Understanding the prevalence and impact of personal attacks in online discussions is challenging. A method that combines crowdsourcing and machine learning provides a way forward, but caveats must be considered.
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References
Wulczyn, E., Thain, N. & Dixon, L. Proc. 2017 World Wide Web Conf., 1391–1399 (Int. World Wide Web Conf. Committee, 2017). go.nature.com/2pvpizu
Manning, C. D., Raghavan, P. & Schütze, H. Introduction to Information Retrieval (Cambridge Univ. Press, 2008).
Shermis, M. D. & Burstein, J. C. Automated Essay Scoring: A Cross-disciplinary Perspective (Taylor & Francis, 2003).
Pang, B. & Lee, L. Opinion Mining and Sentiment Analysis (Now Publishers, 2008).
Mayfield, E. & Rosé, C. P. in Handbook of Automated Essay Grading (eds Shermis, M. D. & Burnstein, J.) 124–135 (Routledge, 2013).
Nguyen, D., Doğruöz, A. S., Rosé, C. P. & de Jong, F. M. G. Comput. Linguist. 42, 537–593 (2016).
Shermis, M. D. & Burstein, J. Handbook of Automated Essay Evaluation: Current Applications and New Directions (Routledge, 2013).
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Rosé, C. A social spin on language analysis. Nature 545, 166–167 (2017). https://doi.org/10.1038/545166a
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DOI: https://doi.org/10.1038/545166a
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