2000 character limit reached
What Makes A Video Radicalizing? Identifying Sources of Influence in QAnon Videos (2404.14616v1)
Published 22 Apr 2024 in cs.SI
Abstract: In recent years, radicalization is being increasingly attempted on video-sharing platforms. Previous studies have been proposed to identify online radicalization using generic social context analysis, without taking into account comprehensive viewer traits and how those can affect viewers' perception of radicalizing content. To address the challenge, we examine QAnon, a conspiracy-based radicalizing group, and have designed a comprehensive questionnaire aiming to understand viewers' perceptions of QAnon videos. We outline the traits of viewers that QAnon videos are the most appealing to, and identify influential factors that impact viewers' perception of the videos.
- Identifying the popularity and persuasiveness of right-and left-leaning group videos on social media. In 2021 IEEE International Conference on Big Data (Big Data), pages 2454–2460. IEEE.
- Amarnath Amarasingam and Marc-André Argentino. 2020. The qanon conspiracy theory: A security threat in the making. CTC Sentinel, 13(7):37–44.
- Oscar Araque and Carlos A Iglesias. 2020. An approach for radicalization detection based on emotion signals and semantic similarity. IEEE Access, 8:17877–17891.
- Combining social network analysis and sentiment analysis to explore the potential for online radicalisation. In 2009 International Conference on Advances in Social Network Analysis and Mining, pages 231–236. IEEE.
- Randy Borum. 2011. Radicalization into violent extremism i: A review of social science theories. Journal of strategic security, 4(4):7–36.
- Paul Ekman and Wallace V Friesen. 1971. Constants across cultures in the face and emotion. Journal of personality and social psychology, 17(2):124.
- Qanon conspiracy theory: examining its evolution and mechanisms of radicalization. Journal for Deradicalization, (26):152–216.
- A very brief measure of the big-five personality domains. Journal of Research in personality, 37(6):504–528.
- Ranking right-wing extremist social media profiles by similarity to democratic and extremist groups. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 24–33, Copenhagen, Denmark. Association for Computational Linguistics.
- Modeling ideological salience and framing in polarized online groups with graph neural networks and structured sparsity. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 536–550, Seattle, United States. Association for Computational Linguistics.
- Clayton Hutto and Eric Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media, volume 8, pages 216–225.
- Towards the automatic identification and monitoring of radicalization activities in twitter. In International Conference on Knowledge Management in Organizations, pages 589–599. Springer.
- Acoustic-prosodic, lexical and demographic cues to persuasiveness in competitive debate speeches. In Interspeech, pages 1034–1038.
- The development and psychometric properties of liwc2015. Technical report.
- SpeechBrain: A general-purpose speech toolkit. ArXiv:2106.04624.
- Auditing radicalization pathways on youtube. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 131–141.
- Rode Snehal Sudhakar and Manjare Chandraprabha Anil. 2015. Analysis of speech features for emotion detection: a review. In 2015 International Conference on Computing Communication Control and Automation, pages 661–664. IEEE.
- The grievance dictionary: Understanding threatening language use. Behavior research methods, 53(5):2105–2119.
- What makes a speaker charismatic? producing and perceiving charismatic speech. In Proc. 10th International Conference on Speech Prosody, volume 2020, pages 685–689.