Social Search and Task-Related Relevance Dimensions in Microblogging Sites

Putri, Divi Galih Prasetyo; Viviani, Marco; Pasi, Gabriella

doi:10.1007/978-3-030-60975-7_22

Cited by 4 publications

(1 citation statement)

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“…However, providing information that is credible as well as topically relevant, is still a difficult and ongoing problem (Viviani and Pasi 2017;Putri et al 2020). Furthermore, social media platforms tend to emphasize some of the congenital, social, and psychological traits of individuals, which lead them to trust, above all, points of view that are similar to their own, disregarding their reliability.…”

Section: Introductionmentioning

confidence: 99%

Echo chamber detection and analysis

Villa

Pasi

Viviani

2021

Soc. Netw. Anal. Min.

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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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Section: Introductionmentioning

confidence: 99%