An Analytical Emotion Framework of Rumour Threads on Social Media

TL;DR

This paper presents a comprehensive analytical framework for understanding emotional dynamics in social media rumour threads, highlighting how emotions influence rumour spread and differ from non-rumour discussions.

Contribution

It introduces a multi-aspect emotion detection framework contrasting rumour and non-rumour threads, with causal and correlation analysis on large datasets.

Findings

Rumours evoke more negative emotions like anger and fear.

Non-rumours tend to evoke positive emotions such as joy and love.

Emotions are contagious and influence the spread of rumours.

Abstract

Rumours in online social media pose significant risks to modern society, motivating the need for better understanding of how they develop. We focus specifically on the interface between emotion and rumours in threaded discourses, building on the surprisingly sparse literature on the topic which has largely focused on single aspect of emotions within the original rumour posts themselves, and largely overlooked the comparative differences between rumours and non-rumours. In this work, we take one step further to provide a comprehensive analytical emotion framework with multi-aspect emotion detection, contrasting rumour and non-rumour threads and provide both correlation and causal analysis of emotions. We applied our framework on existing widely-used rumour datasets to further understand the emotion dynamics in online social media threads. Our framework reveals that rumours trigger more negative emotions (e.g., anger, fear, pessimism), while non-rumours evoke more positive ones. Emotions are contagious, rumours spread negativity, non-rumours spread positivity. Causal analysis shows surprise bridges rumours and other emotions; pessimism comes from sadness and fear, while optimism arises from joy and love.