Harnessing Personalization Methods to Identify and Predict Unreliable Information Spreader Behavior

Studies on detecting and understanding the spread of unreliable news on social media have identified key characteristic differences between reliable and unreliable posts. These differences in language use also vary in expression across individuals, making it important to consider personal factors in unreliable news detection. The application of personalization methods for this has been made possible by recent publication of datasets with user histories, though this area is still largely unexplored. In this paper we present approaches to represent social media users in order to improve performance on three tasks: (1) classification of unreliable news posts, (2) classification of un- reliable news spreaders, and, (3) prediction of the spread of unreliable news. We compare the User2Vec method from previous work to two other approaches; a learnable user embedding layer trained with the downstream task, and a representation derived from an authorship attribution classifier. We demonstrate that the implemented strategies substantially improve classification performance over state-of-the-art and provide initial results on the task of unreliable news prediction.

  • Published in:
    2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics
  • Type:
    Inproceedings
  • Authors:
    Ashraf, Shaina; Gruschka, Fabio; Flek, Lucie; Welch, Charles
  • Year:
    2024

Citation information

Ashraf, Shaina; Gruschka, Fabio; Flek, Lucie; Welch, Charles: Harnessing Personalization Methods to Identify and Predict Unreliable Information Spreader Behavior, 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2024, Association for Computational Linguistics, Ashraf.etal.2024b,

Associated Lamarr Researchers

Prof. Dr. Lucie Flek

Prof. Dr. Lucie Flek

Area Chair NLP to the profile