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
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
Penghui Wei,Nan Xu,Wenji Mao +2 more
TL;DR: Wang et al. as discussed by the authors proposed a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter, which consists of two components: the bottom component classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network, and the top component predicts the rumor veracity by exploiting the temporal dynamics of stance evolution.
Proceedings ArticleDOI
MDFEND: Multi-domain Fake News Detection
TL;DR: In this article, a multi-domain fake news detection model (MDFEND) was proposed by utilizing domain gate to aggregate multiple representations extracted by a mixture of experts, which can significantly improve the performance of MFND.
Posted Content
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding
Sahar Abdelnabi,Mario Fritz +1 more
TL;DR: The Adversarial Watermarking Transformer (AWT) is introduced with a jointly trained encoder-decoder and adversarial training that, given an input text and a binary message, generates an output text that is unobtrusively encoded with the given message.
Posted Content
Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data
TL;DR: Reuters Tracer, a system that automates end-to-end news production using Twitter data, is presented, capable of detecting, classifying, annotating, and disseminating news in real time for Reuters journalists without manual intervention.
Journal ArticleDOI
Analysis of Techniques for Rumor Detection in Social Media
TL;DR: This paper focuses on detailed discussion of datasets and state-of-the-art approaches of rumor detection and sheds light upon supervised and unsupervised methods and deep learning approaches for rumor detection.
References
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Proceedings ArticleDOI
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