Improving Opinion Formation Models on Social Media Through Emotions

Abstract

Opinion formation models describe the opinion dynamics of interacting people. Social media are drastically increasing and have become one of the most critical media for people interactions. According to psychological researches, one’s emotion diffuses across interacting people. Furthermore, emotion affects people’s opinion. The emotion contagion also happens through social media via the users’ posts and affects the readers. Therefore, emotion is an essential element in opinion formation models in a social network which has attracted little attention. In this paper, we show how considering emotion in opinion formation model for online social networks improves the model. We have used a dataset containing some debates from the CreateDebate.com website. Two classifiers, with and without considering emotions, have been implemented based on the social impact model of opinion formation to predict the stances of the users’ next post in the dataset and the results have been compared with the dataset. The experiment results lead us to conclude that considering emotions improves the accuracy and precision of the social impact model of opinion formation in social media.