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Using graph embedding and machine learning to identify rebels on twitter

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  • Masood, Muhammad Ali
  • Abbasi, Rabeeh Ayaz

Abstract

During the last two decades, the number of incidents from extremists have increased, so as the use of social media. Research suggests that extremists use social media for reaching their purposes like recruitment, fund raising, and propaganda. Limited research is available to identify rebel users on social media platforms. Therefore, we propose a Supervised Rebel Identification (SRI) framework to identify rebels on Twitter. The framework consists of a novel mechanism to structure the users’ tweets into a directed user graph. This user graph links predicates (verbs) with the subject and object words to understand semantics of the underlying data. We convert the user graph into graph embedding to use these semantics within the machine learning algorithms. Apart from the user graph and its embedding, we propose fourteen other features belonging to tweets’ contents and users’ profiles. For evaluation, we present the first multicultural and multiregional dataset of rebels affiliated with nine rebel movements belonging to five countries. We evaluate the proposed SRI framework against two state-of-the-art baselines. The results show that the SRI framework outperforms the baselines with high accuracy.

Suggested Citation

  • Masood, Muhammad Ali & Abbasi, Rabeeh Ayaz, 2021. "Using graph embedding and machine learning to identify rebels on twitter," Journal of Informetrics, Elsevier, vol. 15(1).
  • Handle: RePEc:eee:infome:v:15:y:2021:i:1:s1751157720306386
    DOI: 10.1016/j.joi.2020.101121
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    References listed on IDEAS

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    1. Li, Daifeng & Ding, Ying & Shuai, Xin & Bollen, Johan & Tang, Jie & Chen, Shanshan & Zhu, Jiayi & Rocha, Guilherme, 2012. "Adding community and dynamic to topic models," Journal of Informetrics, Elsevier, vol. 6(2), pages 237-253.
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    3. Wang, Yuzhuo & Zhang, Chengzhi, 2020. "Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing," Journal of Informetrics, Elsevier, vol. 14(4).
    4. Dogan, Turgut & Uysal, Alper Kursat, 2020. "A novel term weighting scheme for text classification: TF-MONO," Journal of Informetrics, Elsevier, vol. 14(4).
    5. Muhammad Aslam Jarwar & Rabeeh Ayaz Abbasi & Mubashar Mushtaq & Onaiza Maqbool & Naif R. Aljohani & Ali Daud & Jalal S. Alowibdi & J.R. Cano & S. García & Ilyoung Chong, 2017. "CommuniMents: A Framework for Detecting Community Based Sentiments for Events," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(2), pages 87-108, April.
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    Cited by:

    1. Muñoz, María M. & Rojas-de-Gracia, María-Mercedes & Navas-Sarasola, Carlos, 2022. "Measuring engagement on twitter using a composite index: An application to social media influencers," Journal of Informetrics, Elsevier, vol. 16(4).
    2. Seyyed Reza Taher Harikandeh & Sadegh Aliakbary & Soroush Taheri, 2023. "An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1567-1582, March.
    3. Miguel Won & Jorge M. Fernandes, 2022. "Analyzing Twitter networks using graph embeddings: an application to the British case," Journal of Computational Social Science, Springer, vol. 5(1), pages 253-263, May.

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