Twitter under crisis

Mendoza, Marcelo; Poblete, Bárbara; Castillo, Carlos

doi:10.1145/1964858.1964869

Cited by 728 publications

(106 citation statements)

References 9 publications

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“…False rumors have affected stock prices and the motivation for large-scale investments, for example, wiping out $130 billion in stock value after a false tweet claimed that Barack Obama was injured in an explosion (7). Indeed, our responses to everything from natural disasters (8,9) to terrorist attacks (10) have been disrupted by the spread of false news online.…”

mentioning

confidence: 99%

The spread of true and false news online

Vosoughi

Roy

Aral

2018

Science

5,536

110

3,377

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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 bỹ 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.F oundational theories of decision-making (1-3), cooperation (4), communication (5), and markets (6) all view some conceptualization of truth or accuracy as central to the functioning of nearly every human endeavor. Yet, both true and false information spreads rapidly through online media. Defining what is true and false has become a common political strategy, replacing debates based on a mutually agreed on set of facts. Our economies are not immune to the spread of falsity either. False rumors have affected stock prices and the motivation for large-scale investments, for example, wiping out $130 billion in stock value after a false tweet claimed that Barack Obama was injured in an explosion (7). Indeed, our responses to everything from natural disasters (8, 9) to terrorist attacks (10) have been disrupted by the spread of false news online.New social technologies, which facilitate rapid information sharing and large-scale information cascades, can enable the spread of misinformation (i.e., information that is inaccurate or misleading). But although more and more of our access to information and news is guided by these new technologies (11), we know little about their contribution to the spread of falsity online. Though considerable attention has been paid to anecdotal analyses of the spread of false news by the media (12), there are few large-scale empirical investigations of the diffusion of misinformation or its social origins. Studies of the spread of misinformation are currently limited to analyses of small, ad hoc samples that ignore two of the most important scientific questions: How do truth and falsity diffuse differently, and what factors of human judgment explain these differences? Current work analyzes the spread of single rumors, like the discovery of the Higgs boson (13) or the Haitian earthquake of 2010 (14), and multiple rumors from a single disaster event, like t...

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confidence: 99%

The spread of true and false news online

Vosoughi

Roy

Aral

2018

Science

5,536

110

3,377

View full text Add to dashboard Cite

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“…studied Twitter user activities related to the 2010 earthquake in Chile (Mendoza et al, 2010). They reported that false rumors received significantly less re-tweets and confirming tweets, and more denying tweets and questioning tweets, when compared to confirmed truths.…”

Section: Credibility Of Micro-bloggingmentioning

confidence: 99%

Rethinking civic computing in China

Kou

Nardi

2016

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Civic computing research is concerned with the relationship of digital technologies to civic participation. We discuss Foucault’s work on the care of the self which considers how a person seeks a certain mode of being such as happiness, perfection, or wisdom. We describe our qualitative study of Chinese mainland citizens who used technologies to understand and participate in political events, in particular the Hong Kong Umbrella Movement. We examine how care of the self offers an alternative, critical perspective for rethinking civic participation and civic computing.

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“…Mendoza et al used the data from 2010 earthquake in Chile to explore the behavior of Twitter users for emergency response activity [18]. Their results showed that propagation of tweets related to rumors versus true news differed and could be used to develop automated classification solutions to identify correct information.…”

Section: Role Of Twitter During News Eventsmentioning

confidence: 99%

“…The credibility and quality of information often plays a critical role during high impact events. Fake news and rumors also propagate along with genuine news [18]. Researchers have shown that role of Twitter during mass convergence and emergency events differs considerably than more general Twitter activity [12].…”

Section: Introductionmentioning

confidence: 99%

Credibility ranking of tweets during high impact events

Gupta

Kumaraguru

2012

Proceedings of the 1st Workshop on Privacy and Security in Online Social Media

240

151

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Twitter has evolved from being a conversation or opinion sharing medium among friends into a platform to share and disseminate information about current events. Events in the real world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on Twitter is trustworthy or useful in providing information about the event. In this paper, we analyzed the credibility of information in tweets corresponding to fourteen high impact news events of 2011 around the globe. From the data we analyzed, on average 30% of total tweets posted about an event contained situational information about the event while 14% was spam. Only 17% of the total tweets posted about the event contained situational awareness information that was credible. Using regression analysis, we identified the important content and sourced based features, which can predict the credibility of information in a tweet. Prominent content based features were number of unique characters, swear words, pronouns, and emoticons in a tweet, and user based features like the number of followers and length of username. We adopted a supervised machine learning and relevance feedback approach using the above features, to rank tweets according to their credibility score. The performance of our ranking algorithm significantly enhanced when we applied re-ranking strategy. Results show that extraction of credible information from Twitter can be automated with high confidence.

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Twitter under crisis

Cited by 728 publications

References 9 publications

The spread of true and false news online

The spread of true and false news online

Rethinking civic computing in China

Credibility ranking of tweets during high impact events

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