Research Article
Detecting Fake News Spreaders on Twitter Through Follower Networks
@INPROCEEDINGS{10.1007/978-3-031-33614-0_13, author={Smita Ghosh and Juan Manuel Zuluaga Fernandez and Isabel Zuluaga Gonz\^{a}lez and Andres Mauricio Calle and Navid Shaghaghi}, title={Detecting Fake News Spreaders on Twitter Through Follower Networks}, proceedings={Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings}, proceedings_a={BDTA}, year={2023}, month={5}, keywords={Bidirectional Encoder Representations from Transformers (BERT) Fake News Detection Misinformation Spread Natural Language Processing (NLP) Social Media Twitter User Impact Factor (UIF)}, doi={10.1007/978-3-031-33614-0_13} }- Smita Ghosh
Juan Manuel Zuluaga Fernandez
Isabel Zuluaga González
Andres Mauricio Calle
Navid Shaghaghi
Year: 2023
Detecting Fake News Spreaders on Twitter Through Follower Networks
BDTA
Springer
DOI: 10.1007/978-3-031-33614-0_13
Abstract
Obtaining news from social media platforms has become increasingly popular due to their ease of access and high speed of information dissemination. These same factors have, however, also increased the range and speed at which misinformation and fake news spread. While machine-run accounts (bots) contribute significantly to the spread of misinformation, human users on these platforms also play a key role in contributing to the spread. Thus, there is a need for an in-depth understanding of the relationship between users and the spread of fake news. This paper proposes a new data-driven metric calledUser Impact Factor (UIF)aims to show the importance of user content analysis and neighbourhood influence to profile a fake news spreader on Twitter. Tweets and retweets of each user are collected and classified as ‘fake’ or ‘not fake’ using Natural Language Processing (NLP). These labeled posts are combined with data on the number of the user’s followers and retweet potential in order to generate the user’s impact factor. Experiments are performed using data collected from Twitter and the results show the effectiveness of the proposed approach in identifying fake news spreaders.
