M. V.Khavronenko

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  • № 3, 2021

    • What Bots Can (and Can’t) Do? (Model of Protest and Counter-Protest Political Mobilization)

      Despite the increasing interest among scholars in the effect of Internet bots, or automated social media accounts, on the processes of political communication and mobilization in the online sphere, the extent of bots’ effectiveness and the specific mechanisms of their use remain largely understudied. The deficit of the overarching conceptual understanding and concrete results is arguably due to researchers’ aspiration to solve a problem in the empirical way, without attempting to combine data analysis with mathematical and computational modeling.

      Having analyzed the existing models on the topic, the authors offer their own model that is based on the spiral-of-silence theory. The key features of the model that set it apart from the existing ones are the following: a) taking into account differences in the types of motivation and costs associated with expressing protest and loyalist sentiments; b) including “partner effect” into the spiral-ofsilence mechanism; c) employing a neurological decision-ma king scheme according to which the same stimulus can prompt action and be a deterrent.

      On the basis of a series of computational experiments with the model, the authors demonstrate that bots are more effective in mobilizing opposition members when an individual motivated for political participation refrains from it because his local social community does not share his views. In this case, the emergence of a like-minded partner bot can destroy the spiral of silence created by this community and encourage this individual to openly express his position. On the contrary, when mobilizing loyalists, bots are most effective in relation to poorly motivated individuals.

      The model elaborated by the authors not only allows us to evaluate bots’ effects in a new way, but it also sheds light on how people make decisions in the framework of political communication and mobilization in social networks.

      DOI: 10.30570/2078-5089-2021-102-3-172-194

      Pages: 172-194