The spread of true and false news online

@article{Vosoughi2018TheSO,
  title={The spread of true and false news online},
  author={Soroush Vosoughi and Deb K. Roy and Sinan Aral},
  journal={Science},
  year={2018},
  volume={359},
  pages={1146 - 1151}
}
Lies spread faster than the truth There is worldwide concern over false news and the possibility that it can influence political, economic, and social well-being. To understand how false news spreads, Vosoughi et al. used a data set of rumor cascades on Twitter from 2006 to 2017. About 126,000 rumors were spread by ∼3 million people. False news reached more people than the truth; the top 1% of false news cascades diffused to between 1000 and 100,000 people, whereas the truth rarely diffused to… 
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References

SHOWING 1-10 OF 84 REFERENCES
Information credibility on twitter
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%.
The spreading of misinformation online
TLDR
A massive quantitative analysis of Facebook shows that information related to distinct narratives––conspiracy theories and scientific news––generates homogeneous and polarized communities having similar information consumption patterns, and derives a data-driven percolation model of rumor spreading that demonstrates that homogeneity and polarization are the main determinants for predicting cascades’ size.
Homophily and polarization in the age of misinformation
TLDR
It is found that the frequent (and selective) exposure to specific kind of content (polarization) is a good proxy for the detection of homophile clusters where certain kind of rumors are more likely to spread.
Rumor Gauge
TLDR
The ability to track rumors and predict their outcomes may have practical applications for news consumers, financial markets, journalists, and emergency services, and more generally to help minimize the impact of false information on Twitter.
Rumor has it: Identifying Misinformation in Microblogs
TLDR
This paper addresses the problem of rumor detection in microblogs and explores the effectiveness of 3 categories of features: content- based, network-based, and microblog-specific memes for correctly identifying rumors, and believes that its dataset is the first large-scale dataset on rumor detection.
Rumor Cascades
TLDR
It is found that rumor cascades run deeper in the social network than reshare cascades in general and that different variants tend to dominate different bursts in popularity.
Evaluating Event Credibility on Twitter
TLDR
A credibility analysis approach enhanced with event graph-based optimization to solve the problem of automatically assessing the credibility of popular Twitter events and shows that its methods are significantly more accurate than the decision tree classifier approach.
Political rumoring on Twitter during the 2012 US presidential election: Rumor diffusion and correction
TLDR
It was found that Twitter helped rumor spreaders circulate false information within homophilous follower networks, but seldom functioned as a self-correcting marketplace of ideas.
Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy
TLDR
The role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster was highlighted, and automated techniques can be used in identifying real images from fake images posted on Twitter.
Everyone's an influencer: quantifying influence on twitter
TLDR
It is concluded that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects and that predictions of which particular user or URL will generate large cascades are relatively unreliable.
...
...