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2024 | OriginalPaper | Buchkapitel

Improving Understanding of Misinformation Campaigns with a Two-Stage Methodology Using Semantic Analysis of Fake News

verfasst von : Sidbewendin Angelique Yameogo

Erschienen in: Research Challenges in Information Science

Verlag: Springer Nature Switzerland

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Abstract

Internet and social media are fueling the spread of disinformation on an unprecedented scale. Numerous tactics and techniques, such as Fake News, are employed to seek geopolitical advantages or financial gains. Many studies have focused on the automatic detection of Fake News, particularly using machine learning techniques. However, an informational attack often involves various vectors, targets, authors, and content. Detecting such an attack requires a global analysis of multiple Fake News instances. This research proposal aims to assist specialists, such as intelligence analysts or journalists responsible for combating disinformation, in better characterizing and detecting informational attacks.

We propose a framework based on a two-stage approach. The first stage involves extracting valuable knowledge from each Fake News using both Artificial Intelligence and Natural Language Processing (NLP) techniques. The second stage entails aggregating the collected information using data analysis methods to facilitate the characterization and identification of disinformation campaigns.

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Titel: Improving Understanding of Misinformation Campaigns with a Two-Stage Methodology Using Semantic Analysis of Fake News
verfasst von: Sidbewendin Angelique Yameogo
Verlag: Springer Nature Switzerland
Buch: Research Challenges in Information Science
Print ISBN: 978-3-031-59467-0

Electronic ISBN: 978-3-031-59468-7

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-59468-7_14

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