Research framework and broader academic knowledge base
Serbia: documented application of the same framework
How do bots, organized networks, and algorithms make a message seem larger and more popular than it is?
Bots and organized networks can give the same message the appearance of mass and spontaneous support. A large number of orders repeat the same claims, artificially raise the mark among popular topics, attack critics and bury the conversation so that the actual participants withdraw. The platform algorithm can then further spread content that already receives a lot of reactions, regardless of whether they are authentic.
The important effect is not just persuasion, but creating the impression that “everybody thinks so”, that opposition is lonely or that public speaking will bring an attack. However, high visibility is not the same as changing opinions, and bot detection tools are wrong. The claim seeks evidence of a harmonized time, content and account network, as well as the careful separation of an organized campaign from actual popularity.
Programs that recognize bots and organized networks can be wrong. The high visibility of the message also does not prove that people have changed their minds.
Works supporting this answer, with their evidence type and an explanation of how they relate to the claim.
Explain how the system works
Schlumberger, O., Edel, M., Maati, A. i Saglam, K. (2024), „How Authoritarianism Transforms: A Framework for the Study of Digital Dictatorship“, Government and Opposition 59(3): 761–783.
- Contribution to this answer
- The paper shows how digital technologies can increase the ability of authoritarian authorities to monitor, direct and shape political communication, but it does not specifically study how bots or coordinated networks artificially increase the popularity of a message. Here is a wider framework of digital authoritarianism.
research that checks more closely what the cause is
Milli, S. i dr. (2025), „Engagement, user satisfaction, and the amplification of divisive content on social media“, PNAS Nexus 4(3): pgaf062.
- Contribution to this answer
- Milli and co-authors show very concrete what the algorithm itself can do: ranking according to engagement gives more visibility to emotionally charged and politically divisive content than a chronological representation, even when users themselves do not say that they want such content more. The message may therefore seem more important simply because the system rewards it with a greater range.
research that checks more closely what the cause is
Brady, W. J., McLoughlin, K., Doan, T. N. i Crockett, M. J. (2021), „How Social Learning Amplifies Moral Outrage Expression in Online Social Networks“, Science Advances 7(33): eabe5641.
Brady, W. J., McLoughlin, K., Doan, T. N. i Crockett, M. J. (2021), "How Social Learning Amplifies Moral Outrage Expression in Online Social Networks", Science Advances 7(33): eabe5641.
- Contribution to this answer
- Brady and the co-authors show another mechanism of reinforcement: when the expression of moral indignation receives more social rewards likes, shares and other reactions people learn to repeat it more often. Thus, the network itself can produce more and more content that looks like a broad and intense mood of the public.
study of Iran and international technology firms
Conduit, D. (2025), „Digital Authoritarianism and the Global Technology Industry: Evidence from Iran“, Government and Opposition 60(3): 751–772.
- Contribution to this answer
- Conduit shows how global technology companies can enable authoritarian regimes to have sophisticated digital control and operations, but the work is not about bots, algorithmic rankings or artificial creation of popularity messages.
review more research
Roberts, M. E. (2020), „Resilience to Online Censorship“, Annual Review of Political Science 23: 401–419.
- Contribution to this answer
- Roberts studies when people manage to bypass Internet censorship and give up on it. This is important for digital information space, but does not explain the mechanism of bots, coordinated amplification or algorithmic popularity.
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