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The spread of true and false news online
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 more than 1000 people. Falsehood also diffused faster than the truth. The degree of novelty and the emotional reactions of recipients may be responsible for the differences observed.
Science, this issue p. 1146
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.
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Science
Volume 359 | Issue 6380
9 March 2018
9 March 2018
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Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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Received: 14 September 2017
Accepted: 19 January 2018
Published in print: 9 March 2018
Acknowledgments
We are indebted to Twitter for providing funding and access to the data. We are also grateful to members of the MIT research community for invaluable discussions. The research was approved by the MIT institutional review board. The analysis code is freely available at https://goo.gl/forms/AKIlZujpexhN7fY33. The entire data set is also available, from the same link, upon signing an access agreement stating that (i) you shall only use the data set for the purpose of validating the results of the MIT study and for no other purpose; (ii) you shall not attempt to identify, reidentify, or otherwise deanonymize the data set; and (iii) you shall not further share, distribute, publish, or otherwise disseminate the data set. Those who wish to use the data for any other purposes can contact and make a separate agreement with Twitter.
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A future challenge: How will information that accuracy changes is distributed?
Soroush Vosoughi et al. revealed that false news spreads faster than true news (1).
Meanwhile, since the Great East Japan Earthquake in 2011, Twitter has been used for rescue requests by disasters. Those tweets tend to be spread by a third party, but it is also pointed out that the original tweet becomes difficult to be found in a large amount of re-tweets, and the non-editable tweet after the rescue induces a confusion. In other words, even though it is true information at the beginning, it turns into less accurate information or false information later. This is a different pattern from mixed information, and the visualization (2) may be drawn with a video.
Nowadays, Twitter Japan is guiding to delete the original tweets after rescue (3).
References:
1. S. Vosoughi, D. Roy, S. Aral, The spread of true and false news online. Science (80-. ). 359, 1146–1151 (2018).
2. Cover stories: Visualizing the spread of true and false news on social media. Science (80-. ). 359, eaat4382 (2018).
3. Twitter Japan, An example of rescue request (In Japanese) (2015), (available at https://twitter.com/TwitterLifeline/status/642206749265518592).
Based on Shannon's measure of information content, rare messages (lies) are more rapidly spread than non-rare messages (the truth)
Soroush Vosoughi et al. reported that lies spread faster than the truth (1). Shannon (2) derived a measure of information content called "surprisal" of a message m:
Information(m)=-log_2 P(m)=log_2 (1/(P(m)))
Shannon derivation states that rare messages are more informative than non-rare messages. In other words, we tend to retweet more rare messages than non-rare messages. If false news are more rare, then we will retweet them. If true news are more rare, then we will retweet them. It is obvious in twitter messages that we tend to retweet "surprisal" of a message. Based on Shannon's measure of information content, rare messages (lies) are more rapidly spread than non-rare messages (the truth). Shannon has already predicted their conclusion.
References:
1. Soroush Vosoughi et al.," The spread of true and false news online," Science 09 Mar 2018: Vol. 359, Issue 6380, pp. 1146-1151
2. https://en.wikipedia.org/wiki/Quantities_of_information