Revealing The Secret Power: How Algorithms Can Influence Content Visibility on Twitter/X
Alessandro Galeazzi, Pujan Paudel, Mauro Conti, Emiliano De Cristofaro, Gianluca Stringhini

TL;DR
This study analyzes how Twitter/X algorithms manipulate content visibility, revealing systematic biases against external links and account-dependent visibility alterations, emphasizing the need for transparency in social media moderation.
Contribution
It provides a large-scale quantitative analysis of visibility manipulation patterns on Twitter/X, highlighting biases and the influence of user accounts on content visibility.
Findings
External links are penalized, reducing visibility up to eightfold.
Visibility varies depending on the account producing the content.
Algorithmic biases are consistent across ideological and source reliability lines.
Abstract
In recent years, the opaque design and the limited public understanding of social networks' recommendation algorithms have raised concerns about potential manipulation of information exposure. Reducing content visibility, aka shadow banning, may help limit harmful content; however, it can also be used to suppress dissenting voices. This prompts the need for greater transparency and a better understanding of this practice. In this paper, we investigate the presence of visibility alterations through a large-scale quantitative analysis of two Twitter/X datasets comprising over 40 million tweets from more than 9 million users, focused on discussions surrounding the Ukraine-Russia conflict and the 2024 US Presidential Elections. We use view counts to detect patterns of reduced or inflated visibility and examine how these correlate with user opinions, social roles, and narrative framings.…
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Taxonomy
TopicsMisinformation and Its Impacts
