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Comparing the Impact of Social Media Regulations on News Consumption
Ieee Transactions on Computational Social Systems ; : 11, 2022.
Article in English | English Web of Science | ID: covidwho-1883142
ABSTRACT
Users online tend to consume information adhering to their system of beliefs and ignore dissenting information. During the COVID-19 pandemic, users get exposed to a massive amount of information about a new topic having a high level of uncertainty. In this article, we analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation concerning COVID-19. We compare the two platforms on about three million pieces of content, analyzing user interaction with respect to news articles. We first describe users' consumption patterns on the two platforms focusing on the political leaning of news outlets. Finally, we characterize the echo chamber effect by modeling the dynamics of users' interaction networks. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content, with a consequent affiliation toward reliable sources in terms of engagement and comments. Conversely, the lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior. Twitter users show segregation toward reliable content with a uniform narrative. Gab, instead, offers a more heterogeneous structure where users, independent of their leaning, follow people who are slightly polarized toward questionable news.
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Full text: Available Collection: Databases of international organizations Database: English Web of Science Type of study: Experimental Studies Language: English Journal: Ieee Transactions on Computational Social Systems Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: English Web of Science Type of study: Experimental Studies Language: English Journal: Ieee Transactions on Computational Social Systems Year: 2022 Document Type: Article