- PAAn Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
by Matus Tomlein (Kempelen Institute of Intelligent Technologies, Slovakia), Branislav Pecher (Kempelen Institute of Intelligent Technologies, Slovakia), Jakub Simko (Kempelen Institute of Intelligent Technologies, Slovakia), Ivan Srba (Kempelen Institute of Intelligent Technologies, Slovakia), Robert Moro (Kempelen Institute of Intelligent Technologies, Slovakia), Elena Stefancova (Kempelen Institute of Intelligent Technologies, Slovakia), Michal Kompan (Kempelen Institute of Intelligent Technologies, Slovakia), Andrea Hrckova (Kempelen Institute of Intelligent Technologies, Slovakia), Juraj Podrouzek (Kempelen institute of intelligent technologies, Slovakia), and Maria Bielikova (Kempelen Institute of Intelligent Technologies, Slovakia)
The negative effects of misinformation filter bubbles in adaptive systems have been known to researchers for some time. Several studies investigated, most prominently on YouTube, how fast a user can get into a misinformation filter bubble simply by selecting “wrong choices” from the items offered. Yet, no studies so far have investigated what it takes to “burst the bubble”, i.e., revert the bubble enclosure. We present a study in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles by watching misinformation promoting content (for various topics). Then, by watching misinformation debunking content, the agents try to burst the bubbles and reach more balanced recommendation mixes. We recorded the search results and recommendations, which the agents encountered, and analyzed them for the presence of misinformation. Our key finding is that bursting of a filter bubble is possible, albeit it manifests differently from topic to topic. Moreover, we observe that filter bubbles do not truly appear in some situations. We also draw a direct comparison with a previous study. Sadly, we did not find much improvements in misinformation occurrences, despite recent pledges by YouTube.
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- 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.
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- 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.
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