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Hate speech detection: Challenges and solutions

link bibtex slides doi: 10.1371/journal.pone.0221152 journal article

Authors: Sean MacAvaney, Hao-Ren Yao, Eugene Yang, Katina Russell, Nazli Goharian, Ophir Frieder

Appeared in: PLoS ONE


As online content continues to grow, so does the spread of hate speech. We identify and examine challenges faced by online automatic approaches for hate speech detection in text. Among these difficulties are subtleties in language, differing definitions on what constitutes hate speech, and limitations of data availability for training and testing of these systems. Furthermore, many recent approaches suffer from an interpretability problem---that is, it can be difficult to understand why the systems make the decisions that they do. We propose a multi-view SVM approach that achieves near state-of-the-art performance, while being simpler and producing more easily interpretable decisions than neural methods. We also discuss both technical and practical challenges that remain for this task.

BibTeX @article{macavaney:plosone2019-hate, author = {MacAvaney, Sean and Yao, Hao-Ren and Yang, Eugene and Russell, Katina and Goharian, Nazli and Frieder, Ophir}, title = {Hate speech detection: Challenges and solutions}, year = {2019}, url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221152}, doi = {10.1371/journal.pone.0221152}, journal = {PLoS ONE}, pages = {1--16}, volume = {14} }