Session 1: Echo Chambers and Filter Bubbles

Date: Monday 14:45 – 15:30 CET
Chair: Ludovico Boratto (University of Cagliari)

  • PAThe Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending
    by Tim Donkers (University of Duisburg-Essen Interactive Systems Research Group, Germany) and Jürgen Ziegler (University of Duisburg-Essen Interactive Systems Research Group, Germany)

    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.

    Full text in ACM Digital Library

  • PAI Want to Break Free! Recommending Friends from Outside the Echo Chamber
    by Antonela Tommasel (ISISTAN, CONICET-UNICEN, Argentina), Juan Manuel Rodriguez (ISISTAN, CONICET-UNICEN, Argentina), and Daniela Godoy (ISISTAN, CONICET-UNICEN, Argentina)

    Recommender systems serve as mediators of information consumption and propagation. In this role, these systems have been recently criticized for introducing biases and promoting the creation of echo chambers and filter bubbles, thus lowering the diversity of both content and potential new social relations users are exposed to. Some of these issues are a consequence of the fundamental concepts on which recommender systems are based on. Assumptions like the homophily principle might lead users to content that they already like or friends they already know, which can be naïve in the era of ideological uniformity and fake news. A significant challenge in this context is how to effectively learn the dynamic representations of users based on the content they share and their echo chamber or community interactions to recommend potentially relevant and diverse friends from outside the network of influence of the users’ echo chamber. To address this, we devise FRediECH (a Friend RecommenDer for breakIng Echo CHambers), an echo chamber-aware friend recommendation approach that learns users and echo chamber representations from the shared content and past users’ and communities’ interactions. Comprehensive evaluations over Twitter data showed that our approach achieved better performance (in terms of relevance and novelty) than state-of-the-art alternatives, validating its effectiveness.

    Full text in ACM Digital Library

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