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An anatomical comparison of fake-news and trusted-news sharing pattern on Twitter

  • S.I.: FakeNews
  • Published:
Computational and Mathematical Organization Theory Aims and scope Submit manuscript

Abstract

Online social networks allow users to share a variety of multi-media content on the World Wide Web. The rising popularity of such social networking platforms coupled with limitations in verifying the veracity of shared content has contributed to increase in misinformation on these media. Misinformation content such as fake-news and hoaxes, though often considered innocuous, may have high social cost such as influencing elections decision, and thus should be investigated carefully. Many researchers have studied various aspects of fake-news including automated ways to recognize it. However, a large-scale study comparing the sharing patterns of fake-news and trusted-news is missing. In this research, we take Ukraine, a country where fake news is common, as a case study. Using datasets generated by three different Tweets collection strategies, we present an anatomical comparison of fake-news and trusted-news sharing pattern on Twitter. Such a comparison enables to identify the characteristics of tweets sharing fake-news, and allows to find the users who are more inclined to share misinformation. Besides, we also study possible bot activities in the dataset. The top conclusions derived from this study are (a) Users sharing fake-news stories are more likely to include hashtags, and the hashtags used in Tweets sharing fake-news stories are similar to hashtags used in Tweets sharing trusted news. (b) Users sharing fake-news are also more likely to include mentions, but mentions used in tweets sharing fake-news and trusted-news are often different. (c) Tweets sharing fake-news have more negative sentiment. In contrast, tweets sharing trusted-news have more positive sentiment.

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Notes

  1. https://github.com/sumeetkr/AwesomeFakeNews.

  2. https://www.washingtonpost.com/news/monkey-cage/wp/2013/12/04/strategic-use-of-facebook-and-twitter-in-ukrainian-protests/.

  3. http://www.stopfake.org/en.

  4. http://rt.com.

  5. http://lifenews.ru or http://life.ru.

  6. http://izvestia.ru.

  7. http://ukraina.ru.

  8. http://x-true.info.

  9. http://odnoklassniki.ru.

  10. https://www.forbes.com/sites/berlinschoolofcreativeleadership/2017/02/01/10-journalism-brands-where-you-will-find-real-facts-rather-than-alternative-facts.

  11. http://www.nltk.org/api/nltk.tokenize.html.

  12. https://github.com/cjhutto/vaderSentiment.

  13. https://github.com/IUNetSci/botometer-python.

References

  • Adamic LA, Huberman BA (2000) Power-law distribution of the World Wide Web. Science 287(5461):2115–2115

    Article  Google Scholar 

  • Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211–36

    Article  Google Scholar 

  • Alstott J, Bullmore E, Plenz D (2014) powerlaw: a python package for analysis of heavy-tailed distributions. PLoS ONE 9(1):e85,777

    Article  Google Scholar 

  • Bertot JC, Jaeger PT, Hansen D (2012) The impact of polices on government social media usage: issues, challenges, and recommendations. Gov Inf Q 29(1):30–40

    Article  Google Scholar 

  • Buccafurri F, Lax G, Nocera A, Ursino D (2012) Discovering links among social networks. In: Joint European conference on machine learning and knowledge discovery in databases, Springer, pp 467–482

  • Carley KM (2014) Ora: a toolkit for dynamic network analysis and visualization. In: Alhajj R, Rokne J (eds) Encyclopedia of social network analysis and mining. Springer, New York, pp 1219–1228

    Chapter  Google Scholar 

  • Chu Z, Gianvecchio S, Wang H, Jajodia S (2010) Who is tweeting on Twitter: human, bot, or cyborg? In: Proceedings of the 26th annual computer security applications conference, ACM, pp 21–30

  • Clauset A, Shalizi CR, Newman ME (2009) Power-law distributions in empirical data. SIAM Rev 51(4):661–703

    Article  Google Scholar 

  • Conroy NJ, Rubin VL, Chen Y (2015) Automatic deception detection: methods for finding fake news. Proc Assoc Inf Sci Technol 52(1):1–4

    Article  Google Scholar 

  • Davis CA, Varol O, Ferrara E, Flammini A, Menczer F (2016) Botornot: A system to evaluate social bots. In: Proceedings of the 25th international conference companion on world wide web, international World Wide Web Conferences Steering Committee, pp 273–274

  • Davoust A, Floyd MW, Esfandiari B (2008) Use of fuzzy histograms to model the spatial distribution of objects in case-based reasoning. In: Conference of the Canadian Society for Computational Studies of Intelligence, Springer, pp 72–83

  • Dickerson JP, Kagan V, Subrahmanian V (2014) Using sentiment to detect bots on twitter: Are humans more opinionated than bots? In: 2014 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 620–627

  • Du N, Wu B, Pei X, Wang B, Xu L (2007) Community detection in large-scale social networks. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, ACM, pp 16–25

  • Ellison NB, Steinfield C, Lampe C (2007) The benefits of Facebook “friends:” social capital and college students’ use of online social network sites. J Comput Mediat Commun 12(4):1143–1168

    Article  Google Scholar 

  • Ferrara E (2015) Manipulation and abuse on social media. ACM SIGWEB Newsletter (Spring), New York

    Google Scholar 

  • Ferrara E, Varol O, Davis C, Menczer F, Flammini A (2014) The rise of social bots. arXiv:1407.5225

  • Hassan N, Li C, Tremayne M (2015) Detecting check-worthy factual claims in presidential debates. In: Proceedings of the 24th ACM international on conference on information and knowledge management, ACM, pp 1835–1838

  • Hutto, CJ, Gilbert E (2014) Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth international AAAI conference on weblogs and social media

  • Ioffe S (2010) Improved consistent sampling, weighted minhash and l1 sketching. In: 2010 IEEE 10th international conference on data mining (ICDM), IEEE, pp 246–255

  • Jin Z, Cao J, Zhang Y, Luo J (2016) News verification by exploiting conflicting social viewpoints in microblogs. In: AAAI, pp 2972–2978

  • Khaldarova I, Pantti M (2016) Fake news: the narrative battle over the Ukrainian conflict. Journal Pract 10(7):891–901

    Google Scholar 

  • Kumar S, Carley KM (2016) Approaches to understanding the motivations behind cyber attacks. In: 2016 IEEE international conference on intelligence and security informatics (ISI), Tucson, Arizona USA

  • Kumar S, Carley KM (2018) People2vec: Learning latent representations of users using their social-media activities. In: international conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation, Springer, pp 154–163

  • Lee K, Eoff BD, Caverlee J (2011) Seven months with the devils: a long-term study of content polluters on twitter. In: ICWSM

  • Morstatter F, Wu L, Nazer TH, Carley KM, Liu H (2016) A new approach to bot detection: striking the balance between precision and recall. In: 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 533–540

  • Newman N, Fletcher R, Kalogeropoulos A, Levy DA, Nielsen RK (2017) Reuters institute digital news report 2017

  • Parkinson HJ (2016) Click and elect: how fake news helped Donald Trump win a real election. The Guardian

  • Pfeffer J, Zorbach T, Carley KM (2014) Understanding online firestorms: negative word-of-mouth dynamics in social media networks. J Market Commun 20(1–2):117–128

    Article  Google Scholar 

  • Rashkin H, Choi E, Jang JY, Volkova S, Choi Y (2017) Truth of varying shades: analyzing language in fake news and political fact-checking. In: proceedings of the 2017 conference on empirical methods in natural language processing, pp 2921–2927

  • Rubin VL, Lukoianova T (2015) Truth and deception at the rhetorical structure level. J Assoc Inf Sci Technol 66(5):905–917

    Article  Google Scholar 

  • Rubin V, Conroy N, Chen Y, Cornwell S (2016) Fake news or truth? Using satirical cues to detect potentially misleading news. In: proceedings of the second workshop on computational approaches to deception detection, pp 7–17

  • Samanth S (2017) Inside the macedonian fake-news complex. Wired.com. https://www.wired.com/2017/02/veles-macedonia-fake-news/

  • Schifferes S, Newman N, Thurman N, Corney D, Göker A, Martin C (2014) Identifying and verifying news through social media: developing a user-centred tool for professional journalists. Digit Journal 2(3):406–418

    Google Scholar 

  • Shao C, Ciampaglia GL, Varol O, Flammini A, Menczer F (2017) The spread of fake news by social bots. arXiv:1707.07592

  • Shi B, Weninger T (2016) Fact checking in heterogeneous information networks. In: Proceedings of the 25th international conference companion on World Wide Web, International World Wide Web Conferences Steering Committee, pp 101–102

  • Shu K, Wang S, Sliva A, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. arXiv:1708.01967

  • Tacchini E, Ballarin G, Della Vedova ML, Moret S, de Alfaro L (2017) Some like it hoax: automated fake news detection in social networks. arXiv:1704.07506

  • Vargo CJ, Guo L, Amazeen MA (2017) The agenda-setting power of fake news: a big data analysis of the online media landscape from 2014 to 2016. New Media Soc 20:2028–2049

    Article  Google Scholar 

  • Volkova S, Shaffer K, Jang JY, Hodas N (2017) Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 2: Short Papers), vol 2, pp 647–653

  • Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146–1151. https://doi.org/10.1126/science.aap9559

    Article  Google Scholar 

  • Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, Su L, Gao J (2018) Eann: event adversarial neural networks for multi-modal fake news detection

Download references

Funding

Funding was provided by MURI (Grant Nos. N000140811186 and N000141712675).

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Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

    Sumeet Kumar, Binxuan Huang, Ramon Alfonso Villa Cox & Kathleen M. Carley

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  1. Sumeet Kumar
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  2. Binxuan Huang
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  3. Ramon Alfonso Villa Cox
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  4. Kathleen M. Carley
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Correspondence to Sumeet Kumar or Kathleen M. Carley.

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Kumar, S., Huang, B., Cox, R.A.V. et al. An anatomical comparison of fake-news and trusted-news sharing pattern on Twitter. Comput Math Organ Theory 27, 109–133 (2021). https://doi.org/10.1007/s10588-019-09305-5

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