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Behavior-Based Machine Learning Approaches to Identify State-Sponsored Trolls on Twitter

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journal contribution
submitted on 2023-08-24, 09:12 and posted on 2023-09-20, 11:31 authored by Saleh Alhazbi

In recent years, there has been an increased prevalence of adopting state-sponsored trolls by governments and political organizations to influence public opinion through disinformation campaigns on social media platforms. This phenomenon negatively affects the political process, causes distrust in the political systems, sows discord within societies, and hastens political polarization. Thus, there is a need to develop automated approaches to identify sponsored-troll accounts on social media in order to mitigate their impacts on the political process and to protect people against opinion manipulation. In this paper, we argue that behaviors of sponsored-troll accounts on social media are different from ordinary users' because of their extrinsic motivation, and they cannot completely hide their suspicious behaviors, therefore these accounts can be identified using machine learning approaches based solely on their behaviors on the social media platforms. We have proposed a set of behavioral features of users' activities on Twitter. Based on these features, we developed four classification models to identify political troll accounts, these models are based on decision tree, random forest, Adaboost, and gradient boost algorithms. The models were trained and evaluated on a set of Saudi trolls disclosed by Twitter in 2019, the overall classification accuracy reaches up to 94.4%. The models also are capable to identify the Russian trolls with accuracy up to 72.6% without training on this set of trolls. This indicates that although the strategies of coordinated trolls might vary from an organization to another, they are all just employees and have common behaviors that can be identified.

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Published in: IEEE Access
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Open Access funding provided by the Qatar National Library.



  • English



Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU