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Persistent interaction patterns across social media platforms and over time

Growing concern surrounds the impact of social media platforms on public discourse1–4 and their influence on social dynamics5–9, especially in the context of toxicity10–12. Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years—from Usenet to contemporary social media—our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contras ....

South Korea , United States , Francisci Morales , Facebook Community Standards , Conference On Online Social Networks , European Language Resources Association , Wh Network , Terms Of Service , How Does Youtube Enforce Its Community Guidelines , Meeting Of The Association For Computational Linguistics , International World Wide Web Conferences Steering Committee , Association For Computational Linguistics , Association For Computing Machinery , Association For The Advancement Of Artificial Intelligence , Social Netw , Media Soc , Wide Web Conference , Human Factors , Computing Systems , Computing Machinery , Science Conference , Language Resources , Evaluation Conference , Social Media , Artificial Intelligence , Knowledge Discovery ,

Frontiers | To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media

As a worldwide epidemic in the digital age, cyberbullying is a pertinent but understudied concern especially from the perspective of language. Elucidating the linguistic features of cyberbullying is critical both to preventing it and to cultivating ethical and responsible digital citizens. In this study, a mixed-method approach integrating lexical feature analysis, sentiment polarity analysis, and semantic network analysis was adopted to develop a deeper understanding of cyberbullying language. Five cyberbullying cases on Chinese social media were analyzed to uncover explicit and implicit linguistic features. Results indicated that cyberbullying comments had significantly different linguistic profiles than non-bullying comments and that explicit and implicit bullying were distinct. The content of cases further suggested that cyberbullying language varied in the use of words, types of cyberbullying, and sentiment polarity. These findings offer useful insight for designing automatic cybe ....

New York , United States , United Kingdom , Ying Chao , City Of , Kyongsang Bukto , South Korea , Saudi Arabia , Textmind Gao , Faizzuddin Zainal , Sina Weibo , Tencent Weibo , Penerbit Universiti Kebangsaan , Ibn Rafiq , M Schultz Oxford , Cohen Kappa , International World Wide Web Conferences Steering Committee , Science Research Network , Association For Computing Machinery , Proceeding Of Knowledge Management International Conference , International Conference On Information Management , Semantic Network Analysis , Cambridge University Press , National Social Science Fund Of China , International Conference On Machine , Postdoctoral Research Foundation Of China ,