Weaponizing Words in a Connected World: Digital Microaggressions, Weaponised Communication, and Technology-Governed Exposure
Authors
Asia Pacific University of Technology & Innovation (APU) (Malaysia)
Asia Pacific University of Technology & Innovation (APU) (Malaysia)
Universiti Teknologi MARA (UiTM) (Malaysia)
Universiti Teknologi MARA (UiTM) (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.1013COM0010
Subject Category: Digital Communication
Volume/Issue: 10/13 | Page No: 139-157
Publication Timeline
Submitted: 2026-02-22
Accepted: 2026-02-27
Published: 2026-03-17
Abstract
This study examines how digital microaggressions become structurally consequential in platformed communication environments by tracing their structural association with weaponised communication and technology-governed exposure, and by assessing downstream effects on perceived societal vulnerability. Although prior research has examined online hate, emotional virality, and algorithmic amplification in separate streams, fewer studies have modelled how subtle hostile discourse, strategic emotional intensification, and engagement-based platform architectures function as an integrated influence system. Using a cross-sectional survey of 400 active social media users, the study measured four constructs: digital microaggressions (microinsults, microinvalidations, microassaults), weaponised communication (emotional provocation, polarising framing), technology-governed exposure (algorithmic amplification, recommendation frequency), and perceived societal security vulnerability (identity polarisation, social cohesion erosion). Importantly, the construct of societal security vulnerability in this study is conceptualised at the perceptual level. It reflects interpretive assessments of identity polarisation and social cohesion rather than institutional, geopolitical, or policy level instability. This distinction ensures that the findings are bounded within communication-driven risk perception in digital environments. Data were analysed using descriptive statistics and Partial Least Squares Structural Equation Modelling (PLS-SEM). Descriptive results indicate higher exposure to subtle microaggressions (microinsults and microinvalidations) relative to overt hostility, alongside elevated exposure to emotional provocation and perceived algorithmic amplification. Structural results are consistent with a layered amplification pattern in which digital microaggressions are positively associated with weaponised communication, which is associated with technology-governed exposure, corresponding with heightened perceptions of identity polarisation and weakened social cohesion. Rather than implying objective national destabilisation, the findings clarify how repeated exposure to emotionally intensified and algorithmically amplified discourse shapes interpretive perceptions of societal vulnerability. The study advances digital influence research by integrating microaggression theory, emotional contagion dynamics, and platform governance perspectives into a unified structural explanation of how micro level hostility may scale into broader perceptions of societal fragmentation within digitally mediated environments.
Keywords
Digital microaggressions; Weaponised communication; Technology-governed exposure; Societal security vulnerability; Identity polarisation
Downloads
References
1. Abbas, N. F., Eidan, S. S., & Muslah, A. F. (2025). Critical Discourse Analysis of Online Platforms: An Overview. International Journal of Social Sciences and English Literature, 9(8), 27. https://doi.org/10.55220/2576-683x.v9.551 [Google Scholar] [Crossref]
2. Ai, Y., & Mühlenen, A. von. (2025). An experimental online study on the impact of negative social media comments on anxiety and mood. Scientific Reports, 15(1), 26642. https://doi.org/10.1038/s41598-02510810-8 [Google Scholar] [Crossref]
3. Airoldi, M. (2023). Computational Authority in Platform Society. In Oxford University Press eBooks. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780197653609.013.40 [Google Scholar] [Crossref]
4. Ali, S., & Stringhini, G. (2025). Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2502.10921 [Google Scholar] [Crossref]
5. Alloing, C., Fortant, E., Pierre, J., Richert, F., & Palisser, R. (2025). Knowing Your Users by Heart: A Critical Examination of the Scientific Research on Emotions Conducted by Social Media Platforms. Social Media + Society, 11(3). https://doi.org/10.1177/20563051251355456 [Google Scholar] [Crossref]
6. Alnaqbi, H. H., & Ali, E. A. (2025). Social media impact on societal security. Frontiers in Sociology, 10, 1508542. https://doi.org/10.3389/fsoc.2025.1508542 [Google Scholar] [Crossref]
7. Alturif, G., & Al-Sanad, H. A. R. (2023). The repercussions of digital bullying on social media users. Frontiers in Psychology, 14, 1280757. https://doi.org/10.3389/fpsyg.2023.1280757 [Google Scholar] [Crossref]
8. Arora, S. D., Singh, G. P., Chakraborty, A., & Maity, M. (2022). Polarization and social media: A systematic review and research agenda. Technological Forecasting and Social Change, 183, 121942. Elsevier BV. https://doi.org/10.1016/j.techfore.2022.121942 [Google Scholar] [Crossref]
9. Awad, M. N., & Connors, E. H. (2023). Active bystandership by youth in the digital era: Microintervention strategies for responding to social media-based microaggressions and cyberbullying. Psychological Services, 20(3), 423. https://doi.org/10.1037/ser0000749 [Google Scholar] [Crossref]
10. Banerjee, A., & Urminsky, O. (2024). The Language That Drives Engagement: A Systematic Large-scale Analysis of Headline Experiments. Marketing Science, 44(3), 566. https://doi.org/10.1287/mksc.2021.0018 [Google Scholar] [Crossref]
11. Barth, N., Wagner, E., Raab, P., & Wiegärtner, B. (2023). Contextures of hate: Towards a systems theory of hate communication on social media platforms. The Communication Review, 26(3), 209. https://doi.org/10.1080/10714421.2023.2208513 [Google Scholar] [Crossref]
12. Baumann, F., Arora, N., Rahwan, I., & Czaplicka, A. (2025). Dynamics of Algorithmic Content Amplification on TikTok. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2503.20231 [Google Scholar] [Crossref]
13. Berger, J., & Milkman, K. L. (2012). What Makes Online Content Viral? Journal of Marketing Research, 49(2), 192. https://doi.org/10.1509/jmr.10.0353 [Google Scholar] [Crossref]
14. Bernstein, M. S., Christin, A., Hancock, J. T., Hashimoto, T., Jia, C., Lam, M. S., Meister, N., Persily, N., Piccardi, T., Saveski, M., Tsai, J. L., Ugander, J., & Xu, C. (2023). Embedding Societal Values into Social Media Algorithms. Journal of Online Trust and Safety, 2(1). https://doi.org/10.54501/jots.v2i1.148 [Google Scholar] [Crossref]
15. Bodo, B. (2025). Humble tools of divine intervention – The misunderstood role of algorithms in public opinion formation. Dialogues on Digital Society. https://doi.org/10.1177/29768640251369974 [Google Scholar] [Crossref]
16. Bouchaud, P. (2024). Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feeds. Journal of Computational Social Science, 7(1), 721. https://doi.org/10.1007/s42001-024-00255-w [Google Scholar] [Crossref]
17. Brady, W. J., Jackson, J. C., Lindström, B., & Crockett, M. J. (2023). Algorithm-mediated social learning in online social networks. Trends in Cognitive Sciences, 27(10), 947. Elsevier BV. https://doi.org/10.1016/j.tics.2023.06.008 [Google Scholar] [Crossref]
18. Brady, W. J., McLoughlin, K. L., Doan, T. N., & Crockett, M. J. (2021). How social learning amplifies moral outrage expression in online social networks. Science Advances, 7(33). https://doi.org/10.1126/sciadv.abe5641 [Google Scholar] [Crossref]
19. Burnell, K., Trekels, J., George, M. J., & Nesi, J. (2024). Digital Cruelty’s Impact on Self-Esteem and Body Image (p. 439). https://doi.org/10.1007/978-3-031-69362-5_60 [Google Scholar] [Crossref]
20. Çakmak, M. C., Agarwal, N., & Oni, R. (2024). The bias beneath: analyzing drift in YouTube’s algorithmic recommendations. Social Network Analysis and Mining, 14(1). https://doi.org/10.1007/s13278-02401343-5 [Google Scholar] [Crossref]
21. Chan, J., Choi, F., Saha, K., & Chandrasekharan, E. (2025). The Ranking Effect: How Algorithmic Rank Influences Attention on Social Media. https://doi.org/10.48550/ARXIV.2509.18440 [Google Scholar] [Crossref]
22. Chaudhary, Y., & Penn, J. (2024). Large Language Models as Instruments of Power: New Regimes of Autonomous Manipulation and Control. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2405.03813 [Google Scholar] [Crossref]
23. Ciavolino, E., Ferrante, L., Sternativo, G. A., Cheah, J., Rollo, S., Marinaci, T., & Venuleo, C. (2021). A confirmatory composite analysis for the Italian validation of the interactions anxiousness scale: a higherorder version. Behaviormetrika, 49(1), 23. https://doi.org/10.1007/s41237-021-00151-x [Google Scholar] [Crossref]
24. Clifton, M., Clifton, R. L., & Zapolski, T. C. B. (2025). Racial Media Microaggressions: Impact on Black Adolescent Mental and Behavioral Health. OTJR Occupational Therapy Journal of Research. https://doi.org/10.1177/15394492251385473 [Google Scholar] [Crossref]
25. Conn, V. S. (2020). Crafting Effective Abstracts. Western Journal of Nursing Research, 44(7), 635. https://doi.org/10.1177/0193945920920638 [Google Scholar] [Crossref]
26. Costa, P. L., McDuffie, J. W., Brown, S. E. V., He, Y., Ikner, B. N., Sabat, I. E., & Miner, K. N. (2022). Microaggressions: Mega problems or micro issues? A meta‐analysis. Journal of Community Psychology, 51(1), 137. Wiley. https://doi.org/10.1002/jcop.22885 [Google Scholar] [Crossref]
27. Cover, R. (2024). Apprehending digital hostility and online abuse: Feminist care ethics in/and digital ecologies. Thesis Eleven, 183(1), 33. https://doi.org/10.1177/07255136241284658 [Google Scholar] [Crossref]
28. Cui, P. (2025). Dynamics and Inequalities in Digital Social Networks: A Computational and Sociological Review. https://doi.org/10.48550/ARXIV.2503.02887 [Google Scholar] [Crossref]
29. DeCuir‐Gunby, J. T., McCoy, W. N., Gibson, S. M., Modaressi, S. L., & Macias, A. J. (2024). Using Critical Race Mixed Methodology to Explore African American College Students’ Experiences with Racial Microaggressions. Innovative Higher Education, 49(6), 1077. https://doi.org/10.1007/s10755-024-097326 [Google Scholar] [Crossref]
30. Derald, W. (2022). Microaggressions: Death by a Thousand Cuts. Scientific American, 31, 48. https://doi.org/10.1038/scientificamericanracism0621-48 [Google Scholar] [Crossref]
31. D’Ignazi, J., Kaltenbrunner, A., Mens, G. L., Germano, F., & Gómez, V. (2025). Rewarding Engagement and Personalization in Popularity-Based Rankings Amplifies Extremism and Polarization. https://doi.org/10.48550/ARXIV.2510.24354 [Google Scholar] [Crossref]
32. Erstad, O., Hegna, K., Livingstone, S., Negru‐Subtirica, O., & Stoilova, M. (2024). How digital technologies become embedded in family life across generations: scoping the agenda for researching ‘platformised relationality.’ Families Relationships and Societies, 13(2), 164. https://doi.org/10.1332/20467435y2024d000000023 [Google Scholar] [Crossref]
33. Eschmann, R. (2020). Digital Resistance: How Online Communication Facilitates Responses to Racial Microaggressions. Sociology of Race and Ethnicity, 7(2), 264. https://doi.org/10.1177/2332649220933307 [Google Scholar] [Crossref]
34. Galeazzi, A., Paudel, P., Conti, M., De Cristofaro, E., & Stringhini, G. (2024). Revealing The Secret Power: How Algorithms Can Influence Content Visibility on Twitter/X. https://doi.org/10.48550/ARXIV.2410.17390 [Google Scholar] [Crossref]
35. Gandini, A., Keeling, S., & Reviglio, U. (2025). Conceptualising the ‘algorithmic public opinion’: Public opinion formation in the digital age. Dialogues on Digital Society. https://doi.org/10.1177/29768640251323147 [Google Scholar] [Crossref]
36. Garzonio, E. (2022). Performative Intermediaries Versus Digital Regulation. A Multidisciplinary Analysis of the Power of Algorithms. In Frontiers in sociology and social research (p. 157). Springer International Publishing. https://doi.org/10.1007/978-3-031-11756-5_10 [Google Scholar] [Crossref]
37. Goldenberg, A., & Willer, R. (2023). Amplification of emotion on social media. Nature Human Behaviour, 7(6), 845. https://doi.org/10.1038/s41562-023-01604-x [Google Scholar] [Crossref]
38. Gombar, M., & Cvitković, M. K. (2025). Cognitive Resonance Theory in Strategic Communication: Understanding Personalization, Emotional Resonance, and Echo Chambers. OALib, 12(4), 1. https://doi.org/10.4236/oalib.1113171 [Google Scholar] [Crossref]
39. Goswami, G. (2025). AI Echo Chambers: How Algorithms Shape Reality, Influence Democracy, and Fuel Misinformation. https://doi.org/10.36227/techrxiv.174059950.03385147/v1 [Google Scholar] [Crossref]
40. Habib, H., & Nithyanand, R. (2025a). YouTube Recommendations Reinforce Negative Emotions: Auditing Algorithmic Bias with Emotionally-Agentic Sock Puppets. https://doi.org/10.48550/ARXIV.2501.15048 [Google Scholar] [Crossref]
41. Habib, H., & Nithyanand, R. (2025b). YouTube Recommendations Reinforce Negative Emotions: Auditing Algorithmic Bias with Emotionally-Agentic Sock Puppets. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2501.15048 [Google Scholar] [Crossref]
42. Hall, A. R., Meter, D. J., Culianos, D., Uresti, A., Medina, M., & Nishina, A. (2025). The effects of college students’ online experiences with racial/ethnic discrimination. Social Psychology of Education, 28(1). https://doi.org/10.1007/s11218-025-10137-2 [Google Scholar] [Crossref]
43. Harmer, E., & Southern, R. (2021). Digital microaggressions and everyday othering: an analysis of tweets sent to women members of Parliament in the UK. Information Communication & Society, 24(14), 1998. https://doi.org/10.1080/1369118x.2021.1962941 [Google Scholar] [Crossref]
44. Hartmann, D., Oueslati, A., & Staufer, D. (2024). Watching the Watchers: A Comparative Fairness Audit of Cloud-based Content Moderation Services. https://doi.org/10.48550/ARXIV.2406.14154 [Google Scholar] [Crossref]
45. Hartmann, D., Oueslati, A., Staufer, D., Pohlmann, L., Munzert, S., & Heuer, H. (2025). Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations. 1. https://doi.org/10.1145/3706598.3713998 [Google Scholar] [Crossref]
46. Hu, B., Zhu, Y., Liu, C., Zheng, S., Zhao, Z., & Bao, R. (2024). Collectivism, face concern and Chinesestyle lurking among university students: the moderating role of trait mindfulness. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1298357 [Google Scholar] [Crossref]
47. Huffaker, J. S. (2023). Social Reference Processing with Collaborative Human-AI Systems. Deep Blue (University of Michigan). https://doi.org/10.7302/8321 [Google Scholar] [Crossref]
48. Hunt, R. A., Townsend, D. M., Simpson, J., Nugent, R., Stallkamp, M., & Bozdag, E. (2024). Digital Battlegrounds: The Power Dynamics and Governance of Contemporary Platforms. Academy of Management Annals. https://doi.org/10.5465/annals.2022.0188 [Google Scholar] [Crossref]
49. Huszár, F., Ktena, S. I., O’Brien, C., Belli, L., Schlaikjer, A., & Hardt, M. (2021). Algorithmic amplification of politics on Twitter. Proceedings of the National Academy of Sciences, 119(1). https://doi.org/10.1073/pnas.2025334119 [Google Scholar] [Crossref]
50. Idris, Z., Warganegara, D. S., Saabar, S. S., Aziz, A. A., & Idris, J. (2025). Microaggressions on Social Media: A Conceptual Framework of 3R Issues and Youth Consumer Behaviour in Malaysia. International Journal of Research and Innovation in Social Science, 314. https://doi.org/10.47772/ijriss.2025.913com0029 [Google Scholar] [Crossref]
51. Jahn, L. (2023). Curbing Amplifiation Online : Towards Improving the Quality of Information Spread on Social Media Using Agent-Based Models and Twitter Data. Research Portal Denmark, 150. https://local.forskningsportal.dk/local/dki-cgi/ws/cris-link?src=ku&id=ku-ef61aeed-56f5-4d78b171a00a80798d1a&ti=Curbing%20Amplifiation%20Online%20%3A%20Towards%20Improving%20the%20Quality%20of%20Information%20Spread%20on%20Social%20Media%20Using%20AgentBased%20Models%20and%20Twitter%20Data [Google Scholar] [Crossref]
52. Kasi, A. Z., Kasi, M., & Qadir, A. (2021). The Effects of Social Media on National Security: An Overview. Global Strategic & Securities Studies Review, 121. https://doi.org/10.31703/gsssr.2021(vi-i).13 [Google Scholar] [Crossref]
53. Kearney, A., Poredi, N., Shelton, J., Akcinaroglu, S., Karakoç, E., Tran, T. N. T., & Chen, Y. (2025). Echoes amplified: a study of AI-generated content and digital echo chambers. 23. https://doi.org/10.1117/12.3053447 [Google Scholar] [Crossref]
54. Khambatta, P., Mariadassou, S., Morris, J., & Wheeler, S. C. (2023). Tailoring recommendation algorithms to ideal preferences makes users better off. Scientific Reports, 13(1), 9325. https://doi.org/10.1038/s41598-023-34192-x [Google Scholar] [Crossref]
55. Kirasur, N., & Jhaver, S. (2024). Understanding the Resilience of Caste: A Critical Discourse Analysis of Community Profiles on X. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2407.02810 [Google Scholar] [Crossref]
56. Kossowska, M., Kłodkowski, P., Siewierska-Chmaj, A., Guinote, A., Kessels, U., Moyano, M., & Strömbäck, J. (2023). Internet-based micro-identities as a driver of societal disintegration. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02441-z [Google Scholar] [Crossref]
57. Lazar, S. (2024). Lecture II: Communicative Justice and the Distribution of Attention. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2410.20718 [Google Scholar] [Crossref]
58. Leach, S., Formanowicz, M., Nikadon, J., & Cichocka, A. (2025). Moral Outrage Predicts the Virality of Petitions for Change on Social Media, But Not the Number of Signatures They Receive. Social [Google Scholar] [Crossref]
59. Psychological and Personality Science, 17(2), 194. https://doi.org/10.1177/19485506251335373 [Google Scholar] [Crossref]
60. Lerman, K., Feldman, D., He, Z., & Rao, A. (2024). Affective polarization and dynamics of information spread in online networks. Npj Complexity, 1(1). https://doi.org/10.1038/s44260-024-00008-w [Google Scholar] [Crossref]
61. Li, C., Qin, J., Wang, X., & Xue, F. (2024). Navigating the Digital Horizon: The Impact and Future of Communication Technologies in Society. Lecture Notes in Education Psychology and Public Media, 51(1), 223. https://doi.org/10.54254/2753-7048/51/20240938 [Google Scholar] [Crossref]
62. Luo, Y., Sornette, D., & Lera, S. C. (2024). Quantification of the Self-Excited Emotion Dynamics in Online Interactions. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2408.05700 [Google Scholar] [Crossref]
63. Marchi, M., Travascio, A., Uberti, D., Micheli, E. D., Quartaroli, F., Laquatra, G., Grenzi, P., Pingani, L., Ferrari, S., Fiorillo, A., Converti, M., Pinna, F., Amaddeo, F., Ventriglio, A., Mirandola, M., & Galeazzi, G. M. (2023). Microaggression toward LGBTIQ people and implications for mental health: A systematic. International Journal of Social Psychiatry, 70(1), 23. SAGE Publishing. https://doi.org/10.1177/00207640231194478 [Google Scholar] [Crossref]
64. Mariano, L. de O., Moura, L. de, Mattos, R. H. P., Bizarria, F. P. de A., & Kind, L. (2025). Faces of exclusion: the “social,” the “digital” and “digital racism” in a decolonial critical essay. Frontiers in Sociology, 10, 1534313. https://doi.org/10.3389/fsoc.2025.1534313 [Google Scholar] [Crossref]
65. Marino, E. B., Baleato, S., & Ribeiro, A. S. (2024). The Polarization Loop: How Emotions Drive Propagation of Disinformation in Online Media—The Case of Conspiracy Theories and Extreme Right Movements in Southern Europe. Social Sciences, 13(11), 603. https://doi.org/10.3390/socsci13110603 [Google Scholar] [Crossref]
66. Martino, E. D., Galeazzi, A., Starnini, M., Quattrociocchi, W., & Cinelli, M. (2024). Characterizing the Fragmentation of the Social Media Ecosystem. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.16826 [Google Scholar] [Crossref]
67. McInroy, L. B., Beer, O., Scheadler, T. R., Craig, S. L., & Eaton, A. D. (2023). Exploring the psychological and physiological impacts of digital microaggressions and hostile online climates on LGBTQ + youth. Current Psychology, 43(3), 2586. https://doi.org/10.1007/s12144-023-04435-1 [Google Scholar] [Crossref]
68. McInroy, L. B., Scheadler, T. R., McDonald, M., Eaton, A. D., & Craig, S. L. (2025). Digital microaggressions and LGBTQ+ youth: exploring potential impacts and opportunities for educational intervention. Educational Psychology, 1. https://doi.org/10.1080/01443410.2025.2541743 [Google Scholar] [Crossref]
69. McLoughlin, K. L., Brady, W. J., Goolsbee, A., Kaiser, B., Klonick, K., & Crockett, M. J. (2024). Misinformation exploits outrage to spread online. Science, 386(6725), 991. https://doi.org/10.1126/science.adl2829 [Google Scholar] [Crossref]
70. Megersa, T., & Minaye, A. (2023). Social media users’ online behavior with regard to the circulation of hate speech. Frontiers in Communication, 8. https://doi.org/10.3389/fcomm.2023.1276245 [Google Scholar] [Crossref]
71. Metzler, H., & García, D. (2023). Social Drivers and Algorithmic Mechanisms on Digital Media. Perspectives on Psychological Science, 19(5), 735. https://doi.org/10.1177/17456916231185057 [Google Scholar] [Crossref]
72. Milli, S., Carroll, M., Wang, Y., Pandey, S., Zhao, S., & Dragan, A. D. (2025). Engagement, user satisfaction, and the amplification of divisive content on social media. PNAS Nexus, 4(3). https://doi.org/10.1093/pnasnexus/pgaf062 [Google Scholar] [Crossref]
73. Murray, R., Orr, B., Al-khateeb, S., & Agarwal, N. (2025). Constructing a multi-theoretical framework for mob modeling. Social Network Analysis and Mining, 15(1). https://doi.org/10.1007/s13278-025-01449-4 [Google Scholar] [Crossref]
74. Myrick, R., & Wang, C. (2024). Domestic Polarization and International Rivalry: How Adversaries Respond to America’s Partisan Politics. The Journal of Politics, 86(1), 141. https://doi.org/10.1086/726926 [Google Scholar] [Crossref]
75. Nordbrandt, M. (2021). Affective polarization in the digital age: Testing the direction of the relationship between social media and users’ feelings for out-group parties. New Media & Society, 25(12), 3392. https://doi.org/10.1177/14614448211044393 [Google Scholar] [Crossref]
76. Ocampo, N. B., Свиридова, Е., Cabrio, E., & Villata, S. (2023). An In-depth Analysis of Implicit and Subtle Hate Speech Messages. 1997. https://doi.org/10.18653/v1/2023.eacl-main.147 [Google Scholar] [Crossref]
77. Oksanen, A., Celuch, M., Latikka, R., Oksa, R., & Savela, N. (2021). Hate and harassment in academia: the rising concern of the online environment. Higher Education, 84(3), 541. https://doi.org/10.1007/s10734-021-00787-4 [Google Scholar] [Crossref]
78. Park, A., Kim, M., & Kim, E.-S. (2023). SEM analysis of agreement with regulating online hate speech: influences of victimization, social harm assessment, and regulatory effectiveness assessment. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1276568 [Google Scholar] [Crossref]
79. Park, J., & Kim, S. (2024). Why do users perceive digital platforms as indispensable to their lives?: A study on KakaoTalk in Korea. Telecommunications Policy, 48(10), 102863. https://doi.org/10.1016/j.telpol.2024.102863 [Google Scholar] [Crossref]
80. Patil, S., Jani, A. S., & Konatam, S. (2024). Trend Amplification or Suppression: The Dual Role of AI in [Google Scholar] [Crossref]
81. Influencing Viral Content. International Journal of Global Innovations and Solutions (IJGIS). https://doi.org/10.21428/e90189c8.361bcc7f [Google Scholar] [Crossref]
82. Paziuk, A., Lande, D., Shnurko-Tabakova, E., & Kingston, P. (2025). Decoding manipulative narratives in cognitive warfare: a case study of the Russia-Ukraine conflict. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1566022 [Google Scholar] [Crossref]
83. Piccardi, T., Saveski, M., Jia, C., Hancock, J. T., Tsai, J. L., & Bernstein, M. A. (2024a). Social Media Algorithms Can Shape Affective Polarization via Exposure to Antidemocratic Attitudes and Partisan Animosity. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.14652 [Google Scholar] [Crossref]
84. Piccardi, T., Saveski, M., Jia, C., Hancock, J. T., Tsai, J. L., & Bernstein, M. S. (2024b). Reranking Social Media Feeds: A Practical Guide for Field Experiments. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.19571 [Google Scholar] [Crossref]
85. Piccardi, T., Saveski, M., Jia, C., Hancock, J., Tsai, J. L., & Bernstein, M. S. (2025). Reranking partisan animosity in algorithmic social media feeds alters affective polarization. Science, 390(6776). https://doi.org/10.1126/science.adu5584 [Google Scholar] [Crossref]
86. Pierce, G. L., Holland, C. C., Cleary, P. F., & Rabrenovic, G. (2022). The opportunity costs of the politics of division and disinformation in the context of the twenty-first century security deficit. SN Social Sciences, 2(11). https://doi.org/10.1007/s43545-022-00514-5 [Google Scholar] [Crossref]
87. Pottier, P., Lagisz, M., Burke, S., Drobniak, S. M., Downing, P. A., Macartney, E. L., Martinig, A. R., Mizuno, A., Morrison, K., Pollo, P., Ricolfi, L., Tam, J., Williams, C., Yang, Y., & Nakagawa, S. (2024). Title, abstract and keywords: a practical guide to maximize the visibility and impact of academic papers. Proceedings of the Royal Society B Biological Sciences, 291(2027), 20241222. https://doi.org/10.1098/rspb.2024.1222 [Google Scholar] [Crossref]
88. Pratap, A., & Pathak, A. (2025). From Public Square to Echo Chamber: The Fragmentation of Online Discourse. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2501.18441 [Google Scholar] [Crossref]
89. Shien, O. Y., Huei, N. S., & Yan, N. L. (2023). The Impact of Social Media Marketing on Young Consumers’ Purchase Intention in Malaysia: The Mediating Role of Consumer Engagement. International Journal of Academic Research in Business and Social Sciences, 13(1). https://doi.org/10.6007/ijarbss/v13i1/15806 [Google Scholar] [Crossref]
90. Shin, D. (2021). Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm. Journal of Information Science, 49(1), 18. https://doi.org/10.1177/0165551520985495 [Google Scholar] [Crossref]
91. Stappen, L., Baird, A., Lienhart, M., Bätz, A., & Schuller, B. W. (2022). An Estimation of Online Video User Engagement From Features of Time- and Value-Continuous, Dimensional Emotions. Frontiers in Computer Science, 4. https://doi.org/10.3389/fcomp.2022.773154 [Google Scholar] [Crossref]
92. Stepanov, V. N. (2023). Emotion vs conflict-generating communication in a hybrid media environment. RUDN Journal of Studies in Literature and Journalism, 28(4), 800. https://doi.org/10.22363/2312-9220-2023-28-4-800-808 [Google Scholar] [Crossref]
93. Strauss, C., Harr, M. D., & Pieper, T. M. (2024). Analyzing digital communication: a comprehensive literature review. Management Review Quarterly, 75(4), 3119. https://doi.org/10.1007/s11301-02400455-8 [Google Scholar] [Crossref]
94. Tong, S. T. (2024). Foundations, Definitions, and Directions in Online Hate Research. In Routledge eBooks (p. 37). Informa. https://doi.org/10.4324/9781003472148-3 [Google Scholar] [Crossref]
95. Vaishnav, S., & Wallace, D. (2022). Navigating Microaggressions in Online Learning Environments. Journal of Technology in Counselor Education and Supervision, 2(2). https://doi.org/10.22371/tces/0020 [Google Scholar] [Crossref]
96. Vasist, P. N., Chatterjee, D., & Krishnan, S. (2023). The Polarizing Impact of Political Disinformation and Hate Speech: A Cross-country Configural Narrative. Information Systems Frontiers, 26(2), 663. https://doi.org/10.1007/s10796-023-10390-w [Google Scholar] [Crossref]
97. Vasist, P. N., & Krishnan, S. (2022). ICT4D: development or destabilization? A cross-country study on the polarizing effect of political disinformation through social media. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-2099119/v1 [Google Scholar] [Crossref]
98. Voinea, C., Marin, L., & Vică, C. (2024). Digital Slot Machines: Social Media Platforms as Attentional Scaffolds. Topoi, 43(3), 685. https://doi.org/10.1007/s11245-024-10031-0 [Google Scholar] [Crossref]
99. Vombatkere, K., Mousavi, S., Zannettou, S., Roesner, F., & Gummadi, K. P. (2024). TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media Feeds. 3789. https://doi.org/10.1145/3589334.3645600 [Google Scholar] [Crossref]
100. Vu, N. V., Nazari, M. A., Dang, T., Muralev, Y., Mohanraj, M., Tran, T., & Quoc, H. A. (2025). Type of the Paper: Article. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5384374 [Google Scholar] [Crossref]
101. Wu, J. (2024). Social and ethical impact of emotional AI advancement: the rise of pseudo-intimacy relationships and challenges in human interactions. Frontiers in Psychology, 15, 1410462. https://doi.org/10.3389/fpsyg.2024.1410462 [Google Scholar] [Crossref]
102. Wu, Y., Zhao, M., & Canny, J. (2025). Beyond Likes: How Normative Feedback Complements Engagement Signals on Social Media. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2505.09583 [Google Scholar] [Crossref]
103. Yadin, D., Yahav, I., Zalmanson, L., & Munichor, N. (2023). Resolving the Ethical Tension Between Creating a Civil Environment and Facilitating Free Expression Online: Comment Reordering as an Alternative to Comment Moderation. Journal of Business Ethics, 192(2), 261. https://doi.org/10.1007/s10551-023-05450-9 [Google Scholar] [Crossref]
104. Yo, P. W., Kee, D. M. H., Yu, J. W., Hu, M. K., Jong, Y. C., Ahmed, Z., Gwee, S. L., Gawade, O., & Nair, R. K. (2021). The Influencing Factors of Customer Satisfaction: A Case Study of Shopee in Malaysia. Studies of Applied Economics, 39(12). https://doi.org/10.25115/eea.v39i12.6839 [Google Scholar] [Crossref]
105. Yu, M., & Riddle, K. (2022). An Experimental Test of the Effects of Digital Content Permanency on Perceived Anonymity and Indirect Effects on Cyber Bullying Intentions. Social media & Society, 8(1). https://doi.org/10.1177/20563051221087255 [Google Scholar] [Crossref]
106. Yu, Z., Sen, I., Assenmacher, D., Samory, M., Fröhling, L., Dahn, C., Nozza, D., & Wagner, C. (2024). The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets. arXiv (Cornell University). Cornell University. https://doi.org/10.1177/08944393241258771 [Google Scholar] [Crossref]
107. Zhang, J. (2025). The Emotional Economy in Social Media Platforms: An Analysis of Algorithm-Driven User Engagement and Commercial Value. Highlights in Art and Design, 12(2), 113. https://doi.org/10.54097/87aa6f65 [Google Scholar] [Crossref]