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Echo chamber detection and analysis: A topology- and content-based approach in the COVID-19 scenario.
Villa, Giacomo; Pasi, Gabriella; Viviani, Marco.
  • Villa G; Information and Knowledge Representation, Retrieval, and Reasoning (IKR3) Lab - Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy.
  • Pasi G; Information and Knowledge Representation, Retrieval, and Reasoning (IKR3) Lab - Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy.
  • Viviani M; Information and Knowledge Representation, Retrieval, and Reasoning (IKR3) Lab - Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy.
Soc Netw Anal Min ; 11(1): 78, 2021.
Article in English | MEDLINE | ID: covidwho-1372829
ABSTRACT
Social media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice," and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Soc Netw Anal Min Year: 2021 Document Type: Article Affiliation country: S13278-021-00779-3

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Soc Netw Anal Min Year: 2021 Document Type: Article Affiliation country: S13278-021-00779-3