Information credibility on twitter
28 Mar 2011-pp 675-684
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%.
Citations
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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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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 2016TL;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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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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25 Jul 2010
TL;DR: The behavior of Twitter users under an emergency situation is explored and it is shown that it is posible to detect rumors by using aggregate analysis on tweets, and that the propagation of tweets that correspond to rumors differs from tweets that spread news.
Abstract: In this article we explore the behavior of Twitter users under an emergency situation. In particular, we analyze the activity related to the 2010 earthquake in Chile and characterize Twitter in the hours and days following this disaster. Furthermore, we perform a preliminary study of certain social phenomenons, such as the dissemination of false rumors and confirmed news. We analyze how this information propagated through the Twitter network, with the purpose of assessing the reliability of Twitter as an information source under extreme circumstances. Our analysis shows that the propagation of tweets that correspond to rumors differs from tweets that spread news because rumors tend to be questioned more than news by the Twitter community. This result shows that it is posible to detect rumors by using aggregate analysis on tweets.
1,012 citations
TL;DR: The findings suggest that Twitter messages sent during these types of events contain more displays of information broadcasting and brokerage, and that general Twitter use seems to have evolved over time to offer more of an information-sharing purpose.
Abstract: This paper offers a descriptive account of Twitter (a microblogging service) across four high-profile, mass convergence events - two emergency and two national security. We statistically examine how Twitter is being used surrounding these events, and compare and contrast how that behaviour is different from more general Twitter use. Our findings suggest that Twitter messages sent during these types of events contain more displays of information broadcasting and brokerage, and we observe that general Twitter use seems to have evolved over time to offer more of an information-sharing purpose. We also provide preliminary evidence that Twitter users who join during and in apparent relation to a mass convergence or emergency event are more likely to become long-term adopters of the technology.
990 citations
06 Jun 2010
TL;DR: TwitterMonitor, a system that performs trend detection over the Twitter stream and provides meaningful analytics that synthesize an accurate description of each topic on Twitter in real time, is presented.
Abstract: We present TwitterMonitor, a system that performs trend detection over the Twitter stream The system identifies emerging topics (ie 'trends') on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic Users interact with the system by ordering the identified trends using different criteria and submitting their own description for each trend We discuss the motivation for trend detection over social media streams and the challenges that lie therein We then describe our approach to trend detection, as well as the architecture of TwitterMonitor Finally, we lay out our demonstration scenario
942 citations
"Information credibility on twitter" refers background or methods in this paper
...To do this, we ran an event supervised classi.er over the collection of 2,524 cases detected by Twitter Monitor....
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...3.1 Automatic event detection We use Twitter events detected by Twitter Monitor [18]7 during a 2-months period....
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...1 Automatic event detection We use Twitter events detected by Twitter Monitor [18](7) during a 2-months period....
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...[18] M. Mathioudakis and N. Koudas....
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...Recently Mathioudakis and Koudas [18] described an on-line monitoring system to perform trend detection over the Twitter stream....
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TL;DR: In this paper, the authors assess people's perceptions of the credibility of various categories of Internet information compared to similar information provided by other media and find that people increasingly rely on Internet and web-based information despite evidence that it is potentially inaccurate and biased.
Abstract: People increasingly rely on Internet and web-based information despite evidence that it is potentially inaccurate and biased. Therefore, this study sought to assess people's perceptions of the credibility of various categories of Internet information compared to similar information provided by other media. The 1,041 respondents also were asked about whether they verified Internet information. Overall, respondents reported they considered Internet information to be as credible as that obtained from television, radio, and magazines, but not as credible as newspaper information. Credibility among the types of information sought, such as news and entertainment, varied across media channels. Respondents said they rarely verified web-based information, although this too varied by the type of information sought. Levels of experience and how respondents perceived the credibility of information were related to whether they verified information. This study explores the social relevance of the findings and discusses...
932 citations
"Information credibility on twitter" refers background in this paper
...People trust the Internet as a news source as much as other media, with the exception of newspapers [8]....
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06 Feb 2010
TL;DR: A content-based categorization of the type of messages posted by Twitter users is developed, based on which the analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.
Abstract: In this work we examine the characteristics of social activity and patterns of communication on Twitter, a prominent example of the emerging class of communication systems we call "social awareness streams." We use system data and message content from over 350 Twitter users, applying human coding and quantitative analysis to provide a deeper understanding of the activity of individuals on the Twitter network. In particular, we develop a content-based categorization of the type of messages posted by Twitter users, based on which we examine users' activity. Our analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.
834 citations
"Information credibility on twitter" refers background in this paper
...While most messages on Twitter are conversation and chatter, people also use it to share relevant information and to report news [13, 22, 21]....
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