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Article published In: The Discourse of Terrorism
Edited by Encarnación Hidalgo-Tenorio and Juan L. Castro
[Pragmatics and Society 13:3] 2022
► pp. 532–554

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A semi-supervised algorithm for detecting extremism propaganda diffusion on social media

 | University of Granada
 | University of Zaragoza
 | University of Granada
 | University of Granada
Published online: 21 July 2022
DOI logo with link
https://doi.org/10.1075/ps.21009.fra
Abstract
Extremist online networks reportedly tend to use Twitter and other Social Networking Sites (SNS) in order to issue propaganda and recruitment statements. Traditional machine learning models may encounter problems when used in such a context, due to the peculiarities of microblogging sites and the manner in which these networks interact (both between themselves and with other networks). Moreover, state-of-the-art approaches have focused on non-transparent techniques that cannot be audited; so, despite the fact that they are top performing techniques, it is impossible to check if the models are actually fair. In this paper, we present a semi-supervised methodology that uses our Discriminatory Expressions algorithm for feature selection to detect expressions that are biased towards extremist content (Francisco, Manuel, and Juan Luis Castro. 2020. ‘Discriminatory Expressions to Produce Interpretable Models in Microblogging Context’. ArXiv:2012.02104 [Cs], November. [URL]). With the help of human experts, the relevant expressions are filtered and used to retrieve further extremist content in order to iteratively provide a set of relevant and accurate expressions. These discriminatory expressions have been proved to produce less complex models that are easier to comprehend, and thus improve model transparency. In the following, we present close to 70 expressions that were discovered by using this method alongside the validation test of the algorithm in several different contexts.
Article outline
  • Introduction
  • Theoretical background
    • What is a model and how do we train it?
    • How can we check that the model learned correctly?
    • Can models be interpreted by humans?
    • How do models deal with Natural Language documents?
    • Is it possible to reduce the dimensionality of the vector representation?
    • What are the reference filtering methods?
      • CHI2 (chi-square)
      • Information gain (IG)
      • Mutual information (MI)
      • Odds ratio (OR)
      • Expected cross entropy (ECE)
      • ANOVA F-value
      • Galavotti-sebastiani-simi coefficient (GSS)
    • Are filters going to help us comprehend models?
    • How can we be sure that this is the way to go?
  • Discriminatory expressions (DE)
    • Definition (Expression)
      • Definition (Discriminatory Expression)
  • Methodology
  • Experiments
  • Performance and comprehensibility tests
  • Application-related Tests
  • Results and discussion
  • Application-specific results
  • Limitations
  • Conclusions
  • Future work
  • Notes
  • References
References (37)
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