Research trends in the control of hate speech on social media for the 2016–2022 time frame

Autores/as

  • Ana M. Sánchez-Sánchez Universidad Pablo de Olavide
  • David Ruiz-Muñoz Junta de Andalucía, Sevilla, España
  • Francisca J. Sánchez-Sánchez Universidad Pablo de Olavide, Sevilla, España

DOI:

https://doi.org/10.7764/cdi.56.60093

Palabras clave:

Hate speech; social media; detection; machine learning; deep learning; natural language processing systems; bibliometric analysis

Resumen

The growth in the number of social media users has resulted in a corresponding rise in the spread of hate speech on these platforms, leading to a growing, but little studied, problem. The bibliometric study aimed to examine the research trend and identify the most productive authors, the most active institutions, the leading countries and the most employed virtual hate speech control mechanisms by analyzing 576 relevant publications from the Scopus database published between 2016-2022. The findings showed an increase in publication and India as a leading country/region in research on virtual hate speech control mechanisms. Deep learning and natural language processing systems were identified as the most commonly used control mechanisms. Based on the results, it is recommended that future researchers focus on multidisciplinary collaboration and valid mechanisms for different languages. This paper provides a general overview of the current state of research in this field and serves as a guide for authors and institutions in their research and collaboration strategies.

Biografía del autor/a

Ana M. Sánchez-Sánchez, Universidad Pablo de Olavide

Ana M. Sánchez-Sánchez, profesora adjunta del departamento de Economía, Métodos Cuantitativos e Historia Económica de la Universidad Pablo de Olavide. Doctora en Administración y Dirección de Empresas por la Universidad Pablo de Olavide. Sus líneas de investigación incluyen indicadores de pobreza, economía del turismo, desarrollo sostenible, comportamiento del turista y turismo laboral. Es autora de publicaciones sobre evaluación de impacto de revistas de economía y turismo. Es miembro del grupo de investigación Estudios Estadísticos y Demoscópicos Multidisciplinares.

David Ruiz-Muñoz, Junta de Andalucía, Sevilla, España

David Ruiz-Muñoz, auditor interno de la Junta de Andalucía. Se ha desempeñado como profesor del departamento de Economía, Métodos Cuantitativos e Historia Económica y del departamento de Economía Financiera y Contabilidad de la Universidad Pablo de Olavide. Doctor en Administración y Dirección de Empresas por la Universidad Pablo de Olavide. Es autor de publicaciones sobre evaluación de impacto de revistas de economía y sociología.

Francisca J. Sánchez-Sánchez, Universidad Pablo de Olavide, Sevilla, España

Francisca J. Sánchez-Sánchez, profesora adjunta del departamento de Economía, Métodos Cuantitativos e Historia Económica de la Universidad Pablo de Olavide. Es 0-doctora en Administración y Dirección de Empresas por la Universidad Pablo de Olavide. Sus líneas de investigación se centran en el estudio de modelos, aplicando la metodología del análisis multivariante y DEA. Dentro de sus colaboraciones se incluyen trabajos aplicados, que le han permitido intervenir en trabajos de diversas temáticas. Es autora de publicaciones sobre evaluación de impacto de revistas de economía y turismo.

Citas

Al-Hassan, A. & Al-Dossari, H. (2022). Detection of hate speech in Arabic tweets using deep learning. Multimedia Systems, 28, 1963–1974. https://doi.org/10.1007/s00530-020-00742-w

Albadi, N., Kurdi, M., & Mishra, S. (2018). Are they Our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 69-76). https://doi.org/10.1109/ASONAM.2018.8508247

Alotaibi, M., Alotaibi, B., & Razaque, A. (2021). A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media. Electronics, 10(21), 2664. https://doi.org/10.3390/electronics10212664

Alrashidi, B., Jamal, A., Khan, I., & Alkhathlan, A. (2022). A review on abusive content automatic detection: approaches, challenges and opportunities. PeerJ Computer Science, 8, e1142. https://doi.org/10.7717/peerj-cs.1142

Badjatiya, P., Gupta, S., Gupta, M., & Varma, V. (2017). Deep Learning for Hate Speech Detection in Tweets. In R. Barret & R. Cummings (Chairs), WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion (pp. 759-760). International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/3041021.3054223

Bailurkar, R. & Raul, N. (2021). Detecting Bots to Distinguish Hate Speech on Social Media. In 2021 12th International Conference on Computing Communication and Networking Technologies (pp. 1-5). IEEE. https://doi.org/10.1109/ICCCNT51525.2021.9579883

Batani, J., Mbunge, E., Muchemwa, B., Gaobotse, G., Gurajena, C., Fashoto, S., Kavu, T., & Dandajena, K. (2022). A Review of Deep Learning Models for Detecting Cyberbullying on Social Media Networks. In R. Silhavy (Ed.), Cybernetics Perspectives in Systems - Proceedings of 11th Computer Science On-line Conference, CSOC 2022 (vol. 3) (pp. 528-550). Springer Science. https://doi.org/10.1007/978-3-031-09073-8_46

Baydogan, C. & Alatas, B. (2021). Metaheuristic Ant Lion and Moth Flame OptimizationBased Novel Approach for Automatic Detection of Hate Speech in Online Social Networks. In IEEE Access, 9, 110047-110062. https://doi.org/10.1109/ACCESS.2021.3102277

Ben-David, A. & Matamoros Fernández, A. (2016). Hate Speech and Covert Discrimination on Social Media: Monitoring the Facebook Pages of Extreme-Right Political Parties in Spain. International Journal of Communication, 10, 1167–1193. https://ijoc.org/index.php/ijoc/article/view/3697

Bohra, A., Vijay, D., Singh, V., Akhtar, S. S., & Shrivastava, M. (2018). A Dataset of HindiEnglish Code-Mixed Social Media Text for Hate Speech Detection. In M. Nissim, V. Patti, B. Plank, C. Wagner (Eds.), Proceedings of the 2nd Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (pp. 36 41). Association for Computational Linguistics. https://aclanthology.org/W18-1105

Bojanowski, P., Grave, E., Joulin, A., & Tomas Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5,135–146. https://doi.org/10.1162/tacl_a_00051

Boulouard, Z., Ouaissa, M., & Ouaissa, M. (2022). Machine Learning for Hate Speech Detection in Arabic Social Media. In M. Ouaissa, Z. Boulouard, M. Ouaissa, B. Guermah, (Eds.), Computational Intelligence in Recent Communication Networks (pp. 147-162). Springer. https://doi.org/10.1007/978-3-030-77185-0_10

Boyack, K. W. & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389-2404. https://doi.org/10.1002/asi.21419

Bhawal, S., Roy, P. K., & Kumar, A. (2021). Hate Speech and Offensive Language Identification on Multilingual Code-Mixed Text Using BERT. CEUR Workshop Proceedings, 3159, 615-624. https://ceur-ws.org/Vol-3159/

Burnap, P. & Williams, M. L. (2016). Us and them: identifying cyber hate on Twitter across multiple protected characteristics. EPJ Data Science, 5, 11. https://doi.org/10.1140/epjds/s13688-016-0072-6

Córdoba-Cely, C., Alpiste, F., Londoño, F., & Monguet, J. (2012). Análisis de cocitación de autor en el modelo de aceptación tecnológico, 2005-2010 (Author Co-citation Analysis of the Technology Acceptance Model, 2005 2010). Revista Española De Documentación Científica, 35(2), 238–261. https://doi.org/10.3989/redc.2012.2.864

Dadvar, M., Trieschnigg, D., Ordelman, R., & De Jong. F. (2015). Improving Cyberbullying Detection With User Context. In P. Serdyukov, P. Braslavski, S. O. Kuznetsov, J. Kamps, S. Rüger, E. Agichtein, I. Segalovich, & E. Yilmaz (Eds.), Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science (vol. 7814) (pp. 693-696). Springer.

Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 512 515. https://doi.org/10.1609/icwsm.v11i1.14955

Del Vigna, F., Cimino, A., Dell'Orletta, F., Petrocchi, M., & Tesconi, M. (2017). Hate me, hate me not: Hate speech detection on Facebook. Proceedings of the First Italian Conference on Cybersecurity, 1816, 86-95.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://doi.org/10.48550/arXiv.1810.04805

Dewani, A., Memon, M. A., & Bhatti, S. (2021). Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data. Journal of Big Data, 8, 160. https://doi.org/10.1186/s40537-021-00550-7

Elouali, A., Elberrichi, Z., & Elouali, N. (2020). Hate Speech Detection on Multilingual Twitter Using Convolutional Neural Networks. Revue d'Intelligence Artificielle, 34, 81-88. https://doi.org/10.18280/ria.340111

European Commission. (2020, June 22). El código de conducta de la UE para la lucha contra la incitación ilegal al odio en Internet (Commission publishes EU Code of Conduct on countering illegal hate speech online continues to deliver results) (press release IP/20/1134). https://ec.europa.eu/commission/presscorner/detail/es/IP_20_1134

ECRI General Policy Recommendation No. 15 on Combating Hate Speech and Explanatory Memorandum of December 8, 2015. Strasbourg, France, March 21, 2016.

Fernández, M., Valbuena, C., & Caro, C. (2015). Evolución del racismo, la xenofobia y otras formas conexas de intolerancia en España (Evolution of racism, xenophobia, and other intolerance-related forms in Spain). Subdirección General de Información Administrativa y Publicaciones. https://inclusion.seg-social.es/oberaxe/es/ publicaciones/documentos/documento_0089.htm

Fersini, E., Nozza, D., & Boifava, G. (2020). Profiling Italian Misogynist: An Empirical Study. In J. Monti, V. Basile, M. P. Di Buono, R. Manna, A. Pascucci, & S. Tonelli (Eds.), Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language (pp. 9-13). ELRA. https://aclanthology.org/volumes/2020.restup-1/

Fortuna, P. & Nunes, S. (2018). A Survey on Automatic Detection of Hate Speech in Text. ACM Computing Surveys (CSUR), 51(4), 1-30. https://doi.org/10.1145/3232676

Galeano, S. (2021). Cuáles son las redes sociales con más usuarios del mundo (2023) (Which are the social networks with the most users in the world? (2023)). Marketing Ecommerce. https://marketing4ecommerce.net/cuales-redes-sociales-con-mas-usuarios-mundo-ranking/

Gascón, A. (2019). La lucha contra el discurso del odio en línea en la Unión Europea y los intermediarios de Internet (Fighting online hate speech in the European Union and Internet intermediaries). In Z. Combalía, M. P. Diago, & A. González-Varas (Coords.), Libertad de expresión y discurso de odio por motivos religiosos (Freedom of speech and religiously motivated hate speech) (pp. 64-86). Ediciones del Licregdi.

Giumetti, G.W., Robin, M., & Kowalski, R.M. (2022). Cyberbullying via social media and wellbeing. Current Opinion in Psychology, 45, 101314. https://doi.org/10.1016/j.copsyc.2022.101314

Glänzel, W. & Schubert, A. (2004). Analysing Scientific Networks Through Co-Authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of Quantitative Science and Technology Research. Springer. https://doi.org/10.1007/1-4020-2755-9_12

Gangurde, A., Mankar, P., Chaudhari, D., & Pawar, A. (2022). A Systematic Bibliometric Analysis of Hate Speech Detection on Social Media Sites? Journal of Scientometric Research, 11(1), 100-111.

Gongane, V. U., Munot, M. V., & Anuse, A. D. (2022). Detection and moderation of detrimental content on social media platforms: Current status and future directions. Social Network Analysis and Mining, 12, 129. https://doi.org/10.1007/s13278-022-00951-3

Hedderich, M. A., Lange, L., Adel, H., Strötgen, J., Klakow, D. (2021). A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 2545 2568). https://doi.org/10.48550/arXiv.2010.12309

Kanan, T., Aldaaja, A., Hawashin, B. (2020). Cyber-bullying and cyber-harassment detection using supervised machine learning techniques in Arabic social media contents. The Journal of Internet Technology, 21(5),1409 1421. https://jit.ndhu.edu.tw/article/view/2376

Li, C. T., Ku, L. W., Tsai, Y. C., & Wang, W. Y. (2022). SocialNLP'22: 10th international workshop on natural language processing for social media. In F. Laforest, R. Troncy, L. Médini, & I. Herman (Eds.), Companion Proceedings of the Web Conference 2022 (pp. 849-851). ACM. https://doi.org/10.1145/3487553.3524876

Liyanage, O. & Jayakumar, K. (2021). Hate Speech Detection in Sinhala-English Code-Mixed Language. In Proceedings of the 21st International Conference on Advances in ICT for Emerging Regions, ICter (pp. 225-230). IEEE. https://doi.org/10.1109/ICter53630.2021.9774816

MacAvaney, S., Yao, H. R., Yang, E., Russell, K., Goharian, N., & Frieder, O. (2019). Hate speech detection: Challenges and solutions. PLoS ONE, 14(8). https://doi.org/10.1371/journal.pone.0221152

Mandl, T., Modha S., Kumar M, A., & Chakravarthi, B.J. (2020). Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German. In FIRE '20: Proceedings of the 12th Annual Meeting of the Forum for Information Retrieval Evaluation (pp. 29-32). ACM. https://doi.org/10.1145/3441501.3441517

Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado-López, E. (2021). Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: A multidisciplinary comparison of coverage via citations. Scientometrics, 126, 871-906. https://doi.org/10.1007/s11192-020-03690-4

Mishra, R. (2021). Are We Doing Enough? A Bibliometric Analysis of Hate Speech Research in the Selected Database of Scopus. Library Philosophy and Practice, 5140. https://digitalcommons.unl.edu/libphilprac/5140/

Modha, S., Majumder, P., & Mandl, T. (2022). An empirical evaluation of text representation schemes to filter the social media stream. Journal of Experimental and Theoretical Artificial Intelligence, 34(3), 499-525. https://doi.org/10.1080/0952813X.2021.1907792

Modha, S., Majumder, P., Mandl, T., & Mandalia, C. (2020). Detecting and visualizing hate speech in social media: A cyber watchdog for surveillance. Expert Systems with Applications, 161, 113725. https://doi.org/10.1016/j.eswa.2020.113725

Mondal, M., Araújo Silva, L., & Benevenuto, F. (2017). A measurement study of hate speech in social media. In P. Dolog & P. Vojtas (Chairs), HT '17: Proceedings of the 28th ACM Conference on Hypertext and Social Media (pp. 85-94). ACM. https://doi.org/10.1145/3078714.3078723

Movement Against Intolerance. (2015). Informe Raxen. Racismo, Xenofobia, Antisemitismo, Islamofobia, Neofascismo, Homofobia y otras manifestaciones relacionadas de Intolerancia a través de los hechos. Especial Acción Jurídica contra el Racismo y los Crímenes de Odio. https://inclusion.seg-social.es/oberaxe/es/publicaciones/documentos/documento_0013.htm

Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020). A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media. In H. Cherifi, S. Gaito, J. Mendes, E. Moro, & L. Rocha (Eds.), Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence (vol. 881) (pp. 928-940). Springer. https://doi.org/10.1007/978-3-030-36687-2_77

Mutanga, R. T, Naicker, N, & Olugbara, O. O. (2022). Detecting Hate Speech on Twitter Network using Ensemble Machine Learning. International Journal of Advanced Computer Science and Applications, 13(3). https://doi.org/10.14569/IJACSA.2022.0130341

Nandiyanto, A. B. D., Biddinika, M. K., & Triawan, F. (2020). How bibliographic dataset portrays decreasing number of scientific publication from Indonesia. Indonesian Journal of Science and Technology, 5(1), 154-175. https://doi.org/10.17509/ijost.v5i1.22265

Nobata, C., Tetreault, J., Thomas, A., Mehdad, Y., & Chang, Y. (2016). Abusive Language Detection in Online User Content. In J. Bourdeau, J. A. Hendler, & R. Nkambou (Chairs), WWW '16: Proceedings of the 25th International Conference on World Wide Web (pp. 145-153). International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/2872427.2883062

Omar, A. & Hashem, M. E. (2022). An Evaluation of the Automatic Detection of Hate Speech in Social Media Networks. International Journal of Advanced Computer Science and Applications (IJACSA), 13(2). https://doi.org/10.14569/IJACSA.2022.0130228

Page, M. J., MacKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffman, T. C., Mulrow, C. D., Shamseer, L., Telzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A. Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinnes…, & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372(71). https://doi.org/10.1136/bmj.n71

Pitsilis, G. K., Ramampiaro, H., & Langseth, H. (2018). Effective hate-speech detection in twitter data using recurrent neural networks. Applied Intelligence, 48, 4730-4742. https://doi.org/10.1007/s10489-018-1242-y

Putri, T. T. A., Sriadhi, S., Sari, R. D., Rahmadani, R., & Hutahaean, H. D. (2020). A comparison of classification algorithms for hate speech detection. IOP Conference Series: Materials Science and Engineering, 830(3). https://doi.org/10.1088/1757-899X/830/3/032006

Ramírez-García, A., González-Molina, A., Gutiérrez-Arenas, M., & Moyano-Pacheco, M. (2022). Interdisciplinariedad de la producción científica sobre el discurso del odio y las redes sociales: Un análisis bibliométrico (Interdisciplinarity of scientific production on hate speech and social media: A bibliometric analysis). Comunicar, 72, 129-140. https://doi.org/10.3916/C72-2022-10

Risch, J. & Krestel, R. (2018). Aggression Identification Using Deep Learning and Data Augmentation. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018) (pp. 150-158). Association for Computational Linguistics. https://aclanthology.org/W18-4418/

Romero, L. & Portillo-Salido, E. (2019). Trends in Sigma-1 Receptor Research: A 25-year Bibliometric Analysis. Frontiers in Pharmacology, 10. https://doi.org/10.3389/fphar.2019.00564

Roy, P. K., Bhawal, S., & Subalalitha, Ch. N. (2022). Hate speech and offensive language detection in Dravidian languages using deep ensemble framework. Computer Speech & Language, 75,101386. https://doi.org/10.1016/j.csl.2022.101386

Saeed, F., Al-Sarem, M., & Alromema, W. (2021). Tuning Hyper-Parameters of Machine Learning Methods for Improving the Detection of Hate Speech. In F. Saeed, T. Al-Hadhrami, F. Mohammed, & E. Mohammed (Eds.), Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing (vol. 1188) (pp. 71-78). Springer. https://doi.org/10.1007/978-981-15-6048-4_7

Salim, C. E. R. & Suhartono, D. (2021). Long Short-Term Memory for Hate Speech and Abusive Language Detection on Indonesian Youtube Comment Section. In H. Lin (Ed.), Proceedings of the 2021 11th International Workshop on Computer Science and Engineering (pp. 193-200). https://doi.org/10.18178/wcse.2021.06.029

Satapara, S., Modha, S., Mandl, T., Madhu, H., & Majumder, P. (2021). Overview Of the HASOC Subtrack At FIRE 2021: Conversational Hate Speech Detection in Code-Mixed Language. In P. Mehta, T. Mandl, P. Majumder, & M. Mitra (Eds.), FIRE-WN 2021: FIRE 2021 working notes (pp. 20-31). RWTH.

Schmidt, A. & Wiegand, A. (2017). A Survey on Hate Speech Detection using Natural Language Processing. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media (pp. 1–10). Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-1101

Sellars, A. F., (2016). Defining Hate Speech. Public Law Research, 16-48. https://doi.org/10.2139/ssrn.2882244

Silva, L., Mondal, M., Correa, D., Benevenuto, F., & Weber, I. (2016). Analyzing the Targets of Hate in Online Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 10(1), 687-690. https://doi.org/10.1609/icwsm.v10i1.14811

Sindhu, A., Sarang, S., Zahid, H. K, Zafar, A., Sajid, K. & Ghulam, M. (2020). Automatic Hate Speech Detection using Machine Learning: A Comparative Study. International Journal of Advanced Computer Science and Applications (IJACSA), 11(8). https://doi.org/10.14569/IJACSA.2020.0110861

Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayer, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126, 5113-5142. https://doi.org/10.1007/s11192-021-03948-5

Strossen, N. (2016). Freedom of Speech and Equality: Do We Have to Choose? Journal of Law and Policy, 25(1), 185–225.

Tontodimamma, A., Nissi, E., Sarra, A., & Fontanella, L. (2021). Thirty years of research into hate speech: Topics of interest and their evolution. Scientometrics, 126, 157-179. https://doi.org/10.1007/s11192-020-03737-6

UNESCO. (2021). Addressing Hate Speech on Social Media: Contemporary Challenges. https://unesdoc.unesco.org/ark:/48223/pf0000379177

United Nations. (2019). UN Strategy and Plan of Action on Hate Speech. https://www.un.org/en/hate-speech

Van Eck, N. J. & Waltman, L. (2020). VOSviewer Manual. Universiteit Leiden. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.8.pdf

Vargas-Quesada, B., Chinchilla-Rodríguez, Z., & Rodríguez, N. (2017). Identification and Visualization of the Intellectual Structure in Graphene Research. Frontiers in Research Metrics and Analytics, 2. https://doi.org/10.3389/frma.2017.00007

Waseem, Z. (2016). Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter. In Proceedings of the First Workshop on NLP and Computational Social Science (pp. 138-142). Association for Computational Linguistics.

Waseem, Z. & Hovy, D. (2016). Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. In Proceedings of the NAACL Student Research Workshop (pp. 88–93). Association for Computational Linguistics.

Watanabe, H., Bouazizi, M., & Ohtsuki, T. (2018). Hate Speech on Twitter A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection. IEEE Access, 6, 13825-13835. https://doi.org/10.1109/ACCESS.2018.2806394

Xiang, K., Zhang, Z., Yu, Y., San Lucas, L., Amin, M. R., & Li, Y. (2021). Identification of Hate Tweets: Which Words Matter the Most? In C. Stephanidis, M. Kurosu, J. Y. C. Chen, G. Fragomeni, N. Streitz, S. Konomi, H. Degen, & S. Ntoa (Eds.), HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence (pp. 586-598). Springer. https://doi.org/10.1007/978-3-030-90963-5_44

Zamora-Bonilla, J. & González de Prado Salas, J. (2014). Un análisis inferencialista de la coautoría de artículos científicos (an inferentialist conception regarding the co-authorship of scientific papers). Revista Española de Documentación Científica, 37(4), e064. https://doi.org/10.3989/redc.2014.4.1145

Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019). Predicting the type and target of offensive posts in social media. In Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) (pp. 1415-1420). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.1902.09666

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2023-09-28

Cómo citar

Sánchez-Sánchez, A. M., Ruiz-Muñoz, D., & Sánchez-Sánchez, F. J. (2023). Research trends in the control of hate speech on social media for the 2016–2022 time frame. Cuadernos.Info, (56), 89–116. https://doi.org/10.7764/cdi.56.60093