IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v15y2021i1s1751157720306386.html
   My bibliography  Save this article

Using graph embedding and machine learning to identify rebels on twitter

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157720306386
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.joi.2020.101121?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. Dogan, Turgut & Uysal, Alper Kursat, 2020. "A novel term weighting scheme for text classification: TF-MONO," Journal of Informetrics, Elsevier, vol. 14(4).
    4. Adam Badawy & Emilio Ferrara, 2018. "The rise of Jihadist propaganda on social networks," Journal of Computational Social Science, Springer, vol. 1(2), pages 453-470, September.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kitti Nagy & Jozef Kapusta, 2021. "Improving fake news classification using dependency grammar," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-22, September.
    2. Peng Wang & Mengnan Zhang & Yike Wang & Xiqing Yuan, 2023. "Sustainable Career Development of Chinese Generation Z (Post-00s) Attending and Graduating from University: Dynamic Topic Model Analysis Based on Microblogging," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    3. Small, Kenneth A. & Ng, Chen Feng, 2014. "Optimizing road capacity and type," Economics of Transportation, Elsevier, vol. 3(2), pages 145-157.
    4. Sunil Kumar & Ilyoung Chong, 2018. "Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States," IJERPH, MDPI, vol. 15(12), pages 1-24, December.
    5. Yuzhuo Wang & Chengzhi Zhang & Kai Li, 2022. "A review on method entities in the academic literature: extraction, evaluation, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2479-2520, May.
    6. Xiaorui Jiang & Jingqiang Chen, 2023. "Contextualised segment-wise citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5117-5158, September.
    7. Farhan Shehzad & Abdur Rehman & Kashif Javed & Khalid A. Alnowibet & Haroon A. Babri & Hafiz Tayyab Rauf, 2022. "Binned Term Count: An Alternative to Term Frequency for Text Categorization," Mathematics, MDPI, vol. 10(21), pages 1-25, November.
    8. Xuan Liu & Tianyi Shi & Guohui Zhou & Mingzhe Liu & Zhengtong Yin & Lirong Yin & Wenfeng Zheng, 2023. "Emotion classification for short texts: an improved multi-label method," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    9. Anna Ruelens, 2022. "Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems," Journal of Computational Social Science, Springer, vol. 5(1), pages 731-749, May.
    10. Wang, Zhenhua & Ren, Ming & Gao, Dong & Li, Zhuang, 2023. "A Zipf's law-based text generation approach for addressing imbalance in entity extraction," Journal of Informetrics, Elsevier, vol. 17(4).
    11. Saeed-Ul Hassan & Timothy D. Bowman & Mudassir Shabbir & Aqsa Akhtar & Mubashir Imran & Naif Radi Aljohani, 2019. "Influential tweeters in relation to highly cited articles in altmetric big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 481-493, April.
    12. Schröder, Nadine & Falke, Andreas & Hruschka, Harald & Reutterer, Thomas, 2019. "Analyzing the Browsing Basket: A Latent Interests-Based Segmentation Tool," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 181-197.
    13. Erjia Yan, 2014. "Topic-based Pagerank: toward a topic-level scientific evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 407-437, August.
    14. Yaming Zhang & Wenjie Song & Jiang Shao & Majed Abbas & Jiaqi Zhang & Yaya H. Koura & Yanyuan Su, 2023. "Social Bots’ Role in the COVID-19 Pandemic Discussion on Twitter," IJERPH, MDPI, vol. 20(4), pages 1-21, February.
    15. Coche, Eugénie, 2018. "Privatised enforcement and the right to freedom of expression in a world confronted with terrorism propaganda online," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 7(4), pages 1-17.
    16. Andrea Ceron & Luigi Curini & Stefano M. Iacus, 2019. "ISIS at Its Apogee: The Arabic Discourse on Twitter and What We Can Learn From That About ISIS Support and Foreign Fighters," SAGE Open, , vol. 9(1), pages 21582440187, March.
    17. Hall, Lisa M.H. & Buckley, Alastair R., 2016. "A review of energy systems models in the UK: Prevalent usage and categorisation," Applied Energy, Elsevier, vol. 169(C), pages 607-628.
    18. Qiang Gao & Xiao Huang & Ke Dong & Zhentao Liang & Jiang Wu, 2022. "Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1543-1563, March.
    19. Qian-Jin Zong & Hong-Zhou Shen & Qin-Jian Yuan & Xiao-Wei Hu & Zhi-Ping Hou & Shun-Guo Deng, 2013. "Doctoral dissertations of Library and Information Science in China: A co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 781-799, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:15:y:2021:i:1:s1751157720306386. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.