Research trends in the control of hate speech on social media for the 2016–2022 time frame
DOI:
https://doi.org/10.7764/cdi.56.60093Palabras clave:
Hate speech; social media; detection; machine learning; deep learning; natural language processing systems; bibliometric analysisResumen
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.
Descargas
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
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2023 Cuadernos.info
Esta obra está bajo una licencia internacional Creative Commons Atribución-CompartirIgual 4.0.