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Developing a hierarchical model for unraveling conspiracy theories

EPJ data science, 2024-12, Vol.13 (1), p.31-28 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2193-1127 ;DOI: 10.1140/epjds/s13688-024-00470-5

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  • Title:
    Developing a hierarchical model for unraveling conspiracy theories
  • Author: Ghasemizade, Mohsen ; Onaolapo, Jeremiah
  • Subjects: BERT ; Clustering ; Complexity ; Computer Appl. in Social and Behavioral Sciences ; Computer Science ; Conspiracy ; Conspiracy Theory ; Data-driven Science ; Labels ; Machine learning ; Modeling and Theory Building ; NLP ; RoBERTa ; Text Classification ; Theories ; Tree ; Trees
  • Is Part Of: EPJ data science, 2024-12, Vol.13 (1), p.31-28
  • Description: A conspiracy theory (CT) suggests covert groups or powerful individuals secretly manipulate events. Not knowing about existing conspiracy theories could make one more likely to believe them, so this work aims to compile a list of CTs shaped as a tree that is as comprehensive as possible. We began with a manually curated ‘tree’ of CTs from academic papers and Wikipedia. Next, we examined 1769 CT-related articles from four fact-checking websites, focusing on their core content, and used a technique called Keyphrase Extraction to label the documents. This process yielded 769 identified conspiracies, each assigned a label and a family name. The second goal of this project was to detect whether an article is a conspiracy theory, so we built a binary classifier with our labeled dataset. This model uses a transformer-based machine learning technique and is pre-trained on a large corpus called RoBERTa, resulting in an F1 score of 87%. This model helps to identify potential conspiracy theories in new articles. We used a combination of clustering (HDBSCAN) and a dimension reduction technique (UMAP) to assign a label from the tree to these new articles detected as conspiracy theories. We then labeled these groups accordingly to help us match them to the tree. These can lead us to detect new conspiracy theories and expand the tree using computational methods. We successfully generated a tree of conspiracy theories and built a pipeline to detect and categorize conspiracy theories within any text corpora. This pipeline gives us valuable insights through any databases formatted as text.
  • Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
  • Language: English
  • Identifier: EISSN: 2193-1127
    DOI: 10.1140/epjds/s13688-024-00470-5
  • Source: Springer Nature OA/Free Journals
    Coronavirus Research Database
    ProQuest Central
    DOAJ Directory of Open Access Journals
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