Data-driven Contributions to Understanding User Engagement Dynamics on Social Media

• Social media platforms have fundamentally transformed the way information is produced, distributed, and consumed. News digestion and dissemination are not an exception. A recent study by the Pew Research Center highlights that 53% of Twitter (renamed X) users, alongside notable percentages on Facebook (43%), Reddit (38%), and Instagram (34%), rely on these platforms for their daily news. Unfortunately, not all news is reliable and unbiased, which poses a significant societal challenge. Beyond news, content posted by influencers can also play an important role in shaping opinions and behaviors.Indeed, how users engage with different classes of content (including unreliable content) on social media can amplify their visibility and shape public perceptions and debates. Recognizing this, prior research has studied different aspects of user engagement dynamics with varying classes of content. However, several unexplored dimensions remain. To better understand these dynamics, this thesis addresses part of this research gap through eight comprehensive studies across four key dimensions, where we place particular focus on news content.The first dimension of this thesis presents a large-scale analysis of users' interactions with news publishers on Twitter. This analysis provides a fine-grained understanding of engagement patterns with various classes of publishers, with key findings indicating elevated engagement rates among unreliable news publishers. The second dimension examines the dynamics of interaction patterns between public and private (less public) sharing of news articles on Facebook. This dimension highlights deeper user engagement in private contexts compared to the public sphere, with both spheres showing the highest interaction levels with highly unreliable content. The third dimension investigates the drivers of popularity among news tweets to understand what makes some tweets more/less successful in gaining user engagement. For instance, this analysis reveals the negative impact of analytic language on user engagement, with the biggest engagement declines observed among unreliable publishers. Finally, the thesis emphasizes the importance of temporal dynamics in user engagement. For example, exploring the temporal user engagement with different news classes over time, we observe a positive correlation between the reliability of a post and the early interactions it receives on Facebook. While the thesis quantitatively assesses the effects of reliability across all dimensions, it also places additional focus on the role of bias in the observed patterns.These and other insights presented in the thesis offer actionable insights that can benefit multiple stakeholders, providing policymakers and content moderators with a comprehensive perspective for addressing the spread of problematic content. Moreover, platform designers can leverage the insights to build features that promote healthy online communities, while news outlets can use them to tailor content strategies based on target audiences, and individual users can use them to make informed decisions. Although the thesis has inherent limitations, it deepens our current understanding of engagement dynamics to foster a more secure and trustworthy social media experience that remains engaging.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

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