Countering hate on social media: Large scale classification of hate and counter speech

TL;DR

This paper develops a large-scale, high-accuracy classifier for hate and counter speech on social media, enabling analysis of their interactions and impact over millions of tweets to understand their role in stabilizing online discourse.

Contribution

It introduces a novel ensemble learning approach with paragraph embeddings for classifying hate and counter speech, validated on a large dataset with high accuracy and human agreement.

Findings

Classifier achieved macro F1 scores from 0.76 to 0.97

Automated detection aligns closely with human judgment

Analysis of 135,000 Twitter conversations reveals insights into hate and counter speech dynamics

Abstract

Hateful rhetoric is plaguing online discourse, fostering extreme societal movements and possibly giving rise to real-world violence. A potential solution to this growing global problem is citizen-generated counter speech where citizens actively engage in hate-filled conversations to attempt to restore civil non-polarized discourse. However, its actual effectiveness in curbing the spread of hatred is unknown and hard to quantify. One major obstacle to researching this question is a lack of large labeled data sets for training automated classifiers to identify counter speech. Here we made use of a unique situation in Germany where self-labeling groups engaged in organized online hate and counter speech. We used an ensemble learning algorithm which pairs a variety of paragraph embeddings with regularized logistic regression functions to classify both hate and counter speech in a corpus of millions of relevant tweets from these two groups. Our pipeline achieved macro F1 scores on out of sample balanced test sets ranging from 0.76 to 0.97---accuracy in line and even exceeding the state of the art. On thousands of tweets, we used crowdsourcing to verify that the judgments made by the classifier are in close alignment with human judgment. We then used the classifier to discover hate and counter speech in more than 135,000 fully-resolved Twitter conversations occurring from 2013 to 2018 and study their frequency and interaction. Altogether, our results highlight the potential of automated methods to evaluate the impact of coordinated counter speech in stabilizing conversations on social media.