Abstract
Online harassment is an important problem of modern societies, usually mitigated by the manual work of website moderators, often supported by machine learning tools. The vast majority of previously developed methods enable only retrospective detection of online abuse, e.g., by automatic hate speech detection. Such methods fail to fully protect users as the potential harm related to the abuse has always to be inflicted. The recently proposed proactive approaches that allow detecting derailing online conversations can help the moderators to prevent conversation breakdown. However, they do not predict the time left to the breakdown, which hinders the practical possibility of prioritizing moderators’ works. In this work, we propose a new method based on deep neural networks that both predict the possibility of conversation breakdown and the time left to conversation derailment. We also introduce three specialized loss functions and propose appropriate metrics. The conducted experiments demonstrate that the method, besides providing additional valuable time information, also improves on the standard breakdown classification task with respect to the current state-of-the-art method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, S., Awekar, A.: Deep learning for detecting cyberbullying across multiple social media platforms. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 141–153. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76941-7_11
Beran, T., Li, Q.: Cyber-harassment: a study of a new method for an old behavior. J. Educ. Comput. Res. 32(3), 265 (2005)
Carton, S., Mei, Q., Resnick, P.: Extractive adversarial networks: high-recall explanations for identifying personal attacks in social media posts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2018)
Chang, J.P., Danescu-Niculescu-Mizil, C.: Trouble on the horizon: forecasting the derailment of online conversations as they develop. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2019)
Cheng, J., Danescu-Niculescu-Mizil, C., Leskovec, J.: Antisocial behavior in online discussion communities. In: Proceedings of ICWSM (2015)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2014)
Dachraoui, A., Bondu, A., Cornuéjols, A.: Early classification of time series as a non myopic sequential decision making problem. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9284, pp. 433–447. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23528-8_27
Duggan, M.: Online harassment 2017. Pew Research Center (2017)
Hsu, C.F., Khabiri, E., Caverlee, J.: Ranking comments on the social web. In: 2009 International Conference on Computational Science and Engineering, vol. 4, pp. 90–97. IEEE (2009)
Hua, Y., Danescu-Niculescu-Mizil, C., Taraborelli, D., Thain, N., Sorensen, J., Dixon, L.: WikiConv: a corpus of the complete conversational history of a large online collaborative community. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, pp. 2818–2823. Association for Computational Linguistics (2018)
Janiszewski, P., Skiba, M., Walińska, U.: PUM at SemEval-2020 task 12: aggregation of transformer-based models’ features for offensive language recognition. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval) (2020)
Lango, M.: Tackling the problem of class imbalance in multi-class sentiment classification: an experimental study. Found. Comput. Decis. Sci. 44, 151–178 (2019)
Liu, P., Guberman, J., Hemphill, L., Culotta, A.: Forecasting the presence and intensity of hostility on Instagram using linguistic and social features. In: Proceedings of AAAI Conference on Web and Social Media (ICWSM) (2018)
Malmasi, S., Zampieri, M.: Detecting hate speech in social media. In: Proceedings of the International Conference Recent Advances in Natural Language Processing (2017)
Mathew, B., et al.: Thou shalt not hate: countering online hate speech. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 369–380 (2019)
Mishra, P., Yannakoudakis, H., Shutova, E.: Tackling online abuse: a survey of automated abuse detection methods, CoRR (2019)
Munger, K.: Tweetment effects on the tweeted: experimentally reducing racist harassment. Polit. Behav. 39(3), 629–649 (2017)
Munro, E.R.: The protection of children online: a brief scoping review to identify vulnerable groups. Childhood Wellbeing Research Centre (2011)
Noever, D.: Machine learning suites for online toxicity detection. arXiv preprint arXiv:1810.01869 (2018)
Prabhumoye, S., Tsvetkov, Y., Salakhutdinov, R., Black, A.W.: Style transfer through back-translation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2018)
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. In: Proceedings of the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing (co-located with NeurIPS) (2019)
Santos, C.N.d., Melnyk, I., Padhi, I.: Fighting offensive language on social media with unsupervised text style transfer. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (2018)
Schieb, C., Preuss, M.: Governing hate speech by means of counterspeech on facebook. In: 66th ICA Annual Conference, pp. 1–23 (2016)
Snyder, P., Doerfler, P., Kanich, C., McCoy, D.: Fifteen minutes of unwanted fame: detecting and characterizing doxing. In: Proceedings of the 2017 Internet Measurement Conference, pp. 432–444 (2017)
Sordoni, A., Bengio, Y., Vahabi, H., Lioma, C., Simonsen, J.G., Nie, J.Y.: A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (2015)
Stoop, W., Kunneman, F., van den Bosch, A., Miller, B.: Detecting harassment in real-time as conversations develop. In: Proceedings of the Third Workshop on Abusive Language Online, Florence, Italy, pp. 19–24. Association for Computational Linguistics, August 2019. https://doi.org/10.18653/v1/W19-3503
Wulczyn, E., Taraborelli, D., Thain, N., Dixon, L.: Ex machina: personal attacks seen at scale. In: International World Wide Web Conference (2017)
Zhang, J., Chang, J.P., Danescu-Niculescu-Mizil, C., Lucas Dixon, Y.H., Thain, N., Taraborelli, D.: Conversations gone awry: Detecting early signs of conversational failure. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (2018)
Zhao, R., Zhou, A., Mao, K.: Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th International Conference on Distributed Computing and Networking. ICDCN 2016, Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2833312.2849567
Acknowledgements
Mateusz Lango was supported by the Polish National Science Centre under grant No. 2016/22/E/ST6/00299. Moreover, the research of Jerzy Stefanowski was partially supported by the Polish Ministry of Education and Science, grant no. 0311/SBAD/0709. The authors also acknowledge the support from Google Cloud Platform research grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Janiszewski, P., Lango, M., Stefanowski, J. (2021). Time Aspect in Making an Actionable Prediction of a Conversation Breakdown. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12979. Springer, Cham. https://doi.org/10.1007/978-3-030-86517-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-86517-7_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86516-0
Online ISBN: 978-3-030-86517-7
eBook Packages: Computer ScienceComputer Science (R0)
