Original title: Social bots weaken activist cohesion
Linda Li, Orsolya Vasarhelyi, Balazs Vedres · August 2026
Online political movements rely on strong human relationships to stay active. Automated accounts, or social bots, produce large amounts of online political posts, but researchers do not know if bots damage the ties that hold activist groups together. To test this, researchers tracked retweet networks of core participants in the 2020 Black Lives Matter protests before, during, and after their peak. They looked at how tightly connected people stayed to each other over time. They found that more exposure to bots during the protest peak led to steeper drops in group cohesion afterward. This loss of connection concentrated especially among supporters of the movement. Bots may weaken activist causes by breaking the social ties needed to keep collective action going.
Imagine a group of friends who meet regularly to play soccer. Everyone knows each other and the game works well. Then strangers start showing up, not to play but to distract. Over time, some friends stop coming, and the group becomes less connected. In this paper, the strangers are social bots, and the soccer game is the activist network; the bots' presence weakens human ties, just as distractions break up the friend group.
Activists and organizers rely on tight-knit online networks to coordinate protests. This research shows that social bots can silently dissolve those networks, making it harder for movements to sustain momentum. Countering bots may require protecting human connections, not just fact-checking content.
The study establishes a correlation between bot exposure and cohesion decline, but does not prove causation. It focuses on a single movement (BLM 2020) and one platform (Twitter), so generalizability is unclear. The analysis relies on retweet networks, which may not capture all forms of social ties. Additionally, the mechanism by which bots erode cohesion is not directly tested.
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Social bots weaken activist cohesion — Linda Li, Orsolya Vasarhelyi, Balazs Vedres
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.