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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Book ChapterDOI
Topic Detection in Twitter Using Topology Data Analysis
TL;DR: Topol is proposed, a topic detection method based on Topology Data Analysis (TDA) that transforms the Euclidean feature space into a topological space where the shapes of noisy irrelevant documents are much easier to distinguish from topically-relevant documents.
Journal ArticleDOI
An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors
TL;DR: A adaptive parameter tuning method is proposed to solve the negative transferring problem in the parameter transferring process and achieves more accurate rumor detection and significantly outperforms state-of-the-art rumor detection models.
Journal ArticleDOI
Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo
TL;DR: A novel deep recurrent neural model with a symmetrical network architecture for automatic rumor detection in social media such as Sina Weibo, which shows better performance than the existing methods.
Journal ArticleDOI
Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media
TL;DR: A novel method termed Embed2Detect is proposed for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering that is capable of effective and efficient event detection and it outperforms the recent event detection methods.
Proceedings Article
RP-DNN : a Tweet level propagation context based deep neural networks for early rumor detection in social media
TL;DR: The authors proposed a novel hybrid neural network architecture, which combines a task-specific character-based bidirectional language model and stacked Long Short-Term Memory (LSTM) networks to represent textual contents and social-temporal contexts of input source tweets for modeling propagation patterns of rumors in the early stages of their development.
References
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