← smac.pub home

Hate speech detection: Challenges and solutions

link bibtex slides 541 citations journal article

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

Appeared in: PLoS ONE

DOI 10.1371/journal.pone.0221152 Google Scholar 7wWfoDgAAAAJ:LkGwnXOMwfcC Semantic Scholar 0f270b805737b12420311eb70172b2f89fd4b159 smac.pub plosone2019-hate


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} }