SentiStance: Quantifying the intertwined changes of sentiment and stance in response to an event in online forums

Abstract

How are the sentiment and stance of online users affected by real-world events? Previous studies have ignored the role of events in co-determining sentiment and stance and hence have failed to understand the relationship between these two important aspects of public opinion. In this paper, we develop SentiStance, a systematic framework to understand the intertwined change of sentiment and stance due to real-world events in online discussions. In our approach: (a) we customize state-of-the-art NLP techniques to overcome domain-specific constraints, and (b) we provide an efficient way to quantify the change of sentiment and stance in tandem. As a case study, we focus on the 2020 United States Election events and we analyze 7.5 million posts from 4chan, Reddit, and Parler over a span of three months from November 2020 to January 2021. We showcase our framework by describing the effect that the Jan 6 insurrection had on concepts "Pence"and "Trump."Parler users turn significantly against Pence with (33.1% increase in Against stance and Negative sentiment), while Reddit users' opinion improves (with a drop of 7.1% in the same combination of sentiment and stance). By contrast, the effect of the same event on the concept "Trump"shows no statistically significant change. In addition, our results suggest that conditioning on significant events strengthens the correlation between sentiment and stance, which provides a new perspective on the debate around the correlation between sentiment and stance. Overall, we see our work as a fundamental building block towards a data-driven understanding of the interplay of preferences and emotions of online forum users towards a concept.