Abstract
In recent decades, we have noticed a considerable increase in reports or confession posts of abuse victims on Twitter. Most of the time victims do not report it to their guardians or the concerned authorities. Teenagers and minorities are the most affected group of abuse. Part of these victims tweets about their incident to let go of pain and suffering or as a cry for help. Identifying such reports is challenging, to address such an important task. In this study, we define a new task, tweets reporting abuse classification task (TRACT), and construct a new dataset related to the online abuse reporting. A detailed comparison with existing supervised models and detailed error analysis explores the merit of our proposed model.
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Acknowledgements
We would like to thank anonymous reviewers for helpful comments and suggestions which helped us in improving overall paper quality further. We also like to thank Hostel 5 Bois who helped and supported us in developing this dataset.
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Reddy, S.M., Tyagi, K., Tripathi, A.A., Pawar, A., Kotecha, K. (2021). Tweets Reporting Abuse Classification Task: TRACT. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1335. Springer, Singapore. https://doi.org/10.1007/978-981-33-6984-9_25
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DOI: https://doi.org/10.1007/978-981-33-6984-9_25
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