A Real-Time System for Credibility on Twitter

Adrian Iftene, Daniela Gifu, Andrei-Remus Miron, Mihai-Stefan Dudu


Abstract
Nowadays, social media credibility is a pressing issue for each of us who are living in an altered online landscape. The speed of news diffusion is striking. Given the popularity of social networks, more and more users began posting pictures, information, and news about personal life. At the same time, they started to use all this information to get informed about what their friends do or what is happening in the world, many of them arousing much suspicion. The problem we are currently experiencing is that we do not currently have an automatic method of figuring out in real-time which news or which users are credible and which are not, what is false or what is true on the Internet. The goal of this is to analyze Twitter in real-time using neural networks in order to provide us key elements about both the credibility of tweets and users who posted them. Thus, we make a real-time heatmap using information gathered from users to create overall images of the areas from which this fake news comes.
Anthology ID:
2020.lrec-1.757
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6166–6173
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.757
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.757.pdf