What News Do People Get on Social Media? Analyzing Exposure and Consumption of News through Data Donations

Salim Chouaki, Abhijnan Chakraborty, Oana Goga, Savvas Zannettou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Understanding how exposure to news on social media impacts public discourse and exacerbates political polarization is a significant endeavor in both computer and social sciences. Unfortunately, progress in this area is hampered by limited access to data due to the closed nature of social media platforms. Consequently, prior studies have been constrained to considering only fragments of users' news exposure and reactions. To overcome this obstacle, we present an innovative measurement approach centered on donating personal data for scientific purposes, facilitated through a privacy-preserving tool that captures users' interactions with news on Facebook. This approach offers a nuanced perspective on users' news exposure and consumption, encompassing different types of news exposure: selective, incidental, algorithmic, and targeted, driven by the diverse underlying mechanisms governing news appearance on users' feeds. Our analysis of data from 472 participants based in the U.S. reveals several interesting findings. For instance, users are more prone to encountering misinformation because of their active selection of low-quality news sources rather than being exposed solely due to friends or platform algorithms. Furthermore, our study uncovers that users are open to engaging with news sources with opposite political ideology as long as these interactions are not visible to their immediate social circles. Overall, our study showcases the viability of data donation as a means to provide clarity to longstanding questions in this field, offering new perspectives on the intricate dynamics of social media news consumption and its effects.

Original languageEnglish
Title of host publicationWWW 2024 - Proceedings of the ACM Web Conference
PublisherAssociation for Computing Machinery, Inc
Pages2371-2382
Number of pages12
ISBN (Electronic)9798400701719
DOIs
Publication statusPublished - 13 May 2024
Event33rd ACM Web Conference, WWW 2024 - Singapore, Singapore
Duration: 13 May 202417 May 2024

Publication series

NameWWW 2024 - Proceedings of the ACM Web Conference

Conference

Conference33rd ACM Web Conference, WWW 2024
Country/TerritorySingapore
CitySingapore
Period13/05/2417/05/24

Keywords

  • data donation
  • news exposure
  • social media

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