[
Al-Khatib, K., Wachsmuth, H., Kiesel, J. et al., 2016. A News Editorial Corpus for Mining Argumentation Strategies. In Y. Matsumoto & R. Prasad (Eds.), Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. The COLING 2016 Organizing Committee, pp. 3433–3443.
]Search in Google Scholar
[
Bou-Franch, P., Lorenzo-Dus, N., & Blitvich, P. G., 2012. Social Interaction in YouTube Text-Based Polylogues: A Study of Coherence. Journal of Computer-Mediated Communication, 17, 501–521.
]Search in Google Scholar
[
Card, D., Boydstun, A. E., Gross, J. H. et al., 2015. The Media Frames Corpus: Annotations of Frames Across Issues. In: 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing Conference Proceeding (Short Papers), China: Beijing, pp. 4171–4186.
]Search in Google Scholar
[
Ceci, L., 2021a. Distribution of video comments removed from YouTube worldwide Q3 2021, by reason. Edited by Google. Statista, available at: < https://www.statista.com/statistics/1133165/share-removed-youtube-video-comments-worldwide-by-reason/>.
]Search in Google Scholar
[
Ceci, L., 2021b. YouTube - Statistics & Facts. Statista, available at: < https://www.statista.com/topics/2019/youtube/#dossierKeyfigures>.
]Search in Google Scholar
[
Da San Martino, G., Yu, S., Barrón-Cedeño, A. et al., 2019. Fine-Grained Analysis of Propaganda in News Articles. In: 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing Conference Proceeding, China: Hon Kong, pp. 5636–5646.
]Search in Google Scholar
[
Da San Martino, G., Barrón-Cedeño, A., Wachsmuth, H. et al., 2020. SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. In: 14th International Workshop on Semantic Evaluation Proceeding conference, Barcelona, Spain, pp. 1377–1414.
]Search in Google Scholar
[
Del Vicario, M., Bessi, A., Zollo, F. et al., 2016. The spreading of misinformation online. Proceedings of the National Academy of Sciences of the United States of America, 113(3), 554–559.
]Search in Google Scholar
[
Demata, M., Heaney, D., & Herring, S. C. (Eds.)., 2018. Language and Discourse in Social Media: New Challenges, New Approaches. Altre Modernità: Università degli Studi di Milano.
]Search in Google Scholar
[
Deutsches Institut für Menschenrechte (DIM)., 2017. Gutachten: Geschlechtervielfalt im Recht. Status quo und Entwicklung von Regelungsmodellen zur Anerkennung und zum Schutz von Geschlechtervielfalt. In Bundesministerium für Familie, Frauen, Senioren und Jugend (Eds.), Begleitmaterial zur Interministeriellen Arbeitsgruppe Inter- und Transsexualität – Band 8, available at: < https://www.bmfsfj.de/resource/blob/114066/8a02a557eab695bf7179ff2e92d0ab28/imag-band-8-geschlechtervielfalt-im-rechtdata.pdf>.
]Search in Google Scholar
[
Devlin, J., Chang, M.-W., Lee, K. et al., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019, Minneapolis, Minnesota, USA, pp. 4171–4186.
]Search in Google Scholar
[
Dynel, M., 2014. Participation framework underlying YouTube interaction. Journal of Pragmatics, 73, 37–52.
]Search in Google Scholar
[
Eger, S., Daxenberger, J., & Gurevych, I., 2017. Neural Endto-End Learning for Computational Argumentation Mining. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, pp. 11–22.
]Search in Google Scholar
[
European Union Agency for Fundamental Rights (FRA)., 2015. Being Trans in the EU. Comparative analysis of the EU LGBT survey data. Summary. European Union Agency for Fundamental Rights (FRA)., 2020. EU-LGBTI II: A long way to go for LGBTI equality. Luxembourg.
]Search in Google Scholar
[
Europäische Kommission, Generaldirektion Justiz (EK)., 2011. Trans- und intersexuelle Menschen. Diskriminierung von trans- und intersexuellen Menschen aufgrund des Geschlechts, der Geschlechtsidentität und des Geschlechtsausdrucks. Amt für amtliche Veröffentlichungen der Europäischen Gemeinschaften, available at: < https://op.europa.eu/o/opportal-service/download-handler?identifier=9b338479-c1b5-4d88-a1f8a248a19466f1&format=pdf&language=de&productionSystem=cellar&part=>
]Search in Google Scholar
[
Fairclough, N., 1989. Language and power. Longman Group.
]Search in Google Scholar
[
Fairclough, N., 1992. Discourse and Social Change. Polity Press.
]Search in Google Scholar
[
Fleiss, J. L., 1971. Measuring nominal scale agreement among many raters. Psychological bulletin, 76(5), 378.
]Search in Google Scholar
[
Franzen, J., & Sauer, A., 2010. Benachteiligung von Trans*Personen, insbesondere im Arbeitsleben. Antidiskriminierungsstelle des Bundes, available at: < https://www.antidiskriminierungsstelle.de/SharedDocs/downloads/DE/publikationen/Expertisen/expertise_benachteiligung_von_trans_personen.pdf?__blob=publicationFile&v=3>.
]Search in Google Scholar
[
Herring, S. C., 2004. Computer-Mediated Discourse Analysis: An Approach to Researching Online Behavior. In S. A. Barab, R. Kling, & J. H. Gray (Eds.), Designing for Virtual Communities in the Service of Learning, Cambridge University Press, pp. 338–376.
]Search in Google Scholar
[
Herring, S. C., & Androutsopoulos, J., 2015. Computer-mediated discourse 2.0. In D. Tannen, H. E. Hamilton, & D. Schiffrin (Eds.), The handbook of discourse analysis (2nd ed.), John Wiley & Sons, pp. 127–151.
]Search in Google Scholar
[
Herring, S. C., & Stoerger, S., 2014. Gender and (A) nonymity in Computer-Mediated Communication. In J. Holmes, M. Meyerhoff, & S. Ehrlich (Eds.), Handbook of Language, Gender, and Sexuality (2nd ed.), John Wiley & Sons, pp. 567–586.
]Search in Google Scholar
[
Jeong, A. C., 2003. The Sequential Analysis of Group Interaction and Critical Thinking in Online Threaded Discussions. The American Journal of Distance Education, 17(1), 25–43.
]Search in Google Scholar
[
Jurkiewicz, D., Borchmann, Ł., Kosmala, I. et al., 2020. ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them. In Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain (Online), pp. 1415–1424.
]Search in Google Scholar
[
Krippendorff, K., 2004. Content Analysis. An Introduction to Its Methodology (2nd ed.). Sage.
]Search in Google Scholar
[
Li, T., Lin, L., Choi, M. et al., 2018. YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles. In iSAI-NLP 2018 Proceedings, IEEE, pp. 1–5.
]Search in Google Scholar
[
Liu, Y., Ott, M., Goyal, N. et al., 2019. RoBERTa: A robustly optimized bert pretraining approach, available at: < https://arxiv.org/pdf/1907.11692.pdf>.
]Search in Google Scholar
[
Macgilchrist, F., 2007. Positive Discourse Analysis: Contesting Dominant Discourses by Reframing the Issues. Critical Approaches to Discourse Analysis Across Disciplines, 1(1), 74–94.
]Search in Google Scholar
[
Madden, A., Ruthven, I., & McMenemy, D., 2013. A classification scheme for content analyses of YouTube video comments. Journal of Documentation, 69(5), 693–714.
]Search in Google Scholar
[
Mathet, Y., Widlöcher, A., & Métivier, J.-P., 2015. The Unified and Holistic Method Gamma (γ) for Inter-Annotator Agreement Measure and Alignment. Computational Linguistics, 41(3), 437–479.
]Search in Google Scholar
[
Maurya, P., Jafari, O., Thatte et al., 2022. Building a comprehensive NER model for Satellite Domain. SN Computer Science, 3(3), 199.
]Search in Google Scholar
[
Mochales, R., & Moens, M.-F., 2011. Argumentation mining. Artificial Intelligence and Law,19, 1–22.
]Search in Google Scholar
[
Morio, G., Morishita, T., Ozaki, H. et al., 2020. Hitachi at SemEval-2020 Task 11: An Empirical Study of Pre-Trained Transformer Family for Propaganda Detection. In Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain (Online), pp. 1739–1748.
]Search in Google Scholar
[
Naim, J., Hossain, T., Tasneem, F. et al., 2022. Leveraging fusion of sequence tagging models for toxic spans detection. Neurocomputing, 50, 688–702.
]Search in Google Scholar
[
Park, J., Katiyar, A., & Yang, B., 2015. Conditional Random Fields for Identifying Appropriate Types of Support for Propositions in Online User Comments. In C. Cardie (Ed.), Proceedings of the 2nd Workshop on Argumentation Mining. 2nd Workshop on Argumentation Mining, Denver, Association for Computational Linguistics, pp. 39–44.
]Search in Google Scholar
[
Peldszus, A., 2017. Automatic recognition of argumentation structure in short monological texts PhD thesis. Institutional Repository of the University of Potsdam, Potsdam, Germany.
]Search in Google Scholar
[
Persing, I., & Ng, V., 2016. End-to-End Argumentation Mining in Student Essays. In Proceedings of NAACLHLT 2016, San Diego, California, pp. 1348–1394.
]Search in Google Scholar
[
Reisigl, M., & Wodak, R., 2009. The Discourse-Historical Approach (DHA). In R. Wodak & M. Meyer (Eds.), Methods of Critical Discourse Analysis, Sage, pp. 87–121.
]Search in Google Scholar
[
Rushton, A., Gray, L., Canty, J. et al., 2019. Review. Beyond Binary: (Re)Defining “Gender” for 21st Century Disaster Risk Reduction Research, Policy, and Practice. International Journal of Environmental Research and Public Health, 16(3984), 1–14.
]Search in Google Scholar
[
Schilt, K., & Westbrook, L., 2009. Doing Gender, Doing Heteronormativity. “Gender Normals,” Transgender People, and the Social Maintenance of Heterosexuality. Gender & Society, 23(4), 440–464.
]Search in Google Scholar
[
Schultes, P., Dorner, V., & Lehner, F., 2013. Leave a Comment! An In-Depth Analysis of User Comments on YouTube. In R. Alt & B. Franczyk (Eds.), Proceedings of the 11th International Conference on Wirtschaftsinformatik (WI2013), pp. 659–674.
]Search in Google Scholar
[
Stab, C., & Gurevych, I., 2017. Parsing Argumentation Structures in Persuasive Essays. Computational Linguistics, 43(3), 619–660.
]Search in Google Scholar
[
Stab, C., Miller, T., Schiller, B. et al., 2018. Cross-topic Argument Mining from Heterogeneous Sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.
]Search in Google Scholar
[
Taylor, E., 2010. Cisgender Privilege: On the Privileges of Performing Normative Gender. In K. Bornstein & S. B. Bergmann (Eds.), Gender outlaws: The next generation, Seal Press, pp. 268–272.
]Search in Google Scholar
[
Thelwall, M., Sud, P., & Vis, F., 2012. Commenting on YouTube Videos: From Guatemalan Rock to El Big Bang. Journal of the American Society for Information Science and Technology, 63(3), 616–629.
]Search in Google Scholar
[
van Dijk, T. A., 1992. Discourse and the denial of racism. Discourse & Society, 3(1), 87–118.
]Search in Google Scholar
[
van Dijk, T. A., 1993a. Analyzing Racism Through Discourse Analysis. Some Methodological Reflections. In J. H. Stanfield & R. M. Dennis (Eds.), Race and Ethnicity in Research Methods, Sage, pp. 92–134.
]Search in Google Scholar
[
van Dijk, T. A., 1993b. Principles of critical discourse analysis. Discourse & Society, 4(2), 249–283.
]Search in Google Scholar
[
van Dijk, T. A., 1995. Discourse, power and access. In C. R. Caldas-Coulthard & M. Coulthard (Eds.), Texts and Practices. Readings in Critical Discourse Analysis, Routledge, pp. 84–104.
]Search in Google Scholar
[
van Dijk, T. A., 2001. Critical Discourse Analysis. In D. Schiffrin, D. Tannen, & H. E. Hamilton (Eds.), The Handbook of Discourse Analysis, Blackwell, pp. 352–371.
]Search in Google Scholar
[
van Dijk, T. A., 2011. Discourse, knowledge, power and politics. Towards critical epistemic discourse analysis. In C. Hart (Ed.), Critical Discourse Studies in Context and Cognition, John Benjamins, pp. 27–63.
]Search in Google Scholar
[
van Dijk, T. A., 2012. A note on epistemics and discourse analysis. British Journal of Social Psychology, 51, 478–485.
]Search in Google Scholar
[
van Leeuwen, T., 2008. Discourse and Practice. New Tools for Critical Discourse Analysis. Oxford University Press.
]Search in Google Scholar
[
Wilce, J. M., 2009. Language and emotion. Cambridge University Press.
]Search in Google Scholar
[
Wodak, R., 2001. What CDA is about ± a summary of its history, important concepts and its developments. In R. Wodak & M. Meyer (Eds.), Methods of Critical Discourse Analysis, Sage, pp. 1–13.
]Search in Google Scholar
[
Worthen, M. G. F., 2016. Hetero-cis-normativity and the gendering of transphobia. International Journal of Transgenderism, 17(1), 31–57.
]Search in Google Scholar
[
Worthen, M. G. F., 2021. Why Can’t You Just Pick One? The Stigmatization of Non-binary/Genderqueer People by Cis and Trans Men and Women: An Empirical Test of Norm-Centered Stigma Theory. Sex Roles, 85, 343–356.
]Search in Google Scholar
[
YouTube. (n.d.). YouTube by the Numbers, available at: < https://blog.youtube/press/>.
]Search in Google Scholar
[
Zhang, A. X., Culbertson, B., & Paritosh, P., 2017. Characterizing Online Discussion Using Coarse Discourse Sequences. In Proceedings of the International AAAI Conference on Web and Social Media. International AAAI Conference on Web and Social Media, Montréal, pp. 357–366.
]Search in Google Scholar
[
Ziegele, M., 2016. Nutzerkommentare als Anschlusskommunikation. Theorie und qualitative Analyse des Diskussionswertes von Online-Nachrichten. Springer VS.
]Search in Google Scholar
[
Zhu, X., Cao, J., Tang, D. et al., 2023. Text as Image: Learning Transferable Adapter for Multi-Label Classification. arXiv preprint arXiv:2312.04160.
]Search in Google Scholar
