Advertisement
GET OUR E-ALERTS
Report

The spread of true and false news online

Soroush Vosoughi https://orcid.org/0000-0002-2564-8909, Deb Roy, and Sinan Aral https://orcid.org/0000-0002-2762-058X [email protected]
Science • 9 Mar 2018 • Vol 359, Issue 6380 • pp. 1146-1151 • DOI: 10.1126/science.aap9559
get access

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.
Get full access to this article

View all available purchase options and get full access to this article.

Already a Subscriber?

Supplementary Material

Summary

Materials and Methods
Figs. S1 to S20
Tables S1 to S39
References (37–75)

Resources

File (aap9559_vosoughi_sm.pdf)
Download

References and Notes

1
L. J. Savage, The theory of statistical decision. J. Am. Stat. Assoc. 46, 55–67 (1951).
2
H. A. Simon, The New Science of Management Decision (Harper & Brothers Publishers, New York, 1960).
3
R. Wedgwood, The aim of belief. Noûs 36, 267–297 (2002).
4
E. Fehr, U. Fischbacher, The nature of human altruism. Nature 425, 785–791 (2003).
5
C. E. Shannon, A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).
6
S. Bikhchandani, D. Hirshleifer, I. Welch, A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econ. 100, 992–1026 (1992).
7
K. Rapoza, “Can ‘fake news’ impact the stock market?” Forbes, 26 February 2017; www.forbes.com/sites/kenrapoza/2017/02/26/can-fake-news-impact-the-stock-market/.
8
M. Mendoza, B. Poblete, C. Castillo, “Twitter under crisis: Can we trust what we RT?” in Proceedings of the First Workshop on Social Media Analytics (Association for Computing Machinery, ACM, 2010), pp. 71–79.
9
A. Gupta, H. Lamba, P. Kumaraguru, A. Joshi, “Faking Sandy: Characterizing and identifying fake images on Twitter during Hurricane Sandy,” in Proceedings of the 22nd International Conference on World Wide Web (ACM, 2010), pp. 729–736.
10
K. Starbird, J. Maddock, M. Orand, P. Achterman, R. M. Mason, “Rumors, false flags, and digital vigilantes: Misinformation on Twitter after the 2013 Boston Marathon bombing,” in iConference 2014 Proceedings (iSchools, 2014).
11
J. Gottfried, E. Shearer, “News use across social media platforms,” Pew Research Center, 26 May 2016; www.journalism.org/2016/05/26/news-use-across-social-media-platforms-2016/.
12
C. Silverman, “This analysis shows how viral fake election news stories outperformed real news on Facebook,” BuzzFeed News, 16 November 2016; www.buzzfeed.com/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook/.
13
M. De Domenico, A. Lima, P. Mougel, M. Musolesi, The anatomy of a scientific rumor. Sci. Rep. 3, 2980 (2013).
14
O. Oh, K. H. Kwon, H. R. Rao, “An exploration of social media in extreme events: Rumor theory and Twitter during the Haiti earthquake 2010,” in Proceedings of the International Conference on Information Systems (International Conference on Information Systems, ICIS, paper 231, 2010).
15
M. Tambuscio, G. Ruffo, A. Flammini, F. Menczer, “Fact-checking effect on viral hoaxes: A model of misinformation spread in social networks,” in Proceedings of the 24th International Conference on World Wide Web (ACM, 2015), pp. 977–982.
16
Z. Zhao, P. Resnick, Q. Mei, “Enquiring minds: Early detection of rumors in social media from enquiry posts,” in Proceedings of the 24th International Conference on World Wide Web (ACM, 2015), pp. 1395–1405.
17
M. Gupta, P. Zhao, J. Han, “Evaluating event credibility on Twitter,” in Proceedings of the 2012 Society for Industrial and Applied Mathematics International Conference on Data Mining (Society for Industrial and Applied Mathematics, SIAM, 2012), pp. 153–164.
18
G. L. Ciampaglia, P. Shiralkar, L. M. Rocha, J. Bollen, F. Menczer, A. Flammini, Computational fact checking from knowledge networks. PLOS ONE 10, e0128193 (2015).
19
A. Friggeri, L. A. Adamic, D. Eckles, J. Cheng, “Rumor cascades,” in Proceedings of the International Conference on Weblogs and Social Media (Association for the Advancement of Artificial Intelligence, AAAI, 2014)
20
M. Del Vicario, A. Bessi, F. Zollo, F. Petroni, A. Scala, G. Caldarelli, H. E. Stanley, W. Quattrociocchi, The spreading of misinformation online. Proc. Natl. Acad. Sci. U.S.A. 113, 554–559 (2016).
21
A. Bessi, M. Coletto, G. A. Davidescu, A. Scala, G. Caldarelli, W. Quattrociocchi, Science vs conspiracy: Collective narratives in the age of misinformation. PLOS ONE 10, e0118093 (2015).
22
Friggeri et al. (19) do evaluate two metrics of diffusion: depth, which shows little difference between true and false rumors, and shares per rumor, which is higher for true rumors than it is for false rumors. Although these results are important, they are not definitive owing to the smaller sample size of the study; the early timing of the sample, which misses the rise of false news after 2013; and the fact that more shares per rumor do not necessarily equate to deeper, broader, or more rapid diffusion.
23
S. Goel, A. Anderson, J. Hofman, D. J. Watts, The structural virality of online diffusion. Manage. Sci. 62, 180–196 (2015).
24
L. Itti, P. Baldi, Bayesian surprise attracts human attention. Vision Res. 49, 1295–1306 (2009).
25
S. Aral, M. Van Alstyne, The diversity-bandwidth trade-off. Am. J. Sociol. 117, 90–171 (2011).
26
J. Berger, K. L. Milkman, What makes online content viral? J. Mark. Res. 49, 192–205 (2012).
27
D. M. Blei, A. Y. Ng, M. I. Jordan, Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003).
28
S. Aral, P. Dhillon, “Unpacking novelty: The anatomy of vision advantages,” Working paper, MIT–Sloan School of Management, Cambridge, MA, 22 June 2016; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2388254.
29
T. M. Cover, J. A. Thomas, Elements of Information Theory (Wiley, 2012).
30
T. Kailath, The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Commun. Technol. 15, 52–60 (1967).
31
R. Plutchik, The nature of emotions. Am. Sci. 89, 344–350 (2001).
32
S. M. Mohammad, P. D. Turney, Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29, 436–465 (2013).
33
S. M. Mohammad, S. Kiritchenko, Using hashtags to capture fine emotion categories from tweets. Comput. Intell. 31, 301–326 (2015).
34
S. Vosoughi, D. Roy, “A semi-automatic method for efficient detection of stories on social media,” in Proceedings of the 10th International AAAI Conference on Weblogs and Social Media (AAAI, 2016), pp. 707–710.
35
C. A. Davis, O. Varol, E. Ferrara, A. Flammini, F. Menczer, “BotOrNot: A system to evaluate social bots,” in Proceedings of the 25th International Conference Companion on World Wide Web (ACM, 2016), pp. 273–274.
36
For example, this is an argument made in recent testimony by Clint Watts—Robert A. Fox Fellow at the Foreign Policy Research Institute and Senior Fellow at the Center for Cyber and Homeland Security at George Washington University—given during the U.S. Senate Select Committee on Intelligence hearing on “Disinformation: A Primer in Russian Active Measures and Influence Campaigns” on 30 March 2017; www.intelligence.senate.gov/sites/default/files/documents/os-cwatts-033017.pdf.
37
D. Trpevski, W. K. Tang, L. Kocarev, Model for rumor spreading over networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 81, 056102 (2010).
38
B. Doerr, M. Fouz, T. Friedrich, Why rumors spread so quickly in social networks. Commun. ACM 55, 70–75 (2012).
39
F. Jin, E. Dougherty, P. Saraf, Y. Cao, N. Ramakrishnan, “Epidemiological modeling of news and rumors on Twitter,” in Proceedings of the 7th Workshop on Social Network Mining and Analysis (ACM, 2013).
40
J. Cheng, L. A. Adamic, J. M. Kleinberg, J. Leskovec, “Do cascades recur?” in Proceedings of the 25th International Conference on World Wide Web (ACM, 2016).
41
V. Qazvinian, E. Rosengren, D. R. Radev, Q. Mei, “Rumor has it: Identifying misinformation in microblogs,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, ACL, 2011).
42
S. Vosoughi, M. Mohsenvand, D. Roy, Rumor gauge: Predicting the veracity of rumors on Twitter. ACM Trans. Knowl. Discov. Data 11, 50 (2017).
43
W. Xu, H. Chen, “Scalable rumor source detection under independent cascade model in online social networks,” in 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN) (IEEE, 2015).
44
T. Takahashi, N. Igata, “Rumor detection on Twitter,” in 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS) (IEEE, 2012).
45
C. Castillo, M. Mendoza, B. Poblete, “Information credibility on Twitter,” in Proceedings of the 20th International Conference on World Wide Web (ACM, 2011).
46
R. M. Tripathy, A. Bagchi, S. Mehta, “A study of rumor control strategies on social networks,” in Proceedings of the 19th ACM International Conference on Information and Knowledge Management (ACM, 2010).
47
J. Shin, L. Jian, K. Driscoll, F. Bar, Political rumoring on Twitter during the 2012 U.S. presidential election: Rumor diffusion and correction. New Media Soc. 19, 1214–1235 (2017).
48
P. Ozturk, H. Li, Y. Sakamoto, “Combating rumor spread on social media: The effectiveness of refutation and warning,” in 2015 48th Hawaii International Conference on System Sciences (HICSS) (IEEE, 2015).
49
A. Bessi, F. Petroni, M. Del Vicario, F. Zollo, A. Anagnostopoulos, A. Scala, G. Caldarelli, W. Quattrociocchi, Homophily and polarization in the age of misinformation. Eur. Phys. J. Spec. Top. 225, 2047–2059 (2016).
50
A. Bessi, A. Scala, L. Rossi, Q. Zhang, W. Quattrociocchi, . The economy of attention in the age of (mis)information. J. Trust Manage. 1, 12 (2014).
51
A. Mitchell, J. Gottfried, J. Kiley, K. E. Matsa, “Political polarization & media habits,” Pew Research Center; www.journalism.org/2014/10/21/political-polarization-media-habits/.
52
J. L. Fleiss, Measuring nominal scale agreement among many raters. Psychol. Bull. 76, 378–382 (1971).
53
Q. Le, T. Mikolov, “Distributed representations of sentences and documents,” in Proceedings of the 31st International Conference on Machine Learning (ICML-14) (Journal of Machine Learning Research, 2014).
54
S. Vosoughi, P. Vijayaraghavan, D. Roy, “Tweet2vec: Learning tweet embeddings using character-level cnn-lstm encoder-decoder,” in Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM, 2016).
55
C. A. Davis, O. Varol, E. Ferrara, A. Flammini, F. Menczer, “Botornot: A system to evaluate social bots,” in Proceedings of the 25th International Conference Companion on World Wide Web (ACM, 2016).
56
J. Maddock, K. Starbird, R. M. Mason, “Using historical Twitter data for research: Ethical challenges of tweet deletions,” in CSCW 2015 Workshop on Ethics for Studying Sociotechnical Systems in a Big Data World (ACM, 2015).
57
S. Goel, D. J. Watts, D. G. Goldstein, “The structure of online diffusion networks,” in Proceedings of the 13th ACM conference on Electronic Commerce (ACM, 2012).
58
J. M. Wooldridge, Cluster-sample methods in applied econometrics. Am. Econ. Rev. 93, 133–138 (2003).
59
A. C. Cameron, D. L. Miller, A practitioner’s guide to cluster-robust inference. J. Hum. Resour. 50, 317–372 (2015).
60
P. Vijayaraghavan, S. Vosoughi, D. Roy, “Twitter demographic classification using deep multi-modal multi-task learning,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL) (Volume 2: Short Papers) (ACL, 2017).
61
A. Gupta, H. Lamba, P. Kumaraguru, A. Joshi, “Faking Sandy: Characterizing and identifying fake images on Twitter during Hurricane Sandy,” in Proceedings of the 22nd International Conference on World Wide Web (ACM, 2013).
62
S. M. Mohammad, P. D. Turney, “Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon,” in Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text (ACL, 2010).
63
S. M. Mohammad, “# emotional tweets,” in Proceedings of the First Joint Conference on Lexical and Computational Semantics (ACL, 2012).
64
S. Bird, E. Klein, E. Loper, Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit (O’Reilly Media, ed. 1, 2009).
65
J. W. Pennebaker, M. E. Francis, R. J. Booth, Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates 71, 2001 (2001).
66
M. Mendoza, B. Poblete, C. Castillo, “Twitter under crisis: Can we trust what we RT?” in Proceedings of the First Workshop on Social Media Analytics (ACM, 2010).
67
L. Zeng, K. Starbird, E. S. Spiro, “Rumors at the speed of light? Modeling the rate of rumor transmission during crisis,” in 2016 49th Hawaii International Conference on System Sciences (HICSS) (IEEE, 2016).
68
W. X. Zhao, J. Jiang, J. Weng, J. He, E.-P. Lim, H. Yan, X. Li, “Comparing Twitter and traditional media using topic models,” in European Conference on Information Retrieval (ECIR) (ECIR, 2011).
69
S. Aral, P. Dhillon, “Unpacking novelty: The anatomy of vision advantages,” Working paper, MIT–Sloan School of Management, Cambridge, MA, 22 June 2016.
70
T. M. Cover, J. A. Thomas, Elements of Information Theory (Wiley, ed. 2, 2012).
71
S. Kullback, R. A. Leibler, On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951).
72
V. D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).
73
S. Vosoughi, D. Roy, “A semi-automatic method for efficient detection of stories on social media,” in 10th International AAAI Conference on Web and Social Media (AAAI, 2016).
74
E. Ferrara, O. Varol, C. Davis, F. Menczer, A. Flammini, The rise of social bots. Commun. ACM 59, 96–104 (2016).
75
A. Almaatouq, E. Shmueli, M. Nouh, A. Alabdulkareem, V. K. Singh, M. Alsaleh, A. Alarifi, A. Alfaris, A. Pentland, If it looks like a spammer and behaves like a spammer, it must be a spammer: Analysis and detection of microblogging spam accounts. Int. J. Inf. Secur. 15, 475–491 (2016).

Information & Authors

Information

Published In

Science
Volume 359 • Issue 6380 • 9 March 2018
Pages: 1146 - 1151
PubMed: 29590045

History

Received: 14 September 2017
Accepted: 19 January 2018

Copyright

Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
This is an article distributed under the terms of the Science Journals Default License.

Permissions

Request permissions for this article.

Authors

Affiliations

Soroush Vosoughi https://orcid.org/0000-0002-2564-8909
Massachusetts Institute of Technology (MIT), the Media Lab, E14-526, 75 Amherst Street, Cambridge, MA 02142, USA.
View all articles by this author
Deb Roy
Massachusetts Institute of Technology (MIT), the Media Lab, E14-526, 75 Amherst Street, Cambridge, MA 02142, USA.
View all articles by this author
Sinan Aral* https://orcid.org/0000-0002-2762-058X [email protected]
MIT, E62-364, 100 Main Street, Cambridge, MA 02142, USA.
View all articles by this author

Notes

*Corresponding author. Email: [email protected]

Funding Information

Twitter, Inc.:

Metrics & Citations

Metrics

Citations

View Options

Media

Figures

Other

Tables

Share

Information & Authors
InformationAuthors
Published In
issue cover image
Science
Volume 359|Issue 6380
9 March 2018
Submission history
Received:14 September 2017
Accepted:19 January 2018
Published in print:9 March 2018
Metrics & Citations
MetricsCitations
Article Usage
Altmetrics
Export citation

Select the format you want to export the citation of this publication.

Cited by
    • Akrati Saxena,
    • Pratishtha Saxena,
    • Harita Reddy,
    Fake News Detection Techniques for Social Media, Principles of Social Networking, (325-354), (2022).https://doi.org/10.1007/978-981-16-3398-0_15
    Crossref
    • Naveen Arora,
    Misinformation, Fake News and Rumor Detection, Principles of Social Networking, (307-324), (2022).https://doi.org/10.1007/978-981-16-3398-0_14
    Crossref
    • Maria Prosperina Vitale,
    • Maria Carmela Catone,
    • Ilaria Primerano,
    • Giuseppe Giordano,
    Unveiling Network Data Patterns in Social Media, Handbook of Research on Advanced Research Methodologies for a Digital Society, (571-588), (2022).https://doi.org/10.4018/978-1-7998-8473-6.ch033
    Crossref
    • Adam T. Ford,
    • Abdullahi H. Ali,
    • Sheila R. Colla,
    • Steven J. Cooke,
    • Clayton T. Lamb,
    • Jeremy Pittman,
    • David S. Shiffman,
    • Navinder J. Singh,
    Understanding and avoiding misplaced efforts in conservation, FACETS, 6, 1, (252-271), (2021).https://doi.org/10.1139/facets-2020-0058
    Crossref
    • Manos Tsakiris,
    How should the political animals of the 21st century feel?, Physics of Life Reviews, 36, (77-79), (2021).https://doi.org/10.1016/j.plrev.2020.06.008
    Crossref
    • Josh Compton,
    • Sander Linden,
    • John Cook,
    • Melisa Basol,
    Inoculation theory in the post‐truth era: Extant findings and new frontiers for contested science, misinformation, and conspiracy theories, Social and Personality Psychology Compass, 15, 6, (2021).https://doi.org/10.1111/spc3.12602
    Crossref
    • Derek K. Tracy,
    • Dan W. Joyce,
    • Dawn N. Albertson,
    • Sukhwinder S. Shergill,
    Kaleidoscope, The British Journal of Psychiatry, 218, 5, (293-294), (2021).https://doi.org/10.1192/bjp.2021.41
    Crossref
    • Claudia Wagner,
    • Markus Strohmaier,
    • Alexandra Olteanu,
    • Emre Kıcıman,
    • Noshir Contractor,
    • Tina Eliassi-Rad,
    Measuring algorithmically infused societies, Nature, 595, 7866, (197-204), (2021).https://doi.org/10.1038/s41586-021-03666-1
    Crossref
    • Jevin D. West,
    • Carl T. Bergstrom,
    Misinformation in and about science, Proceedings of the National Academy of Sciences, 118, 15, (e1912444117), (2021).https://doi.org/10.1073/pnas.1912444117
    Crossref
    • Marta Pérez-Escolar,
    • Eva Ordóñez-Olmedo,
    • Purificación Alcaide-Pulido,
    Fact-Checking Skills And Project-Based Learning About Infodemic And Disinformation, Thinking Skills and Creativity, 41, (100887), (2021).https://doi.org/10.1016/j.tsc.2021.100887
    Crossref
  11. See more
Share
Share article link

Share on social media
Get Access
Log in to view the full text

AAAS Log in

AAAS login provides access to Science for AAAS members, and access to other journals in the Science family to users who have purchased individual subscriptions, as well as limited access for those who register for access.

Log in via OpenAthens.
via OpenAthens
Log in via Shibboleth.
via Shibboleth
More options

Purchase digital access to this article

Download and print this article for your personal scholarly, research, and educational use.

Purchase this issue in print

Buy a single issue of Science for just $15 USD.

View Options
Get Access
Tables
Media
FiguresMultimedia
References

(0)eLetters

No eLetters have been published for this article yet.

eLetters is an online forum for ongoing peer review. Submission of eLetters are open to all. eLetters are not edited, proofread, or indexed. Please read our Terms of Service before submitting your own eLetter.

A future challenge: How will information that accuracy changes is distributed?
Shino Iwami
  • Researcher

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)
Yoshiyasu Takefuji
  • Professor
  • Keio University

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

Current Issue

Go to Science
Table of Contents

LATEST NEWS