GO BACK TO /R/CONSPIRACY: AN EXPLORATION OF METHODS FOR THE AUTOMATIC DETECTION OF AFFECTIVE POLARIZATION ON REDDIT

Abstract

Affective polarization – the tendency to hold negative attitudes towards an out-group and biased, positive attitudes towards an in-group – is a hot topic in research and public debate. There are concerns that news media’s tendency to focus on political conflict rather than issues is causing polarization to increase, but researchers lack methods to automatically asses levels of polarization in online debates and correlate them with news articles. This study examines the appropriateness of using Reddit Karma, word embeddings and existing NLP tools for automatic detection of affective polarization in discussions on Reddit. To achieve this, we collect and manually annotate Reddit discussions for expressions of affective polarization and fit multiple logistic regression models on the discussion features and metadata. We find a strong correlation between the probability to encounter expressions of affective polarization in the data and both word embeddings and the confidence scores of toxicity detection. We also find that patterns in the comment votes are good predictors of disagreement in the discussions. Moreover, we present a data set of Reddit-discussions about topics related to the covid-19 pandemic which can be used in further attempts to automatically detect affective polarization in interactive discourse on social media.