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tweets</title> <meta name="description" content="Search results for: tweets"> <meta name="keywords" content="tweets"> <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 ml-auto" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="tweets"> <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> 73</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: tweets</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13</span> Exploiting Identity Grievances: Al-Shabaab Propaganda Targeting Individuals Abroad</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Mabruk">Mustafa Mabruk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Groups such as Al-Shabaab have managed to radicalize many individuals abroad, including the first American citizen to ever be radicalized. Yet the pathways of radicalization for these foreign individuals are understudied. Moreover, current measures to prevent foreign radicalization are ineffective, with privacy, screening and profiling implications that render current counter-radicalization efforts counterproductive. Such measures exhibit strictness, political bias, and harshness. As confirmed by recent studies, such counter-radicalization issues exacerbate existing grievances and channel fresh recruits to Al-Shabaab. Addressing these challenges is paramount, requiring alternative strategies to effectively reduce radicalization without triggering further harm. The development of counter-narratives emerges as a potential measure with minimal risk of exacerbating grievances, yet the development of such counter-narratives necessitates a thorough understanding of the radicalization pathways of foreign individuals that are understudied. This study investigates the success of Al-Shabaab in recruiting individuals abroad by analyzing their propaganda in conjunction with analyzing identity-focused theories of radicalization, including Framing Theory and Social Identity Theory. Qualitative content analysis is used to analyze various propaganda material, including tweets, speeches, and webpages. The analysis reveals that issues of identity are of major significance in the radicalization patterns identified and that grievances of Muslims worldwide are used to exploit identity-related grievances. Based on these findings, the paper argues that such evidence enhances our understanding of potential deradicalization pathways and present counter-narratives based on Islamic scripture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=counter-narratives" title="counter-narratives">counter-narratives</a>, <a href="https://publications.waset.org/abstracts/search?q=foreign%20radicalization" title=" foreign radicalization"> foreign radicalization</a>, <a href="https://publications.waset.org/abstracts/search?q=identity%20grievances" title=" identity grievances"> identity grievances</a>, <a href="https://publications.waset.org/abstracts/search?q=propaganda%20analysis" title=" propaganda analysis"> propaganda analysis</a> </p> <a href="https://publications.waset.org/abstracts/185805/exploiting-identity-grievances-al-shabaab-propaganda-targeting-individuals-abroad" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185805.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">43</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">12</span> Emotion Detection in Twitter Messages Using Combination of Long Short-Term Memory and Convolutional Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bahareh%20Golchin">Bahareh Golchin</a>, <a href="https://publications.waset.org/abstracts/search?q=Nooshin%20Riahi"> Nooshin Riahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20classification" title="emotion classification">emotion classification</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=social%20networks" title=" social networks"> social networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title=" deep neural networks"> deep neural networks</a> </p> <a href="https://publications.waset.org/abstracts/137621/emotion-detection-in-twitter-messages-using-combination-of-long-short-term-memory-and-convolutional-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137621.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">11</span> A Study of Inter-Media Discourse Construction on Sino-US Trade Friction Based on Network Agenda Setting Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanying%20Xie">Wanying Xie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Under the background of the increasing Sino-US trade friction, the two nations pay more attention to the medias’ words. This paper mainly studies the causality, effectiveness, and influence of discourse construction between traditional media and social media. Based on the Network Agenda Setting theory, a kind of associative memory pattern in Psychology, who focuses on how media affect audiences’ cognition of issues and attributes, as well as the significance of the relation between people and matters. The date of the sample chosen in this paper ranges from March 23, 2018, to April 30, 2019. A total of 395 Tweets of Donald Trump are obtained, and 731 related reports are collected from the mainstream American newspapers including New York Times, the Washington Post and the Washington Street, by using Factiva and other databases. The sample data are processed by MAXQDA while the media discourses are analyzed by SPSS and Cite Space, with an aim to study: 1) whether the inter-media discourse construction exists; 2) which media (traditional media V.S. social media) is dominant; 3) the causality between two media. The results show: 1) the discourse construction between three American mainstream newspapers and Donald Trump's Twitter is proved in some periods; 2) the dominant position is extremely depended on the events; 3) the causality between two media is decided by many reasons. New media technology shortens the time of agenda-setting effect to one day or less. By comparing the specific relation between the three major American newspapers and Donald Trump’s Twitter, whose popularity and influence could be reflected. Hopefully, this paper could enable readers to have a more comprehensive understanding of the international media language and political environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discourse%20construction" title="discourse construction">discourse construction</a>, <a href="https://publications.waset.org/abstracts/search?q=media%20language" title=" media language"> media language</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20agenda-setting%20theory" title=" network agenda-setting theory"> network agenda-setting theory</a>, <a href="https://publications.waset.org/abstracts/search?q=sino-us%20trade%20friction" title=" sino-us trade friction"> sino-us trade friction</a> </p> <a href="https://publications.waset.org/abstracts/107046/a-study-of-inter-media-discourse-construction-on-sino-us-trade-friction-based-on-network-agenda-setting-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107046.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">257</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">10</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">9</span> Detecting Hate Speech And Cyberbullying Using Natural Language Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N%C3%A1dia%20Pereira">Nádia Pereira</a>, <a href="https://publications.waset.org/abstracts/search?q=Paula%20Ferreira"> Paula Ferreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Francisco"> Sofia Francisco</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Oliveira"> Sofia Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidclay%20Souza"> Sidclay Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Paula%20Paulino"> Paula Paulino</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Margarida%20Veiga%20Sim%C3%A3o"> Ana Margarida Veiga Simão</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social media has progressed into a platform for hate speech among its users, and thus, there is an increasing need to develop automatic detection classifiers of offense and conflicts to help decrease the prevalence of such incidents. Online communication can be used to intentionally harm someone, which is why such classifiers could be essential in social networks. A possible application of these classifiers is the automatic detection of cyberbullying. Even though identifying the aggressive language used in online interactions could be important to build cyberbullying datasets, there are other criteria that must be considered. Being able to capture the language, which is indicative of the intent to harm others in a specific context of online interaction is fundamental. Offense and hate speech may be the foundation of online conflicts, which have become commonly used in social media and are an emergent research focus in machine learning and natural language processing. This study presents two Portuguese language offense-related datasets which serve as examples for future research and extend the study of the topic. The first is similar to other offense detection related datasets and is entitled Aggressiveness dataset. The second is a novelty because of the use of the history of the interaction between users and is entitled the Conflicts/Attacks dataset. Both datasets were developed in different phases. Firstly, we performed a content analysis of verbal aggression witnessed by adolescents in situations of cyberbullying. Secondly, we computed frequency analyses from the previous phase to gather lexical and linguistic cues used to identify potentially aggressive conflicts and attacks which were posted on Twitter. Thirdly, thorough annotation of real tweets was performed byindependent postgraduate educational psychologists with experience in cyberbullying research. Lastly, we benchmarked these datasets with other machine learning classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aggression" title="aggression">aggression</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=cyberbullying" title=" cyberbullying"> cyberbullying</a>, <a href="https://publications.waset.org/abstracts/search?q=datasets" title=" datasets"> datasets</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title=" hate speech"> hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/142382/detecting-hate-speech-and-cyberbullying-using-natural-language-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142382.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">228</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">8</span> Arabic Lexicon Learning to Analyze Sentiment in Microblogs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20B.%20Rokaya">Mahmoud B. Rokaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of opinion mining and sentiment analysis includes analysis of opinions, sentiments, evaluations, attitudes, and emotions. The rapid growth of social media, social networks, reviews, forum discussions, microblogs, and Twitter, leads to a parallel growth in the field of sentiment analysis. The field of sentiment analysis tries to develop effective tools to make it possible to capture the trends of people. There are two approaches in the field, lexicon-based and corpus-based methods. A lexicon-based method uses a sentiment lexicon which includes sentiment words and phrases with assigned numeric scores. These scores reveal if sentiment phrases are positive or negative, their intensity, and/or their emotional orientations. Creation of manual lexicons is hard. This brings the need for adaptive automated methods for generating a lexicon. The proposed method generates dynamic lexicons based on the corpus and then classifies text using these lexicons. In the proposed method, different approaches are combined to generate lexicons from text. The proposed method classifies the tweets into 5 classes instead of +ve or –ve classes. The sentiment classification problem is written as an optimization problem, finding optimum sentiment lexicons are the goal of the optimization process. The solution was produced based on mathematical programming approaches to find the best lexicon to classify texts. A genetic algorithm was written to find the optimal lexicon. Then, extraction of a meta-level feature was done based on the optimal lexicon. The experiments were conducted on several datasets. Results, in terms of accuracy, recall and F measure, outperformed the state-of-the-art methods proposed in the literature in some of the datasets. A better understanding of the Arabic language and culture of Arab Twitter users and sentiment orientation of words in different contexts can be achieved based on the sentiment lexicons proposed by the algorithm. <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%20sentiment" title=" Twitter sentiment"> Twitter sentiment</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=lexicon" title=" lexicon"> lexicon</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20computation" title=" evolutionary computation"> evolutionary computation</a> </p> <a href="https://publications.waset.org/abstracts/99566/arabic-lexicon-learning-to-analyze-sentiment-in-microblogs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99566.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">189</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">7</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">6</span> Informational Habits and Ideology as Predictors for Political Efficacy: A Survey Study of the Brazilian Political Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pedro%20Cardoso%20Alves">Pedro Cardoso Alves</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Lucia%20Galinkin"> Ana Lucia Galinkin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Carlos%20Ribeiro"> José Carlos Ribeiro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Political participation, can be a somewhat tricky subject to define, not in small part due to the constant changes in the concept fruit of the effort to include new forms of participatory behavior that go beyond traditional institutional channels. With the advent of the internet and mobile technologies, defining political participation has become an even more complicated endeavor, given de amplitude of politicized behaviors that are expressed throughout these mediums, be it in the very organization of social movements, in the propagation of politicized texts, videos and images, or in the micropolitical behaviors that are expressed in daily interaction. In fact, the very frontiers that delimit physical and digital spaces have become ever more diluted due to technological advancements, leading to a hybrid existence that is simultaneously physical and digital, not limited, as it once was, to the temporal limitations of classic communications. Moving away from those institutionalized actions of traditional political behavior, an idea of constant and fluid participation, which occurs in our daily lives through conversations, posts, tweets and other digital forms of expression, is discussed. This discussion focuses on the factors that precede more direct forms of political participation, interpreting the relation between informational habits, ideology, and political efficacy. Though some of the informational habits can be considered political participation, by some authors, a distinction is made to establish a logical flow of behaviors leading to participation, that is, one must gather and process information before acting on it. To reach this objective, a quantitative survey is currently being applied in Brazilian social media, evaluating feelings of political efficacy, social and economic issue-based ideological stances and informational habits pertaining to collection, fact-checking, and diversity of sources and ideological positions present in the participant’s political information network. The measure being used for informational habits relies strongly on a mix of information literacy and political sophistication concepts, bringing a more up-to-date understanding of information and knowledge production and processing in contemporary hybrid (physical-digital) environments. Though data is still being collected, preliminary analysis point towards a strong correlation between information habits and political efficacy, while ideology shows a weaker influence over efficacy. Moreover, social ideology and economic ideology seem to have a strong correlation in the sample, such intermingling between social and economic ideals is generally considered a red flag for political polarization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=political%20efficacy" title="political efficacy">political efficacy</a>, <a href="https://publications.waset.org/abstracts/search?q=ideology" title=" ideology"> ideology</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20literacy" title=" information literacy"> information literacy</a>, <a href="https://publications.waset.org/abstracts/search?q=cyberpolitics" title=" cyberpolitics"> cyberpolitics</a> </p> <a href="https://publications.waset.org/abstracts/77079/informational-habits-and-ideology-as-predictors-for-political-efficacy-a-survey-study-of-the-brazilian-political-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77079.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">234</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">5</span> The Social Aspects of Code-Switching in Online Interaction: The Case of Saudi Bilinguals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shirin%20Alabdulqader">Shirin Alabdulqader</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to investigate the concept of code-switching (CS) between English, Arabic, and the CS practices of Saudi online users via a Translanguaging (TL) lens for more inclusive view towards the nature of the data from the study. It employs Digitally Mediated Communication (DMC), specifically the WhatsApp and Twitter platforms, in order to understand how the users employ online resources to communicate with others on a daily basis. This project looks beyond language and considers the multimodal affordances (visual and audio means) that interlocutors utilise in their online communicative practices to shape their online social existence. This exploratory study is based on a data-driven interpretivist epistemology as it aims to understand how meaning (reality) is created by individuals within different contexts. This project used a mixed-method approach, combining a qualitative and a quantitative approach. In the former, data were collected from online chats and interview responses, while in the latter a questionnaire was employed to understand the frequency and relations between the participants’ linguistic and non-linguistic practices and their social behaviours. The participants were eight bilingual Saudi nationals (both men and women, aged between 20 and 50 years old) who interacted with others online. These participants provided their online interactions, participated in an interview and responded to a questionnaire. The study data were gathered from 194 WhatsApp chats and 122 Tweets. These data were analysed and interpreted according to three levels: conversational turn taking and CS; the linguistic description of the data; and CS and persona. This project contributes to the emerging field of analysing online Arabic data systematically, and the field of multimodality and bilingual sociolinguistics. The findings are reported for each of the three levels. For conversational turn taking, the CS analysis revealed that it was used to accomplish negotiation and develop meaning in the conversation. With regard to the linguistic practices of the CS data, the majority of the code-switched words were content morphemes. The third level of data interpretation is CS and its relationship with identity; two types of identity were indexed; absolute identity and contextual identity. This study contributes to the DMC literature and bridges some of the existing gaps. The findings of this study are that CS by its nature, and most of the findings, if not all, support the notion of TL that multiliteracy is one’s ability to decode multimodal communication, and that this multimodality contributes to the meaning. Either this is applicable to the online affordances used by monolinguals or multilinguals and perceived not only by specific generations but also by any online multiliterates, the study provides the linguistic features of CS utilised by Saudi bilinguals and it determines the relationship between these features and the contexts in which they appear. <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=code-switching" title=" code-switching"> code-switching</a>, <a href="https://publications.waset.org/abstracts/search?q=translanguaging" title=" translanguaging"> translanguaging</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20interaction" title=" online interaction"> online interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=saudi%20bilinguals" title=" saudi bilinguals"> saudi bilinguals</a> </p> <a href="https://publications.waset.org/abstracts/157842/the-social-aspects-of-code-switching-in-online-interaction-the-case-of-saudi-bilinguals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157842.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">131</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">4</span> Navigating States of Emergency: A Preliminary Comparison of Online Public Reaction to COVID-19 and Monkeypox on Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Antonia%20Egli">Antonia Egli</a>, <a href="https://publications.waset.org/abstracts/search?q=Theo%20Lynn"> Theo Lynn</a>, <a href="https://publications.waset.org/abstracts/search?q=Pierangelo%20Rosati"> Pierangelo Rosati</a>, <a href="https://publications.waset.org/abstracts/search?q=Gary%20Sinclair"> Gary Sinclair</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The World Health Organization (WHO) defines vaccine hesitancy as the postponement or complete denial of vaccines and estimates a direct linkage to approximately 1.5 million avoidable deaths annually. This figure is not immune to public health developments, as has become evident since the global spread of COVID-19 from Wuhan, China in early 2020. Since then, the proliferation of influential, but oftentimes inaccurate, outdated, incomplete, or false vaccine-related information on social media has impacted hesitancy levels to a degree described by the WHO as an infodemic. The COVID-19 pandemic and related vaccine hesitancy levels have in 2022 resulted in the largest drop in childhood vaccinations of the 21st century, while the prevalence of online stigma towards vaccine hesitant consumers continues to grow. Simultaneously, a second disease has risen to global importance: Monkeypox is an infection originating from west and central Africa and, due to racially motivated online hate, was in August 2022 set to be renamed by the WHO. To better understand public reactions towards two viral infections that became global threats to public health no two years apart, this research examines user replies to threads published by the WHO on Twitter. Replies to two Tweets from the @WHO account declaring COVID-19 and Monkeypox as ‘public health emergencies of international concern’ on January 30, 2020, and July 23, 2022, are gathered using the Twitter application programming interface and user mention timeline endpoint. Research methodology is unique in its analysis of stigmatizing, racist, and hateful content shared on social media within the vaccine discourse over the course of two disease outbreaks. Three distinct analyses are conducted to provide insight into (i) the most prevalent topics and sub-topics among user reactions, (ii) changes in sentiment towards the spread of the two diseases, and (iii) the presence of stigma, racism, and online hate. Findings indicate an increase in hesitancy to accept further vaccines and social distancing measures, the presence of stigmatizing content aimed primarily at anti-vaccine cohorts and racially motivated abusive messages, and a prevalent fatigue towards disease-related news overall. This research provides value to non-profit organizations or government agencies associated with vaccines and vaccination programs in emphasizing the need for public health communication fitted to consumers' vaccine sentiments, levels of health information literacy, and degrees of trust towards public health institutions. Considering the importance of addressing fears among the vaccine hesitant, findings also illustrate the risk of alienation through stigmatization, lead future research in probing the relatively underexamined field of online, vaccine-related stigma, and discuss the potential effects of stigma towards vaccine hesitant Twitter users in their decisions to vaccinate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=social%20marketing" title="social marketing">social marketing</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=public%20health%20communication" title=" public health communication"> public health communication</a>, <a href="https://publications.waset.org/abstracts/search?q=vaccines" title=" vaccines"> vaccines</a> </p> <a href="https://publications.waset.org/abstracts/155856/navigating-states-of-emergency-a-preliminary-comparison-of-online-public-reaction-to-covid-19-and-monkeypox-on-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155856.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">99</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">3</span> Crisis Management and Corporate Political Activism: A Qualitative Analysis of Online Reactions toward Tesla</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Roxana%20D.%20Maiorescu-Murphy">Roxana D. Maiorescu-Murphy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the US, corporations have recently embraced political stances in an attempt to respond to the external pressure exerted by activist groups. To date, research in this area remains in its infancy, and few studies have been conducted on the way stakeholder groups respond to corporate political advocacy in general and in the immediacy of such a corporate announcement in particular. The current study aims to fill in this research void. In addition, the study contributes to an emerging trajectory in the field of crisis management by focusing on the delineation between crises (unexpected events related to products and services) and scandals (crises that spur moral outrage). The present study looked at online reactions in the aftermath of Elon Musk’s endorsement of the Republican party on Twitter. Two data sets were collected from Twitter following two political endorsements made by Elon Musk on May 18, 2022, and June 15, 2022, respectively. The total sample of analysis stemming from the data two sets consisted of N=1,374 user comments written as a response to Musk’s initial tweets. Given the paucity of studies in the preceding research areas, the analysis employed a case study methodology, used in circumstances in which the phenomena to be studied had not been researched before. According to the case study methodology, which answers the questions of how and why a phenomenon occurs, this study responded to the research questions of how online users perceived Tesla and why they did so. The data were analyzed in NVivo by the use of the grounded theory methodology, which implied multiple exposures to the text and the undertaking of an inductive-deductive approach. Through multiple exposures to the data, the researcher ascertained the common themes and subthemes in the online discussion. Each theme and subtheme were later defined and labeled. Additional exposures to the text ensured that these were exhaustive. The results revealed that the CEO’s political endorsements triggered moral outrage, leading to Tesla’s facing a scandal as opposed to a crisis. The moral outrage revolved around the stakeholders’ predominant rejection of a perceived intrusion of an influential figure on a domain reserved for voters. As expected, Musk’s political endorsements led to polarizing opinions, and those who opposed his views engaged in online activism aimed to boycott the Tesla brand. These findings reveal that the moral outrage that characterizes a scandal requires communication practices that differ from those that practitioners currently borrow from the field of crisis management. Specifically, because scandals flourish in online settings, practitioners should regularly monitor stakeholder perceptions and address them in real-time. While promptness is essential when managing crises, it becomes crucial to respond immediately as a scandal is flourishing online. Finally, attempts should be made to distance a brand, its products, and its CEO from the latter’s political views. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crisis%20management" title="crisis management">crisis management</a>, <a href="https://publications.waset.org/abstracts/search?q=communication%20management" title=" communication management"> communication management</a>, <a href="https://publications.waset.org/abstracts/search?q=Tesla" title=" Tesla"> Tesla</a>, <a href="https://publications.waset.org/abstracts/search?q=corporate%20political%20activism" title=" corporate political activism"> corporate political activism</a>, <a href="https://publications.waset.org/abstracts/search?q=Elon%20Musk" title=" Elon Musk"> Elon Musk</a> </p> <a href="https://publications.waset.org/abstracts/152335/crisis-management-and-corporate-political-activism-a-qualitative-analysis-of-online-reactions-toward-tesla" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152335.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">91</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">2</span> We Are the Earth That Defends Itself: An Exploration of Discursive Practices of Les Soulèvements De La Terre</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sophie%20Del%20Fa">Sophie Del Fa</a>, <a href="https://publications.waset.org/abstracts/search?q=Loup%20Ducol"> Loup Ducol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This presentation will focus on the discursive practices of Les Soulèvements de la Terre (hereafter SdlT), a French environmentalist group mobilized against agribusiness. More specifically, we will use, as a case study, the violently repressed demonstration that took place in Sainte-Soline on March 25, 2023 (see after for details). The SdlT embodies the renewal of anti-capitalist and environmentalist struggles that began with Occupy Wall Street in 2009 and in France with the Nuit debout in 2016 and the yellow vests movement from 2019 to 2020. These struggles have three things in common: they are self-organized without official leaders, they rely mainly on occupations to reappropriate public places (squares, roundabouts, natural territories) and they are anti-capitalist. The SdlT was created in 2021 by activists coming from the Zone-to-Defend of Notre-Dame-des-Landes, a victorious 10 yearlong occupation movement against an airport near Nantes, France (from 2009 to 2018). The SdlT is not labeled as a formal association, nor as a constituted group, but as an anti-capitalist network of local struggles at the crossroads of ecology and social issues. Indeed, although they target agro-industry, land grabbing, soil artificialization and ecology without transition, the SdlT considers ecological and social questions as interdependent. Moreover, they have an encompassing vision of ecology that they consider as a concern for the living as a whole by erasing the division between Nature and Culture. Their radicality is structured around three main elements: federative and decentralized dimensions, the rhetoric of living alliances and militant creatives strategies. The objective of this reflexion is to understand how these three dimensions are articulated through the SdlT’s discursive practices. To explore these elements, we take as a case study one specific event: the demonstration against the ‘basins’ held in Sainte-Soline on March 25, 2023, on the construction site of new water storage infrastructure for agricultural irrigation in western France. This event represents a turning point for the SdlT. Indeed, the protest was violently repressed: 5000 grenades were fired by the police, hundreds of people were injured, and one person was still in a coma at the time of writing these lines. Moreover, following Saint-Soline’s events, the Minister of Interior Affairs, Gérald Darmin, threatened to dissolve the SdlT, thus adding fuel to the fire in an already tense social climate (with the ongoing strikes against the pensions reform). We anchor our reflexion on three types of data: 1) our own experiences (inspired by ethnography) of the Sainte-Soline demonstration; 2) the collection of more than 500 000 Tweets with the #SainteSoline hashtag and 3) a press review of texts and articles published after Sainte-Soline’s demonstration. The exploration of these data from a turning point in the history of the SdlT will allow us to analyze how the three dimensions highlighted earlier (federative and decentralized dimensions, rhetoric of living alliances and creatives militant strategies) are materialized through the discursive practices surrounding the Sainte-Soline event. This will allow us to shed light on how a new contemporary movement implements contemporary environmental struggles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discursive%20practices" title="discursive practices">discursive practices</a>, <a href="https://publications.waset.org/abstracts/search?q=Sainte-Soline" title=" Sainte-Soline"> Sainte-Soline</a>, <a href="https://publications.waset.org/abstracts/search?q=Ecology" title=" Ecology"> Ecology</a>, <a href="https://publications.waset.org/abstracts/search?q=radical%20ecology" title=" radical ecology"> radical ecology</a> </p> <a href="https://publications.waset.org/abstracts/166481/we-are-the-earth-that-defends-itself-an-exploration-of-discursive-practices-of-les-soulevements-de-la-terre" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166481.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">71</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">1</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 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