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Proceedings ArticleDOI

Information credibility on twitter

TL;DR: 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%.

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Citations
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Journal ArticleDOI
09 Mar 2018-Science
TL;DR: A large-scale analysis of tweets reveals that false rumors spread further and faster than the truth, and false news was more novel than true news, which suggests that people were more likely to share novel information.
Abstract: We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

4,241 citations


Cites background from "Information credibility on twitter"

  • ...Some work develops theoretical models of rumor diffusion [37, 38, 39, 40], or methods for rumor detection [41, 42, 43, 44], credibility evaluation [45] or interventions to curtail the spread of rumors [46, 47, 48]....

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Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors presented a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets.
Abstract: Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

1,891 citations

Posted Content
TL;DR: This survey presents a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets, and future research directions for fake news detection on socialMedia.
Abstract: Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of "fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

887 citations


Cites background or methods from "Information credibility on twitter"

  • ...to infer the credibility and reliability for each user using various aspects of user demographics, such as registration age, number of followers/followees, number of tweets the user has authored, etc [11]. Group level user features capture overall characteristics of groups of users related to the news [99]. The assumption is that the spreaders of fake news 10https://www.wired.com/2016/12/photos-fuel-s...

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  • ...h as supporting, denying, etc [37]. Topic features can be extracted using topic models, such as latent Dirichlet allocation (LDA) [49]. Credibility features for posts assess the degree of reliability [11]. Group level features aim to aggregate the feature values for all relevant posts for specic news articles by using \wisdom of crowds". For example, the average credibility scores are used to ev...

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Proceedings Article
09 Jul 2016
TL;DR: A novel method that learns continuous representations of microblog events for identifying rumors based on recurrent neural networks that detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.
Abstract: Microblogging platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. To detect rumors, existing approaches have relied on hand-crafted features for employing machine learning algorithms that require daunting manual effort. Upon facing a dubious claim, people dispute its truthfulness by posting various cues over time, which generates long-distance dependencies of evidence. This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. The proposed model is based on recurrent neural networks (RNN) for learning the hidden representations that capture the variation of contextual information of relevant posts over time. Experimental results on datasets from two real-world microblog platforms demonstrate that (1) the RNN method outperforms state-of-the-art rumor detection models that use hand-crafted features; (2) performance of the RNN-based algorithm is further improved via sophisticated recurrent units and extra hidden layers; (3) RNN-based method detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.

791 citations


Cites background or methods from "Information credibility on twitter"

  • ...We construct two microblog datasets using Twitter (www. twitter.com) and Sina Weibo (weibo.com)....

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  • ...To balance the two classes, we further added some non-rumor events from two public datasets [Castillo et al., 2011; Kwon et al., 2013]....

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  • ...The simplest RNN model, tanh-RNN, achieves 82.7% accuracy on Twitter and 87.3% on Weibo....

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  • ...For example, on August 25th of 2015, a rumor about “shootouts and kidnappings by drug gangs happening near schools in Veracruz” spread through Twitter and Facebook1....

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  • ...We refine the keywords by adding, deleting or replacing words manually, and iteratively until the composed queries can have reasonably precise Twitter search results....

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Journal ArticleDOI
TL;DR: An increasing trend in published articles on health-related misinformation and the role of social media in its propagation is observed, and the most extensively studied topics involving misinformation relate to vaccination, Ebola and Zika Virus, although others, such as nutrition, cancer, fluoridation of water and smoking also featured.

773 citations


Cites background from "Information credibility on twitter"

  • ...Many studies have thus analysed the credibility of user-generated contents and the cognitive process involved in the decision to spread online information on social and political events (Abbasi and Liu, 2013; Castillo et al., 2011; Lupia, 2013; Swire et al., 2017)....

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References
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01 Jan 2010
TL;DR: The premise is that truly mundane content is not interesting in any context, and thus can be quickly filtered using simple queryindependent features, which could be used for tiering indexes in a micro-blog search engine, with the filtered uninteresting content relegated to the less frequently accessed tiers.
Abstract: A BST R A C T We study the problem of identifying uninteresting content in text streams from micro-blogging services such as Twitter. Our premise is that truly mundane content is not interesting in any context, and thus can be quickly filtered using simple queryindependent features. Such a filter could be used for tiering indexes in a micro-blog search engine, with the filtered uninteresting content relegated to the less frequently accessed tiers.

59 citations


"Information credibility on twitter" refers background in this paper

  • ...In other words, we separate messages that are of potential interest to a broad set of people, from conversations that are of little importance outside a reduced circle of friends [2]....

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  • ...Many of the features follow previous works including [1, 2, 12, 26]....

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Journal Article
TL;DR: In this paper, the authors argue that most big companies should not rely on viral marketing to spread the word about their products and brands, and propose a new model called "Big Seed Marketing" that combines the power of traditional advertising with the extra punch provided by viral propagation.
Abstract: In spite of the recent popularity of viral marketing, we argue that most big companies should not rely on it to spread the word about their products and brands. Instead, we propose a new model called “Big Seed Marketing” that combines the power of traditional advertising with the extra punch provided by viral propagation. Between traditional advertising and viral marketing is an important gap that can be filled by big companies looking for an advantage in the market place and a better return on their advertising and marketing dollar.

56 citations

01 Dec 2009
TL;DR: This exploratory study examines the tweets that followed the 30 March 2009 Morgan Hill earthquake and investigates the possibility of using the tweets to detect seismic events and produce rapid maps of the felt area.

4 citations