Detecting Polarizing Language in Twitter using Topic Models and ML Algorithms

Abstract

The upsurge in the use of social media in public discourses has made it possible for social scientists to engage in emerging and interesting areas of research. Normally, public debates tend to assume polar positions along political, social or ideological lines. Generally, polarity in the language used is more of blaming the opposing group in such debates. In this paper, we investigated the detection of polarizing language in tweets in the event of a disaster. Our approach entails combining topic modeling and Machine Learning (ML) algorithms to generate topics that we consider to be polarized thereby classifying a given tweet as polar or not. Our latent Dirichlet allocation (LDA)-based model incorporates external resources in the form of a lexicon of blame-oriented words to induce the generation of polar topics. The Collapsed Gibbs sampling is used to infer new documents and to estimate the values of parameters employed in our model. We computed the log likelihood (LL) ratios using our model and two other state-of-the-art LDA-based models for evaluation. Furthermore, we compared polarized detection classification accuracy using the features extracted from polarized topics, bag of words (BOW) and part of speech (POS)-based features. Preliminary experiments returned higher overall accuracy results of 87.67% using topic-based features compared to BOW and POS-based features.