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Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts

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Book cover Combating Online Hostile Posts in Regional Languages during Emergency Situation (CONSTRAINT 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1402))

Abstract

Fake news, hostility, defamation are some of the biggest problems faced in social media. We present the findings of the shared tasks (https://constraint-shared-task-2021.github.io/) conducted at the CONSTRAINT Workshop at AAAI 2021. The shared tasks are ‘COVID19 Fake News Detection in English’ and ‘Hostile Post Detection in Hindi’. The tasks attracted 166 and 44 team submissions respectively. The most successful models were BERT or its variations.

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Notes

  1. 1.

    https://hindi.boomlive.in/fake-news.

  2. 2.

    https://www.bhaskar.com/no-fake-news/.

  3. 3.

    https://developer.twitter.com/en/docs/twitter-api.

  4. 4.

    https://huggingface.co/bert-base-multilingual-uncased.

  5. 5.

    Results for all the teams is available at https://competitions.codalab.org/competitions/26655#learn_the_details-result.

  6. 6.

    Results for all the teams is available at https://competitions.codalab.org/competitions/26654#learn_the_details-submission-details.

References

  1. A brief history of fake news. https://www.cits.ucsb.edu/fake-news/brief-history

  2. Fake news alert. https://www.who.int/india/emergencies/coronavirus-disease-(covid-19)/fake-news-alert

  3. How is ‘fake news’ defined, and when will it be added to the dictionary?. https://www.merriam-webster.com/words-at-play/the-real-story-of-fake-news

  4. Akiwowo, S., et al. (eds.): Proceedings of the Fourth Workshop on Online Abuse and Harms. Association for Computational Linguistics (2020)

    Google Scholar 

  5. Arampatzis, A., et al. (eds.): 11th International Conference of the CLEF Association (CLEF 2020). LNCS (2020)

    Google Scholar 

  6. Aroyehun, S.T., Gelbukh, A.: Aggression detection in social media: using deep neural networks, data augmentation, and pseudo labeling. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018) (2018)

    Google Scholar 

  7. Azhan, M., Ahmad, M.: LaDiff ULMFiT: a layer differentiated training approach for ULMFiT. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 54–61, Springer, Cham (2021)

    Google Scholar 

  8. Bang, Y., et al.: Model generalization on COVID-19 fake news detection. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 128–140, Springer, Cham (2021)

    Google Scholar 

  9. Baris, I., Boukhers, Z.: ECOL: early detection of COVID lies using content, prior knowledge and source information. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 141–152, Springer, Cham (2021)

    Google Scholar 

  10. Beran, T., Li, Q.: Cyber-harassment: a study of a new method for an old behavior. JECR 32(3), 265 (2005)

    Google Scholar 

  11. Bhardwaj, M., et al.: Hostility detection dataset in Hindi (2020)

    Google Scholar 

  12. Bhatnagar, V., et al.: Divide and conquer: an ensemble approach for hostile post detection in Hindi. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 244–255, Springer, Cham (2021)

    Google Scholar 

  13. Chakravarthi, B.R., et al.: Findings of the shared task on offensive language identification in Tamil, Malayalam, and Kannada. In: Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages (2021)

    Google Scholar 

  14. Chen, B., et al.: Transformer-based language model fine-tuning methods for COVID-19 fake news detection. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 83–92, Springer, Cham (2021)

    Google Scholar 

  15. Cheng, Y., Chen, Z.F.: The influence of presumed fake news influence: examining public support for corporate corrective response, media literacy interventions, and governmental regulation. Mass Commun. Soc. 23(5), 705–729 (2020)

    Article  Google Scholar 

  16. Claire Wardle, H.D.: Information disorder: toward an interdisciplinary framework for research and policy making (2017). https://tverezo.info/wp-content/uploads/2017/11/PREMS-162317-GBR-2018-Report-desinformation-A4-BAT.pdf

  17. Cui, L., Lee, D.: CoAID: COVID-19 healthcare misinformation dataset (2020)

    Google Scholar 

  18. Das, S.D., Basak, A., Dutta, S.: A heuristic-driven ensemble framework for COVID-19 fake news detection. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 164–176, Springer, Cham (2021)

    Google Scholar 

  19. Davidson, T., et al.: Automated hate speech detection and the problem of offensive language. In: Proceedings of ICWSM (2017)

    Google Scholar 

  20. De, A., et al.: Coarse and fine-grained hostility detection in Hindi posts using fine tuned multilingual embeddings. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 201–212, Springer, Cham (2021)

    Google Scholar 

  21. Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding (2019)

    Google Scholar 

  22. Dolhansky, B., et al.: The deepfake detection challenge (DFDC) dataset (2020)

    Google Scholar 

  23. Felber, T.: Constraint 2021: machine learning models for COVID-19 fake news detection shared task (2021)

    Google Scholar 

  24. Fišer, D., et al. (eds.): Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) (2018)

    Google Scholar 

  25. Glazkova, A., Glazkov, M., Trifonov, T.: g2tmn at constraint@AAAI2021: exploiting CT-BERT and ensembling learning for COVID-19 fake news detection. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 116–127, Springer, Cham (2021)

    Google Scholar 

  26. Gundapu, S., Mamidi, R.: Transformer based automatic COVID-19 fake news detection system (2021)

    Google Scholar 

  27. Gupta, A., et al.: Hostility detection and COVID-19 fake news detection in social media (2021)

    Google Scholar 

  28. Gururangan, S., et al.: Don’t stop pretraining: adapt language models to domains and tasks (2020)

    Google Scholar 

  29. Holone, H.: The filter bubble and its effect on online personal health information. Croatian Med. J. 57, 298 (2016)

    Article  Google Scholar 

  30. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification (2018)

    Google Scholar 

  31. Humprecht, E., Hellmueller, L., Lischka, J.A.: Hostile emotions in news comments: a cross-national analysis of Facebook discussions. Soc. Media+ Soc. 6(1), 2056305120912481 (2020)

    Google Scholar 

  32. Jha, V.K., et al.: DHOT-repository and classification of offensive tweets in the Hindi language. Procedia Comput. Sci. 171, 2324–2333 (2020)

    Article  Google Scholar 

  33. Joshi, R., Karnavat, R., Jirapure, K., Joshi, R.: Evaluation of deep learning models for hostility detection in Hindi text (2021)

    Google Scholar 

  34. Kakwani, D., et al.: IndicNLPSuite: monolingual corpora. In: Findings of EMNLP, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages (2020)

    Google Scholar 

  35. Kamal, O., Kumar, A., Vaidhya, T.: Hostility detection in Hindi leveraging pre-trained language models. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 213–223, Springer, Cham (2021)

    Google Scholar 

  36. Kar, D., Bhardwaj, M., Samanta, S., Azad, A.P.: No rumours please! A multi-indic-lingual approach for COVID fake-tweet detection. arXiv:2010.06906 (2020)

  37. Keelery, S.: Social media users in India, October 2020. https://www.statista.com/statistics/278407/number-of-social-network-users-in-india/

  38. Koloski, B., Stepišnik-Perdih, T., Škrlj, B.: Identification of COVID-19 related fake news via neural stacking. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 177–188, Springer, Cham (2021)

    Google Scholar 

  39. Kumar, R., et al.: Benchmarking aggression identification in social media. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018) (2018)

    Google Scholar 

  40. Kumar, R., et al.: Evaluating aggression identification in social media. In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (2020)

    Google Scholar 

  41. Leite, J.A., et al.: Toxic language detection in social media for Brazilian Portuguese: new dataset and multilingual analysis (2020)

    Google Scholar 

  42. Li, X., et al.: Exploring text-transformers in AAAI 2021 shared task: COVID-19 fake news detection in English. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 106–115, Springer, Cham (2021)

    Google Scholar 

  43. Liu, R., Zhou, X.: Extracting latent information from datasets in the constraint-2020 shared task on the hostile post detection. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 62–73, Springer, Cham (2021)

    Google Scholar 

  44. Liu, Y., et al.: RoBERTa: arobustly optimized BERT pretraining approach (2019)

    Google Scholar 

  45. Martens, D., Maalej, W.: Towards understanding and detecting fake reviews in app stores. Empirical Softw. Eng. 24(6), 3316–3355 (2019)

    Article  Google Scholar 

  46. Mathew, B., et al.: HateXplain: a benchmark dataset for explainable hate speech detection (2020)

    Google Scholar 

  47. Mollas, I., et al.: Ethos: an online hate speech detection dataset (2020)

    Google Scholar 

  48. Müller, M., Salathé, M., Kummervold, P.E.: COVID-Twitter-BERT: a natural language processing model to analyse COVID-19 content on Twitter (2020)

    Google Scholar 

  49. Nakamura, K., Levy, S., Wang, W.Y.: r/Fakeddit: a new multimodal benchmark dataset for fine-grained fake news detection (2020)

    Google Scholar 

  50. Nikhil, N., et al.: LSTMs with attention for aggression detection. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (2018)

    Google Scholar 

  51. Patwa, P., et al.: Fighting an infodemic: COVID-19 fake news dataset. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 21–29, Springer, Cham (2021)

    Google Scholar 

  52. Raha, T., et al.: Identifying COVID-19 fake news in social media (2021)

    Google Scholar 

  53. Raha, T., et al.: Task adaptive pretraining of transformers for hostility detection. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 236–243, Springer, Cham (2021)

    Google Scholar 

  54. Risch, J., Krestel, R.: Bagging BERT models for robust aggression identification. In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (2020)

    Google Scholar 

  55. Roberts, S.T., et al. (eds.): Proceedings of the Third Workshop on Abusive Language Online. Association for Computational Linguistics (2019)

    Google Scholar 

  56. Rose, J.: To believe or not to believe: an epistemic exploration of fake news, truth, and the limits of knowing. Postdigital Sci. Educ. 2, 202–216 (2020)

    Article  Google Scholar 

  57. Rowe, I.: Deliberation 2.0: comparing the deliberative quality of online news user comments across platforms. J. Broadcast. Electron. Media 59(4), 539–555 (2015)

    Article  Google Scholar 

  58. Safi Samghabadi, N., Patwa, P., PYKL, S., Mukherjee, P., Das, A., Solorio, T.: Aggression and misogyny detection using BERT: a multi-task approach. In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (2020)

    Google Scholar 

  59. Saha, P., Mathew, B., Goyal, P., Mukherjee, A.: Hateminers: detecting hate speech against women (2018)

    Google Scholar 

  60. Sai, S., et al.: Stacked embeddings and multiple fine-tuned XLM-roBERTa models for enhanced hostility identification. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 224–235, Springer, Cham (2021)

    Google Scholar 

  61. Sarthak, Shukla, S., Mittal, G., Arya, K.V.: Detecting hostile posts using relational graph convolutional network (2021)

    Google Scholar 

  62. Sharif, O., Hossain, E., Hoque, M.M.: Combating hostility: COVID-19 fake news and hostile post detection in social media (2021)

    Google Scholar 

  63. Shekhar, C., et al.: Walk in wild: an ensemble approach for hostility detection in Hindi posts (2021)

    Google Scholar 

  64. Shifath, S.M.S.U.R., Khan, M.F., Islam, M.S.: A transformer based approach for fighting COVID-19 fake news (2021)

    Google Scholar 

  65. Shu, K., et al.: Fakenewsnet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media (2019)

    Google Scholar 

  66. Shushkevich, E., Cardiff, J.: TUDublin team at constraint@AAAI2021 - COVID19 fake news detection (2021)

    Google Scholar 

  67. Gautam, A., Masud, S.: Fake news detection system using XLNet model with topic distributions: constraint@AAAI2021 shared task. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 189–200, Springer, Cham (2021)

    Google Scholar 

  68. Vallone, R., Ross, L., Lepper, M.: The hostile media phenomenon: biased perception and perceptions of media bias in coverage of the Beirut massacre. J. Pers. Soc. Psychol. 49(3), 577–85 (1985)

    Article  Google Scholar 

  69. Vijjali, R., Potluri, P., Kumar, S., Teki, S.: Two stage transformer model for COVID-19 fake news detection and fact checking (2020)

    Google Scholar 

  70. Wani, A., et al.: Evaluating deep learning approaches for COVID19 fake news detection. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 153–163, Springer, Cham (2021)

    Google Scholar 

  71. Waseem, Z., Chung, W.H.K., Hovy, D., Tetreault, J. (eds.): Proceedings of the First Workshop on Abusive Language Online. Association for Computational Linguistics (2017)

    Google Scholar 

  72. Wendling, M.: The (almost) complete history of ‘fake news’, January 2018. https://www.bbc.com/news/blogs-trending-42724320

  73. Zhou, S., Fu, R., Li., J.: Fake news and hostile post detection using an ensemble learning model. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 74–82, Springer, Cham (2021)

    Google Scholar 

  74. Zutshi, A., Raj, A.: Tackling the infodemic : analysis using transformer based model. In: Chakraborty, T., Shu, K., Bernard, R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021, CCIS 1402, pp. 93–105, Springer, Cham (2021)

    Google Scholar 

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Authors and Affiliations

  1. IIIT Sri City, Sri City, India

    Parth Patwa & Srinivas PYKL

  2. IIIT Delhi, Delhi, India

    Mohit Bhardwaj, Shivam Sharma, Md Shad Akhtar & Tanmoy Chakraborty

  3. IIT Patna, Patna, India

    Gitanjali Kumari & Asif Ekbal

  4. Wipro Research, Bangalore, India

    Vineeth Guptha, Shivam Sharma & Amitava Das

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  1. Parth Patwa
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Editors and Affiliations

  1. IIIT Delhi, New Delhi, India

    Tanmoy Chakraborty

  2. Illinois Institute of Technology, Chicago, IL, USA

    Kai Shu

  3. Arizona State University, Tempe, AZ, USA

    H. Russell Bernard

  4. Arizona State University, Tempe, AZ, USA

    Huan Liu

  5. IIIT Delhi, New Delhi, India

    Md Shad Akhtar

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Patwa, P. et al. (2021). Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts. In: Chakraborty, T., Shu, K., Bernard, H.R., Liu, H., Akhtar, M.S. (eds) Combating Online Hostile Posts in Regional Languages during Emergency Situation. CONSTRAINT 2021. Communications in Computer and Information Science, vol 1402. Springer, Cham. https://doi.org/10.1007/978-3-030-73696-5_5

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