications.waset.org/abstracts/81942/social-media-mining-with-r-twitter-analyses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">184</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28235</span> EFL Saudi Students' Use of Vocabulary via Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Alshabeb">A. Alshabeb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vocabulary is one of the elements that links the four skills of reading, writing, speaking, and listening and is very critical in learning a foreign language. This study aims to determine how Saudi Arabian EFL students learn English vocabulary via Twitter. The study adopts a mixed sequential research design in collecting and analysing data. The results of the study provide several recommendations for vocabulary learning. Moreover, the study can help teachers to consider the possibilities of using Twitter further, and perhaps to develop new approaches to vocabulary teaching and to support students in their use of social media. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=social%20media" title="social media">social media</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=vocabulary" title=" vocabulary"> vocabulary</a>, <a href="https://publications.waset.org/abstracts/search?q=web%202" title=" web 2"> web 2</a> </p> <a href="https://publications.waset.org/abstracts/31383/efl-saudi-students-use-of-vocabulary-via-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31383.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">419</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28234</span> Topic Modelling Using Latent Dirichlet Allocation and Latent Semantic Indexing on SA Telco Twitter Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phumelele%20Kubheka">Phumelele Kubheka</a>, <a href="https://publications.waset.org/abstracts/search?q=Pius%20Owolawi"> Pius Owolawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gbolahan%20Aiyetoro"> Gbolahan Aiyetoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20Dirichlet%20allocation" title=" latent Dirichlet allocation"> latent Dirichlet allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20semantic%20indexing" title=" latent semantic indexing"> latent semantic indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=telco" title=" telco"> telco</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a> </p> <a href="https://publications.waset.org/abstracts/147818/topic-modelling-using-latent-dirichlet-allocation-and-latent-semantic-indexing-on-sa-telco-twitter-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147818.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28233</span> Twitter: The New Marketing Communication Tools</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansur%20Ahmed%20Kazaure">Mansur Ahmed Kazaure</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The emergence of internet-based social media has made it possible for one person to communication with hundreds or even thousands of people about a company goods and services and the companies that provides them. Thus, the impact of customer-to-customer communications has been significantly magnified in the marketplace. Therefore, the essence of this paper is to critically evaluate the literature of social media and their implication for practice, but the author pay attention on twitter as a new marketing communication tools. The author found out that, despite the implication of using social media especially twitter by the companies as part of their marketing communication tool, but still it can enhance the opportunity for the companies to develop and maintain long-term customer relationship. The paper concludes that, using twitter as a marketing communication tool is a market trend and it is the best way for marketers to add value to their customer, however with the Twitter marketers can get a feedback about the performance of their product and its brand in the marketplace. The paper is purely a conceptual discourse based on secondary data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=social%20media" title="social media">social media</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing%20communication" title=" marketing communication"> marketing communication</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing%20communication%20tools" title=" marketing communication tools"> marketing communication tools</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=Facebook" title=" Facebook"> Facebook</a> </p> <a href="https://publications.waset.org/abstracts/19686/twitter-the-new-marketing-communication-tools" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19686.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">473</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28232</span> Semantic Network Analysis of the Saudi Women Driving Decree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dania%20Aljouhi">Dania Aljouhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> September 26th, 2017, is a historic date for all women in Saudi Arabia. On that day, Saudi Arabia announced the decree on allowing Saudi women to drive. With the advent of vision 2030 and its goal to empower women and increase their participation in Saudi society, we see how Saudis’ Twitter users deliberate the 2017 decree from different social, cultural, religious, economic and political factors. This topic bridges social media 'Twitter,' gender and social-cultural studies to offer insights into how Saudis’ tweets reflect a broader discourse on Saudi women in the age of social media. The present study aims to explore the meanings and themes that emerge by Saudis’ Twitter users in response to the 2017 royal decree on women driving. The sample used in the current study involves (n= 1000) tweets that were collected from Sep 2017 to March 2019 to account for the Saudis’ tweets before and after implementing the decree. The paper uses semantic and thematic network analysis methods to examine the Saudis’ Twitter discourse on the women driving issue. The paper argues that Twitter as a platform has mediated the discourse of women driving among the Saudi community and facilitated social changes. Finally, framing theory (Goffman, 1974) and Networked framing (Meraz & Papacharissi 2013) are both used to explain the tweets on the decree of allowing Saudi women to drive based on # Saudi women-driving-cars. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saudi%20Arabia" title="Saudi Arabia">Saudi Arabia</a>, <a href="https://publications.waset.org/abstracts/search?q=women" title=" women"> women</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20network%20analysis" title=" semantic network analysis"> semantic network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=framing" title=" framing "> framing </a> </p> <a href="https://publications.waset.org/abstracts/112269/semantic-network-analysis-of-the-saudi-women-driving-decree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112269.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">155</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28231</span> Saudi Twitter Corpus for Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Assiri">Adel Assiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Emam"> Ahmed Emam</a>, <a href="https://publications.waset.org/abstracts/search?q=Hmood%20Al-Dossari"> Hmood Al-Dossari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis (SA) has received growing attention in Arabic language research. However, few studies have yet to directly apply SA to Arabic due to lack of a publicly available dataset for this language. This paper partially bridges this gap due to its focus on one of the Arabic dialects which is the Saudi dialect. This paper presents annotated data set of 4700 for Saudi dialect sentiment analysis with (K= 0.807). Our next work is to extend this corpus and creation a large-scale lexicon for Saudi dialect from the corpus. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic" title="Arabic">Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a> </p> <a href="https://publications.waset.org/abstracts/44819/saudi-twitter-corpus-for-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44819.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">629</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28230</span> Opinion Mining and Sentiment Analysis on DEFT</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najiba%20Ouled%20Omar">Najiba Ouled Omar</a>, <a href="https://publications.waset.org/abstracts/search?q=Azza%20Harbaoui"> Azza Harbaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Henda%20Ben%20Ghezala"> Henda Ben Ghezala</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Current research practices sentiment analysis with a focus on social networks, DEfi Fouille de Texte (DEFT) (Text Mining Challenge) evaluation campaign focuses on opinion mining and sentiment analysis on social networks, especially social network Twitter. It aims to confront the systems produced by several teams from public and private research laboratories. DEFT offers participants the opportunity to work on regularly renewed themes and proposes to work on opinion mining in several editions. The purpose of this article is to scrutinize and analyze the works relating to opinions mining and sentiment analysis in the Twitter social network realized by DEFT. It examines the tasks proposed by the organizers of the challenge and the methods used by the participants. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title="opinion mining">opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion" title=" emotion"> emotion</a>, <a href="https://publications.waset.org/abstracts/search?q=polarity" title=" polarity"> polarity</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=OSEE" title=" OSEE"> OSEE</a>, <a href="https://publications.waset.org/abstracts/search?q=figurative%20language" title=" figurative language"> figurative language</a>, <a href="https://publications.waset.org/abstracts/search?q=DEFT" title=" DEFT"> DEFT</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=Tweet" title=" Tweet"> Tweet</a> </p> <a href="https://publications.waset.org/abstracts/130709/opinion-mining-and-sentiment-analysis-on-deft" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130709.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28229</span> Charting Sentiments with Naive Bayes and Logistic Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jummalla%20Aashrith">Jummalla Aashrith</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20L.%20Shiva%20Sai"> N. L. Shiva Sai</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Bhavya%20Sri"> K. Bhavya Sri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=visualisation" title=" visualisation"> visualisation</a>, <a href="https://publications.waset.org/abstracts/search?q=python" title=" python"> python</a> </p> <a href="https://publications.waset.org/abstracts/180949/charting-sentiments-with-naive-bayes-and-logistic-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/180949.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">56</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28228</span> Women Hashtactivism: Civic Engagement in Saudi Arabia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ibahrine">Mohammed Ibahrine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the prominent trends in the Saudi digital space in recent years is the boom in the use of social networking sites such as Facebook, YouTube, and Twitter. As of 2016, Twitter has over six million users in Saudi Arabia. In the wake of the recent political instability in the Arab region, digital platforms have gained importance for both, personal and professional purposes. A conspicuously observable tide of social activism has risen, with Twitter playing an increasingly important role. One of their primary goals is to enforce the logic of public visibility, social mobility and civic participation in the Saudi society. Saudi women use Twitter to disseminate specific and relevant information and promote their social agenda that remained unrecognized and invisible in the mainstream media and thus in the public sphere. The question is to what extent does Twitter empower Saudi women or reinforces their social immobility and invisibility? This paper focuses on three kinds of empowerment through Twitter in the religiously conservative and socially patriarchal Saudi society. It traces and analyses how Saudi female hashtactivism is increasingly becoming a site of struggle over visibility, mobility, control, and civic participation. The underlying thesis is that Twitter makes a contribution to the development of participatory culture, especially in the lives of women. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=civic" title="civic">civic</a>, <a href="https://publications.waset.org/abstracts/search?q=hashtactivism" title=" hashtactivism"> hashtactivism</a>, <a href="https://publications.waset.org/abstracts/search?q=Saudi%20Arabia" title=" Saudi Arabia"> Saudi Arabia</a>, <a href="https://publications.waset.org/abstracts/search?q=Twiterverse" title=" Twiterverse"> Twiterverse</a> </p> <a href="https://publications.waset.org/abstracts/68444/women-hashtactivism-civic-engagement-in-saudi-arabia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68444.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">323</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28227</span> The Problem of Child Exploitation on Twitter: A Socio-Anthropological Perspective on Content Filtering Gaps</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samig%20Ibayev">Samig Ibayev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research addresses the problem of illegal child abuse content on the Twitter platform bypassing filtering systems and appearing before users from a social-anthropological perspective. Although the wide access opportunities provided by social media platforms to their users are beneficial in many ways, it is seen that they contain gaps that pave the way for the spread of harmful and illegal content. The aim of the study is to examine the inadequacies of the current content filtering mechanisms of the Twitter platform, to understand the psychological effects of young users unintentionally encountering such content and the social dimensions of this situation. The research was conducted with a qualitative approach and was conducted using digital ethnography, content analysis and user experiences on the Twitter platform. Digital ethnography was used to observe the frequency of child abuse content on the platform and how these contents were presented. The content analysis method was used to reveal the gaps in Twitter's current filtering mechanisms. In addition, detailed information was collected on the extent of psychological effects and how the perception of trust in social media changed through interviews with young users exposed to such content. The main contributions of the research are to highlight the weaknesses in the content moderation and filtering mechanisms of social media platforms, to reveal the negative effects of illegal content on users, and to offer suggestions for preventing the spread of such content. As a result, it is suggested that platforms such as Twitter should improve their content filtering policies in order to increase user security and fulfill their social responsibilities. This research aims to create significant awareness about social media content management and ethical responsibilities on digital platforms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Twitter" title="Twitter">Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=child%20exploitation" title=" child exploitation"> child exploitation</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20filtering" title=" content filtering"> content filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20ethnography" title=" digital ethnography"> digital ethnography</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20anthropology" title=" social anthropology"> social anthropology</a> </p> <a href="https://publications.waset.org/abstracts/194155/the-problem-of-child-exploitation-on-twitter-a-socio-anthropological-perspective-on-content-filtering-gaps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194155.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">9</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28226</span> The Polarization on Twitter and COVID-19 Vaccination in Brazil</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giselda%20Cristina%20Ferreira">Giselda Cristina Ferreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Alberto%20Kamienski"> Carlos Alberto Kamienski</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20L%C3%ADgia%20Scott"> Ana Lígia Scott</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The COVID-19 pandemic has enhanced the anti-vaccination movement in Brazil, supported by unscientific theories and false news and the possibility of wide communication through social networks such as Twitter, Facebook, and YouTube. The World Health Organization (WHO) classified the large volume of information on the subject against COVID-19 as an Infodemic. In this paper, we present a protocol to identify polarizing users (called polarizers) and study the profiles of Brazilian polarizers on Twitter (renamed to X some weeks ago). We analyzed polarizing interactions on Twitter (in Portuguese) to identify the main polarizers and how the conflicts they caused influenced the COVID-19 vaccination rate throughout the pandemic. This protocol uses data from this social network, graph theory, Java, and R-studio scripts to model and analyze the data. The information about the vaccination rate was obtained in a public database for the government called OpenDataSus. The results present the profiles of Twitter’s Polarizer (political position, gender, professional activity, immunization opinions). We observed that social and political events influenced the participation of these different profiles in conflicts and the vaccination rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Twitter" title="Twitter">Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=polarization" title=" polarization"> polarization</a>, <a href="https://publications.waset.org/abstracts/search?q=vaccine" title=" vaccine"> vaccine</a>, <a href="https://publications.waset.org/abstracts/search?q=Brazil" title=" Brazil"> Brazil</a> </p> <a href="https://publications.waset.org/abstracts/171793/the-polarization-on-twitter-and-covid-19-vaccination-in-brazil" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171793.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28225</span> Quantitative Research on the Effects of Following Brands on Twitter on Consumer Brand Attitude</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yujie%20Wei">Yujie Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter uses a variety of narrative methods (e.g., messages, featured videos, music, and actual events) to strengthen its cultivation effect. Consumers are receiving mass-produced brand stores or images made by brand managers according to strict market specifications. Drawing on the cultivation theory, this quantitative research investigates how following a brand on Twitter for 12 weeks can cultivate their attitude toward the brand and influence their purchase intentions. We conducted three field experiments on Twitter to test the cultivation effects of following a brand for 12 weeks on consumer attitude toward the followed brand. The cultivation effects were measured by comparing the changes in consumer attitudes before and after they have followed a brand over time. The findings of our experiments suggest that when consumers are exposed to a brand’s stable, pervasive, and recurrent tweets on Twitter for 12 weeks, their attitude toward a brand can be significantly changed, which confirms the cultivating effects on consumer attitude. Also, the results indicate that branding activities on Twitter, when properly implemented, can be very effective in changing consumer attitudes toward a brand, increasing the purchase intentions, and increasing their willingness to spread the word-of-mouth for the brand on social media. The cultivation effects are moderated by brand type and consumer age. The research provides three major marketing implications. First, Twitter marketers should create unique content to engage their brand followers to change their brand attitude through steady, cumulative exposure to the branding activities on Twitter. Second, there is a significant moderating effect of brand type on the cultivation effects, so Twitter marketers should align their branding content with the brand type to better meet the needs and wants of consumers for different types of brands. Finally, Twitter marketers should adapt their tweeting strategies according to the media consumption preferences of different age groups of their target markets. This empirical research proves that content is king. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tweeting" title="tweeting">tweeting</a>, <a href="https://publications.waset.org/abstracts/search?q=cultivation%20theory" title=" cultivation theory"> cultivation theory</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20brand%20attitude" title=" consumer brand attitude"> consumer brand attitude</a>, <a href="https://publications.waset.org/abstracts/search?q=purchase%20intentions" title=" purchase intentions"> purchase intentions</a>, <a href="https://publications.waset.org/abstracts/search?q=word-of-mouth" title=" word-of-mouth"> word-of-mouth</a> </p> <a href="https://publications.waset.org/abstracts/118469/quantitative-research-on-the-effects-of-following-brands-on-twitter-on-consumer-brand-attitude" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">109</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28224</span> Using an Epidemiological Model to Study the Spread of Misinformation during the Black Lives Matter Movement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Maleki">Maryam Maleki</a>, <a href="https://publications.waset.org/abstracts/search?q=Esther%20Mead"> Esther Mead</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Arani"> Mohammad Arani</a>, <a href="https://publications.waset.org/abstracts/search?q=Nitin%20Agarwal"> Nitin Agarwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The proliferation of social media platforms like Twitter has heightened the consequences of the spread of misinformation. To understand and model the spread of misinformation, in this paper, we leveraged the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological model to describe the underlying process that delineates the spread of misinformation on Twitter. Compared to the other epidemiological models, this model produces broader results because it includes the additional Skeptics (Z) compartment, wherein a user may be Exposed to an item of misinformation but not engage in any reaction to it, and the additional Exposed (E) compartment, wherein the user may need some time before deciding to spread a misinformation item. We analyzed misinformation regarding the unrest in Washington, D.C. in the month of March 2020, which was propagated by the use of the #DCblackout hashtag by different users across the U.S. on Twitter. Our analysis shows that misinformation can be modeled using the concept of epidemiology. To the best of our knowledge, this research is the first to attempt to apply the SEIZ epidemiological model to the spread of a specific item of misinformation, which is a category distinct from that of rumor and hoax on online social media platforms. Applying a mathematical model can help to understand the trends and dynamics of the spread of misinformation on Twitter and ultimately help to develop techniques to quickly identify and control it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Black%20Lives%20Matter" title="Black Lives Matter">Black Lives Matter</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemiological%20model" title=" epidemiological model"> epidemiological model</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20modeling" title=" mathematical modeling"> mathematical modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=misinformation" title=" misinformation"> misinformation</a>, <a href="https://publications.waset.org/abstracts/search?q=SEIZ%20model" title=" SEIZ model"> SEIZ model</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a> </p> <a href="https://publications.waset.org/abstracts/132209/using-an-epidemiological-model-to-study-the-spread-of-misinformation-during-the-black-lives-matter-movement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132209.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">166</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=twitter%20accounts%20analysis&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=twitter%20accounts%20analysis&page=3">3</a></li> <li class="page-item"><a class="page-link" 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