The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending

Abstract

Echo chambers are social phenomena that amplify agreement and suppress opposing views in social media which may lead to fragmentation and polarization of the user population. In prior research, echo chambers have mainly been modeled as a result of social information diffusion. While most scientific work has framed echo chambers as a result of epistemic imbalances between polarized communities, we argue that members of echo chambers often actively discredit outside sources to maintain coherent world views. We therefore argue that two different types of echo chambers occur in social media contexts: Epistemic echo chambers create information gaps mainly through their structure whereas ideological echo chambers systematically exclude counter-attitudinal information. Diversifying recommendations by simply widening the scope of topics and viewpoints covered to counteract the echo chamber effect may be ineffective in such contexts. To investigate the characteristics of this dual echo chamber view and to assess the depolarizing effects of diversified recommendations, we apply an agent-based modeling approach. We rely on knowledge graph embedding techniques not only to generate recommendations, but also to show how to utilize logical graph queries in embedding spaces to diversify recommendations aimed at challenging polarization in online discussions. The results of our evaluation indicate that counteracting the two different types of echo chambers requires fundamentally different diversification strategies.