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Effect of text augmentation and adversarial training on fake news detection

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.1109/TCSS.2023.3344597
AuthorSearch for: ORCID identifier: https://orcid.org/0000-0001-9568-1563; Search for: ORCID identifier: https://orcid.org/0000-0003-2987-8047; Search for: ORCID identifier: https://orcid.org/0000-0002-5506-5261; Search for: 1ORCID identifier: https://orcid.org/0000-0002-4045-8687
Affiliation
  1. National Research Council Canada. Digital Technologies
FormatText, Article
Subjectmeaning representation (AMR); adversarial training; clonal selection algorithm; cyberattack; fake news; graph representation; text generation; data models; training; machine learning; training data; data augmentation; social networking (online)
Abstract
The action of spreading false information through fake news articles presents a significant danger to society because it has the ability to shape public opinion with inaccurate facts. This can lead to negative effects, such as reduced trust in institutions and the promotion of conflict, division, and even violence. In this article, a text augmentation technique is introduced as a means of generating new data from preexisting fake news datasets. This approach has the potential to enhance classifier performance by a range of 3%–11%. It can also be utilized to launch a successful attack on trained classifiers, with up to a 90% success rate. However, the success rate of these attacks decreased to less than 28% when the model was retrained with the generated adversarial examples. These results demonstrate the effectiveness of text augmentation as a viable method for detecting fake news and increasing classifier accuracy and performance, as well as its ability to be utilized to perform adversarial machine learning (ML) and improve the resilience of ML algorithms.
Publication date
PublisherInstitute of Electrical and Electronics Engineers
In
  • IEEE Transactions on Computational Social Systems 11, no. 4 (12 January 2024): 4775–4789.
LanguageEnglish
Peer reviewedYes
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Record identifier34458b13-f5d9-4c67-8549-781b36951a8d
Record created2024-04-03
Record modified2024-10-16
Date modified: