Proceedings ArticleDOI
Information credibility on twitter
Carlos Castillo,Marcelo Mendoza,Barbara Poblete +2 more
- pp 675-684
TLDR
There are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.Abstract:
We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally.On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources.We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.read more
Citations
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Proceedings ArticleDOI
Reinforcement Subgraph Reasoning for Fake News Detection
TL;DR: A subgraph reasoning paradigm for fake news detection is proposed, which provides a crystal type of explainability by revealing which subgraphs of the news propagation network are the most important for news verification, and concurrently improves the generalization and discrimination power of graph-based detection models by removing task-irrelevant information.
Journal ArticleDOI
Classifying facts and opinions in Twitter messages: a deep learning-based approach
TL;DR: A deep learning-based algorithm is proposed that automatically separates facts from opinions in Twitter messages and it is found that it indeed benefits subsequent analytics applications.
Posted Content
Can We Spot the "Fake News" Before It Was Even Written?
TL;DR: Media profiles are developed that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame ofReporting, and stance with respect to various claims and topics in the Tanbih news aggregator.
Proceedings ArticleDOI
Assessing Arabic Weblog Credibility via Deep Co-learning
TL;DR: This work proposes deep co-learning, a semi-supervised end-to-end deep learning approach to assess the credibility of Arabic blogs, and evaluates the approach on an Arabic blogs dataset, and reports significant improvements in performance compared to many baselines including fully- supervised deep learning models as well as ensemble models.
Proceedings ArticleDOI
A Study of the Correlation between the Spatial Attributes on Twitter
Bum-Suk Lee,Byung-Yeon Hwang +1 more
TL;DR: This paper investigates the correlation between the profile locations on Twitter and the GPS coordinates in tweets and finds that nearly 50% of users post the most of their tweets in the Profile locations while 30% of Users, who may have high mobility in a wide range, do not have any tweets in their locations.
References
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