An Analytical Emotion Framework of Rumour Threads on Social Media
This work addresses the need for better understanding of rumour development in online social media, focusing on emotion analysis, but it is incremental as it builds on existing datasets and extends prior work by adding multi-aspect detection and comparative analysis.
The paper tackled the problem of understanding emotion dynamics in rumour versus non-rumour threads on social media, revealing that rumours trigger more negative emotions like anger and fear while non-rumours evoke more positive ones, with emotions being contagious and specific causal links identified such as surprise bridging rumours to other emotions.
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.