Big Data and Quality Data for Fake News and Misinformation Detection

Resource type
Date created
2019-05-23
Authors/Contributors
Abstract
Fake news has become an important topic of research in a variety of disciplines including linguistics and computer science. In this paper, we explain how the problem is approached from the perspective of natural language processing, with the goal of building a system to automatically detect misinformation in news. The main challenge in this line of research is collecting quality data, i.e., instances of fake and real news articles on a balanced distribution of topics. We review available datasets and introduce the MisInfoText repository as a contribution of our lab to the community. We make available the full text of the news articles, together with veracity labels previously assigned based on manual assessment of the articles’ truth content. We also perform a topic modelling experiment to elaborate on the gaps and sources of imbalance in currently available datasets to guide future efforts. We appeal to the community to collect more data and to make it available for research purposes.
Document
Published as
Torabi Asr, F., & Taboada, M. (2019). Big Data and quality data for fake news and misinformation detection. Big Data & Society. DOI: 10.1177/2053951719843310.
Publication title
Big Data Society
Document title
Big Data and quality data for fake news and misinformation detection
Date
2019
Publisher DOI
10.1177/2053951719843310
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
Member of collection
Attachment Size
2053951719843310.pdf 590.97 KB