A Topic-Agnostic Approach for Identifying Fake News Pages
| Autor: | Juliana Freire, Sonia Castelo, Anas Elghafari, Aécio Santos, Eduardo F. Nakamura, Kien Pham, Thais G. Almeida |
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| Rok vydání: | 2019 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Computer Science - Computation and Language Information retrieval Computer science 05 social sciences Computer Science - Social and Information Networks 050801 communication & media studies 02 engineering and technology Computer Science - Information Retrieval Identification (information) Politics 0508 media and communications 020204 information systems 0202 electrical engineering electronic engineering information engineering Popular opinion Misinformation Fake news Computation and Language (cs.CL) Classifier (UML) Information Retrieval (cs.IR) |
| Zdroj: | WWW (Companion Volume) |
| DOI: | 10.1145/3308560.3316739 |
| Popis: | Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes. To better understand fake news, how they are propagated, and how to counter their effect, it is necessary to first identify them. Recently, approaches have been proposed to automatically classify articles as fake based on their content. An important challenge for these approaches comes from the dynamic nature of news: as new political events are covered, topics and discourse constantly change and thus, a classifier trained using content from articles published at a given time is likely to become ineffective in the future. To address this challenge, we propose a topic-agnostic (TAG) classification strategy that uses linguistic and web-markup features to identify fake news pages. We report experimental results using multiple data sets which show that our approach attains high accuracy in the identification of fake news, even as topics evolve over time. Comment: Accepted for publication in the Companion Proceedings of the 2019 World Wide Web Conference (WWW'19 Companion). Presented in the 2019 International Workshop on Misinformation, Computational Fact-Checking and Credible Web (MisinfoWorkshop2019). 6 pages |
| Databáze: | OpenAIRE |
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