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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Journal ArticleDOI
Twitter Usage in Indonesia
TL;DR: It is found that while one can potentially leverage Twitter for disaster management, careful collection, assessment, and coordination with official disaster Twitter sites and local on-scene Twitter opinion leaders will be critical.
Journal ArticleDOI
Near real-time topic-driven rumor detection in source microblogs
TL;DR: A novel topic-driven rumor detection framework to determine whether a post is a rumor only according to its source microblog, which significantly outperforms state-of-the-art methods on both two English and two Chinese benchmark datasets.
Journal ArticleDOI
Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
TL;DR: An Attention-based Long-Short Term Memory network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets is proposed that can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms.
Proceedings ArticleDOI
Identification of useful user comments in social media: a case study on flickr commons
TL;DR: The notion of usefulness in the context of social media comments is discussed and compared from end-users as well as expertusers perspectives and a machine-learning approach is presented to automatically classify comments according to their usefulness.
Proceedings ArticleDOI
Feature expansion using word embedding for tweet topic classification
TL;DR: In this paper, word embeddings based on word2vec are used to reduce the vocabulary mismatch for tweet topic classification and increase the likelihood of vocabulary mismatch.
References
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