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trends</title> <meta name="description" content="Search results for: twitter trends"> <meta name="keywords" content="twitter trends"> <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 class="d-block d-lg-none navbar-toggler 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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 trends"> <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> 1746</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: twitter trends</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1746</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">259</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">1745</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">1744</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">1743</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">414</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">1742</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">1741</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">1740</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">1739</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">167</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">1738</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">1737</span> A Comparative Evaluation of the SIR and SEIZ Epidemiological Models to Describe the Diffusion Characteristics of COVID-19 Polarizing Viewpoints on Online</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> This study is conducted to examine how opposing viewpoints related to COVID-19 were diffused on Twitter. To accomplish this, six datasets using two epidemiological models, SIR (Susceptible, Infected, Recovered) and SEIZ (Susceptible, Exposed, Infected, Skeptics), were analyzed. The six datasets were chosen because they represent opposing viewpoints on the COVID-19 pandemic. Three of the datasets contain anti-subject hashtags, while the other three contain pro-subject hashtags. The time frame for all datasets is three years, starting from January 2020 to December 2022. The findings revealed that while both models were effective in evaluating the propagation trends of these polarizing viewpoints, the SEIZ model was more accurate with a relatively lower error rate (6.7%) compared to the SIR model (17.3%). Additionally, the relative error for both models was lower for anti-subject hashtags compared to pro-subject hashtags. By leveraging epidemiological models, insights into the propagation trends of polarizing viewpoints on Twitter were gained. This study paves the way for the development of methods to prevent the spread of ideas that lack scientific evidence while promoting the dissemination of scientifically backed ideas. <p class="card-text"><strong>Keywords:</strong> <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=epidemiological%20model" title=" epidemiological model"> epidemiological model</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=sir%20model" title=" sir model"> sir model</a>, <a href="https://publications.waset.org/abstracts/search?q=covid-19" title=" covid-19"> covid-19</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=social%20contagion" title=" social contagion"> social contagion</a> </p> <a href="https://publications.waset.org/abstracts/177941/a-comparative-evaluation-of-the-sir-and-seiz-epidemiological-models-to-describe-the-diffusion-characteristics-of-covid-19-polarizing-viewpoints-on-online" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177941.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">62</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">1736</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">1735</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">474</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">1734</span> Historical Hashtags: An Investigation of the #CometLanding Tweets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noor%20Farizah%20Ibrahim">Noor Farizah Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Durugbo"> Christopher Durugbo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to investigate how the Twittersphere reacted during the recent historical event of robotic landing on a comet. The news is about Philae, a robotic lander from European Space Agency (ESA), which successfully made the first-ever rendezvous and touchdown of its kind on a nucleus comet on November 12, 2014. In order to understand how Twitter is practically used in spreading messages on historical events, we conducted an analysis of one-week tweet feeds that contain the #CometLanding hashtag. We studied the trends of tweets, the diffusion of the information and the characteristics of the social network created. The results indicated that the use of Twitter as a platform enables online communities to engage and spread the historical event through social media network (e.g. tweets, retweets, mentions and replies). In addition, it was found that comprehensible and understandable hashtags could influence users to follow the same tweet stream compared to other laborious hashtags which were difficult to understand by users in online communities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diffusion%20of%20information" title="diffusion of information">diffusion of information</a>, <a href="https://publications.waset.org/abstracts/search?q=hashtag" title=" hashtag"> hashtag</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=Twitter" title=" Twitter"> Twitter</a> </p> <a href="https://publications.waset.org/abstracts/50619/historical-hashtags-an-investigation-of-the-cometlanding-tweets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50619.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">325</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">1733</span> Resale Housing Development Board Price Prediction Considering Covid-19 through Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinaath%20Anbu%20Durai">Srinaath Anbu Durai</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Zhaoxia"> Wang Zhaoxia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data. <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=Covid-19" title=" Covid-19"> Covid-19</a>, <a href="https://publications.waset.org/abstracts/search?q=housing%20price%20prediction" title=" housing price prediction"> housing price prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=tweets" title=" tweets"> tweets</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=Singapore%20HDB" title=" Singapore HDB"> Singapore HDB</a>, <a href="https://publications.waset.org/abstracts/search?q=behavioral%20economics" title=" behavioral economics"> behavioral economics</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/158988/resale-housing-development-board-price-prediction-considering-covid-19-through-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158988.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">116</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">1732</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">1731</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">1730</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">1729</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">1728</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">1727</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">1726</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">1725</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">1724</span> Social Media and Political Expression: Examining Affordances and Spiral of Silence Theories</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Oz">Mustafa Oz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study compares how do people express their opinions on the Facebook versus on Twitter. It was sought to understand whether people were more willing to express their opinions on some social media channels than others. It was assumed that fear of isolation and affordances may influence users’ opinion expression behaviors on social media websites. Thus two most popular social media websites, Twitter and Facebook, were compared. This study aims to provide the comprehensive understanding of political expression on social media platforms. An online survey (N=535) was conducted to understand respondents’ opinion expression behaviors. Overall, the results suggested that people were more likely to express their opinion on Twitter than Facebook when they think the majority does not support their opinion. The study concluded that people operate differently on Facebook versus 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=spiral%20of%20silence" title=" spiral of silence"> spiral of silence</a>, <a href="https://publications.waset.org/abstracts/search?q=affordances" title=" affordances"> affordances</a>, <a href="https://publications.waset.org/abstracts/search?q=political%20expression" title=" political expression"> political expression</a> </p> <a href="https://publications.waset.org/abstracts/94455/social-media-and-political-expression-examining-affordances-and-spiral-of-silence-theories" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94455.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">1723</span> Investigating the Factors Leading to Utilization of Facebook and Twitter/X Sites by Youths at Elections Evening in Nigeria: A Case Study of 2023 General Elections</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullahi%20Garba%20Abu">Abdullahi Garba Abu</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Bello%20Sada"> Muhammad Bello Sada</a>, <a href="https://publications.waset.org/abstracts/search?q=Aminu%20Abubakar"> Aminu Abubakar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Facebook and Twitter/X platforms are preferred and largely patronized by Youths in Nigeria. The simplicity and popularity of Facebook and Twitter/X have made them preferred social networking sites for Youths to handle or execute different political activities in favor of their chosen candidates or political parties. This is largely related to their interest in using the platform for the purposes of participation in 2023 political activities and general elections. The two Social Networking Sites were used to vigorously pursue party activities on the eve of the 2023 general elections. Youths engaged the two platforms in campaigning for their candidates and political parties and succeeded in reaching a wide audience, shared the policies and manifestos of their parties, engaged with supporters and even posted advertising campaigns for specific demographics. However, the utilization of Facebook and Twitter /X platforms during the 2023 elections was largely seen in two lights: positive and negative lights/intentions. Therefore, this research investigates the motivating factors for which largely Nigerian Youths engage Facebook and Twitter platforms in political activities, with reference to the 2023 general elections. The research uses a survey method through which it reaches out to respondents from all six geo-politial zones. The research found that Nigerian Youths utilize the two social media sites to campaign for politicians voluntarily based on their belief in the capabilities of the candidates. It also found out that Youths were lured into using Facebook and Twitter/X sites to campaign through tribal, religious, and ethnic factors. More so, the research found out that eagerness to share political materials in support of candidates made Youths in Nigeria share unverifiable content on Facebook and Twitter sites. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Facebook" title="Facebook">Facebook</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter%2FX" title=" Twitter/X"> Twitter/X</a>, <a href="https://publications.waset.org/abstracts/search?q=Nigerian%20youths" title=" Nigerian youths"> Nigerian youths</a>, <a href="https://publications.waset.org/abstracts/search?q=2023%20elections" title=" 2023 elections"> 2023 elections</a> </p> <a href="https://publications.waset.org/abstracts/184216/investigating-the-factors-leading-to-utilization-of-facebook-and-twitterx-sites-by-youths-at-elections-evening-in-nigeria-a-case-study-of-2023-general-elections" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184216.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">59</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">1722</span> A Polyphonic Look at Trends</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Turquesa%20Topper">Turquesa Topper</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The reflection focuses on recording and explaining the considerations, conceptualizations and methodological approach with which from the University, that is to say, from the academic field, the study of Trends is addressed with the intention of training professionals in the area, an area that requires disciplinary boundaries and builds a polyphonic vision. When referring to the objective of our Laboratory the detection of aesthetic trends of consumption, we find ourselves in the requirement to define our object: trends, aesthetic trends of consumption, more specifically. The pages cover a conception of trends from a theoretical framework that incorporates contributions from linguistics, semiotics, sociology, cultural studies and project disciplines, in order to consolidate a polyphonic look. The text investigates in the pre-discursive aspect of the trends, in the circulation of the notion of style and in the dynamics of affirmation - denial as the constitutive dynamics of Fashion linked to any process of innovation. From such inquiry, it is presented to Fashion as a system that operates directly on the construction of socio-individual identities unfolding through the liquefaction of signs in trends. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fashion" title="fashion">fashion</a>, <a href="https://publications.waset.org/abstracts/search?q=methodology" title=" methodology"> methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=narrative" title=" narrative"> narrative</a>, <a href="https://publications.waset.org/abstracts/search?q=trends" title=" trends"> trends</a> </p> <a href="https://publications.waset.org/abstracts/72616/a-polyphonic-look-at-trends" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72616.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">250</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">1721</span> Tracing Digital Traces of Phatic Communion in #Mooc</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Judith%20Enriquez-Gibson">Judith Enriquez-Gibson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper meddles with the notion of phatic communion introduced 90 years ago by Malinowski, who was a Polish-born British anthropologist. It explores the phatic in Twitter within the contents of tweets related to moocs (massive online open courses) as a topic or trend. It is not about moocs though. It is about practices that could easily be hidden or neglected if we let big or massive topics take the lead or if we simply follow the computational or secret codes behind Twitter itself and third party software analytics. It draws from media and cultural studies. Though at first it appears data-driven as I submitted data collection and analytics into the hands of a third party software, Twitonomy, the aim is to follow how phatic communion might be practised in a social media site, such as Twitter. Lurking becomes its research method to analyse mooc-related tweets. A total of 3,000 tweets were collected on 11 October 2013 (UK timezone). The emphasis of lurking is to engage with Twitter as a system of connectivity. One interesting finding is that a click is in fact a phatic practice. A click breaks the silence. A click in one of the mooc website is actually a tweet. A tweet was posted on behalf of a user who simply chose to click without formulating the text and perhaps without knowing that it contains #mooc. Surely, this mechanism is not about reciprocity. To break the silence, users did not use words. They just clicked the ‘tweet button’ on a mooc website. A click performs and maintains connectivity – and Twitter as the medium in attendance in our everyday, available when needed to be of service. In conclusion, the phatic culture of breaking silence in Twitter does not have to submit to the power of code and analytics. It is a matter of human code. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=click" title="click">click</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=phatic%20communion" title=" phatic communion"> phatic communion</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media%20data" title=" social media data"> social media data</a>, <a href="https://publications.waset.org/abstracts/search?q=mooc" title=" mooc"> mooc</a> </p> <a href="https://publications.waset.org/abstracts/18814/tracing-digital-traces-of-phatic-communion-in-mooc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18814.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">1720</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">1719</span> Investigating Non-suicidal Self-Injury Discussions on Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Abubakar%20Alhassan">Muhammad Abubakar Alhassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Diane%20Pennington"> Diane Pennington</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social networking sites have become a space for people to discuss public health issues such as non-suicidal self-injury (NSSI). There are thousands of tweets containing self-harm and self-injury hashtags on Twitter. It is difficult to distinguish between different users who participate in self-injury discussions on Twitter and how their opinions change over time. Also, it is challenging to understand the topics surrounding NSSI discussions on Twitter. We retrieved tweets using #selfham and #selfinjury hashtags and investigated those from the United kingdom. We applied inductive coding and grouped tweeters into different categories. This study used the Latent Dirichlet Allocation (LDA) algorithm to infer the optimum number of topics that describes our corpus. Our findings revealed that many of those participating in NSSI discussions are non-professional users as opposed to medical experts and academics. Support organisations, medical teams, and academics were campaigning positively on rais-ing self-injury awareness and recovery. Using LDAvis visualisation technique, we selected the top 20 most relevant terms from each topic and interpreted the topics as; children and youth well-being, self-harm misjudgement, mental health awareness, school and mental health support and, suicide and mental-health issues. More than 50% of these topics were discussed in England compared to Scotland, Wales, Ireland and Northern Ireland. Our findings highlight the advantages of using the Twitter social network in tackling the problem of self-injury through awareness. There is a need to study the potential risks associated with the use of social networks among self-injurers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-harm" title="self-harm">self-harm</a>, <a href="https://publications.waset.org/abstracts/search?q=non-suicidal%20self-injury" title=" non-suicidal self-injury"> non-suicidal self-injury</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%20networks" title=" social networks"> social networks</a> </p> <a href="https://publications.waset.org/abstracts/135147/investigating-non-suicidal-self-injury-discussions-on-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135147.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">132</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">1718</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">10</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">1717</span> The Experiences of Agency in the Utilization of Twitter for English Language Learning in a Saudi EFL Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahd%20Hamad%20Alqasham">Fahd Hamad Alqasham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This longitudinal study investigates Saudi students’ use trajectory and experiences of Twitter as an innovative tool for in-class learning of the English language in a Saudi tertiary English as a foreign language (EFL) context for a 12-week semester. The study adopted van Lier’s agency theory (2008, 2010) as the analytical framework to obtain an in-depth analysis of how the learners’ could utilize Twitter to create innovative ways for them to engage in English learning inside the language classroom. The study implemented a mixed methods approach, including six data collection instruments consisting of a research log, observations, focus group participation, initial and post-project interviews, and a post-project questionnaire. The study was conducted at Qassim University, specifically at Preparatory Year Program (PYP) on the main campus. The sample included 25 male students studying in the first level of PYP. The findings results revealed that although Twitter’s affordances initially paled a crucial role in motivating the learners to initiate their agency inside the classroom to learn English, the contextual constraints, mainly anxiety, the university infrastructure, and the teacher’s role negatively influenced the sustainability of Twitter’s use past week nine of its implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CALL" title="CALL">CALL</a>, <a href="https://publications.waset.org/abstracts/search?q=agency" title=" agency"> agency</a>, <a href="https://publications.waset.org/abstracts/search?q=innovation" title=" innovation"> innovation</a>, <a href="https://publications.waset.org/abstracts/search?q=EFL" title=" EFL"> EFL</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20learning" title=" language learning"> language learning</a> </p> <a href="https://publications.waset.org/abstracts/156340/the-experiences-of-agency-in-the-utilization-of-twitter-for-english-language-learning-in-a-saudi-efl-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156340.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">72</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%20trends&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=twitter%20trends&page=3">3</a></li> <li class="page-item"><a class="page-link" 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