User-Based Stance Analysis for Mitigating the Impact of Social Bots on Measuring Public Opinion with Stance Detection in Twitter

Abstract

Stance detection is the task of detecting the standpoint of a user towards a target of interest, such as a controversial topic. Stance detection has various applications such as surveying and polling the public as an alternative to traditional instruments to measure public opinion. One of the implications of using stance detection on Twitter data to measure public opinion is the prevalence of social bots that can impact the measured public opinion. In this paper, we propose a user-based stance analysis to mitigate the impact of social bots on measuring public opinion from stance detection in Twitter. In contrast to a tweet-based stance analysis, the user-based stance analysis shows a minimal impact of social bots on measured public opinion for all stance classes: favor, against, and neutral.