Quantifying Opinion Rejection: A Method to Detect Social Media Echo Chambers

Kushani Perera, Shanika Karunasekera

Abstract: Social media echo chambers are known to be common sources of misinformation and harmful ideologies that have detrimental impacts on society. Therefore, techniques to detect echo chambers are of great significance. Reinforcement of supporting opinions and rejection of dissenting opinions are two significant echo chamber properties that help detecting them in social networks. However, existing echo chamber detection methods do not capture the opinion rejection behaviour, which leads to poor echo chamber detection accuracy. Measures used by them do not facilitate quantifying both properties simultaneously while preserving the connectivity between echo chamber members. To address this problem, we propose a new measure, Signed Echo (SEcho) that quantifies opinion reinforcement and rejection properties of echo chambers and an echo chamber detection algorithm, Signed Echo Detection Algorithm (SEDA) based on this measure, which preserves the connectivity among echo chamber members. The experimental results for real-world data show that SEDA outperforms the state-of-the-art echo chamber detection methods in detecting the communities with echo chamber properties, such as reinforcement of supporting opinions, rejection of dissenting opinions, connectivity between community members, spread of mis/disinformation and emotional contagion.