research-article

Experience: Managing Misinformation in Social Media—Insights for Policymakers from Twitter Analytics

Authors:

Arpan Kumar Kar,

P. Vigneswara IlavarasanAuthors Info & Claims

Journal of Data and Information Quality (JDIQ), Volume 12, Issue 1

Article No.: 6, Pages 1 - 18

https://doi.org/10.1145/3341107

Published: 16 November 2019 Publication History

Abstract

Governance of misinformation is a serious concern in social media platforms. Based on experiences gathered from different case studies, we offer insights for the policymakers on managing misinformation in social media. These platforms are widely used for not just communication but also content consumption. Managing misinformation is thus a challenge for policymakers and the platforms. This article explores the factors of rapid propagation of misinformation based on our experiences in the domain. An average of about 1.5 million tweets were analysed in each of the three different cases surrounding misinformation. The findings indicate that the tweet emotion and polarity plays a significant role in determining whether the shared content is authentic or not. A deeper exploration highlights that a higher element of surprise combined with other emotions is present in such tweets. Further, the tweets that show case-neutral content often lack the possibilities of virality when it comes to misinformation. The second case explores whether the misinformation is being propagated intentionally by means of the identified fake profiles or it is done by authentic users, which can also be either intentional, for gaining attention, or unintentional, under the assumption that the information is correct. Last, network attributes, including topological analysis, community, and centrality analysis, also catalyze the propagation of misinformation. Policymakers can utilize these findings in this experience study for the governance of misinformation. Tracking and disruption in any one of the identified drivers could act as a control mechanism to manage misinformation propagation.

References

[1]

D. M. Romero, W. Galuba, S. Asur, and B. A. Huberman. 2011. Influence and passivity in social media. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, 18--33.

[2]

S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang. 2013. Prominent features of rumor propagation in online social media. In Proceedings of the IEEE 13th International Conference on Data Mining (ICDM’13). IEEE, 1103--1108.

[3]

K. Hill. 2012. Hurricane Sandy, @ComfortablySmug, and the Flood of Social Media Misinformation. Retrieved from http://www.forbes.com/sites/kashmirhill/2012/10/30/hurricane-sandy-and-the-flood-of social-media-misinformation/.

[4]

E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne. 2008. Finding high-quality content in social media. In Proceedings of the International Conference on Web Search and Data Mining. ACM, 183--194.

[5]

C. Castillo, M. Mendoza, and B. Poblete. 2011. Information credibility on Twitter. In Proceedings of the 20th International Conference on World Wide Web. ACM, 675--684.

[6]

F. Jin, W. Wang, L. Zhao, E. Dougherty, Y. Cao, C. T. Lu, and N. Ramakrishnan. 2014. Misinformation propagation in the age of Twitter. Computer 47, 12 (2014), 90--94.

Digital Library

[7]

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

[8]

A. Gupta, H. Lamba, and P. Kumaraguru. 2013a. &dollar;1.00 per rt# bostonmarathon# prayforboston: Analyzing fake content on Twitter. In Proceedings of the eCrime Researchers Summit (eCRS’13). IEEE, 1--12.

[9]

K. Starbird, J. Maddock, M. Orand, P. Achterman, and R. M. Mason. 2014. Rumors, false flags, and digital vigilantes: Misinformation on Twitter after the 2013 Boston marathon bombing. In Proceedings of the iConference.

[10]

S. O. Oyeyemi, E. Gabarron, and R. Wynn. 2014. Ebola, Twitter, and misinformation: A dangerous combination? Brit. Med. J. 349 (2014), 61--78.

[11]

R. Ehrenberg. 2012. Social media sway: Worries over political misinformation on Twitter attract scientists' attention. Sci. News 182, 8 (2012), 22--25.

[12]

O. Oh, K. H. Kwon, and H. R. Rao. 2010. An exploration of social media in extreme events: Rumor theory and Twitter during the Haiti earthquake 2010. In Proceedings of the International Conference on Computer and Information Science (ICIS’10). 231.

[13]

A. Gupta, H. Lamba, P. Kumaraguru, and A. Joshi. 2013b. Faking Sandy: Characterizing and identifying fake images on Twitter during Hurricane Sandy. In Proceedings of the 22nd International Conference on World Wide Web. ACM, 729--736.

[14]

J. Shin, L. Jian, K. Driscoll, and F. Bar. 2018. The diffusion of misinformation on social media: Temporal pattern, message, and source. Comput. Hum. Behav. 83 (2018), 278--287.

Digital Library

[15]

K. Munger, P. Egan, J. Nagler, J. Ronen, and J. A. Tucker. 2015. Political knowledge and misinformation in the era of social media: Evidence from the 2015 UK Election. In APSA Annual Meeting 2015.

[16]

A. Ho, C. McGrath, and N. Mattheos. 2017. Social media patient testimonials in implant dentistry: Information or misinformation? Clin. Oral Implants Res. 28, 7 (2017), 791--800.

[17]

L. Palen and A. L. Hughes. 2018. Social media in disaster communication. In Handbook of Disaster Research. Springer, Cham, 497--518.

[18]

R. Aswani, A. K. Kar, P. V. Ilavarasan, and Y. K. Dwivedi. 2018. Search engine marketing is not all gold: Insights from Twitter and SEOClerks. Int. J. Info. Manage. 38, 1 (2018), 107--116.

Digital Library

[19]

R. Aswani, A. K. Kar, and P. V. Ilavarasan. 2017. Detection of spammers in Twitter marketing: A hybrid approach using social media analytics and bio inspired computing. Info. Syst. Front. 20, 3 (2017), 515--530.

Digital Library

[20]

C. Shao, G. L. Ciampaglia, O. Varol, A. Flammini, and F. Menczer. 2017. The spread of fake news by social bots. ArXiv Preprint Arxiv:1707.07592.

[21]

A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter. 2018. Detection and resolution of rumours in social media: A survey. ACM Comput. Surveys 51, 2 (2018), 32.

Digital Library

[22]

R. S. Wyer. 1974. Cognitive Organization and Change: An Information Processing Approach. Taylor 8 Francis.

[23]

J. Berger and K. Milkman. 2010. Social transmission, emotion, and the virality of online content. Wharton Research Paper, 106.

[24]

M. Guerini, C. Strapparava, and G. Özbal. 2011. Exploring text virality in social networks. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’11).

[25]

I. Roelens, P. Baecke, and D. F. Benoit. 2016. Identifying influencers in a social network: The value of real referral data. Decis. Support Syst. 91 (2016), 25--36.

Digital Library

[26]

K. K. Kumar and G. Geethakumari. 2014. Detecting misinformation in online social networks using cognitive psychology. Human-centric Comput. Info. Sci. 4, 1 (2014), 14.

[27]

M. Mendoza, B. Poblete, and C. Castillo. 2010. Twitter under crisis: Can we trust what we RT? In Proceedings of the 1st Workshop on Social Media Analytics. ACM, 71--79.

[28]

J. Ratkiewicz, M. Conover, M. Meiss, B. Gonçalves, S. Patil, A. Flammini, and F. Menczer. 2011. Truthy: Mapping the spread of astroturf in microblog streams. In Proceedings of the 20th International Conference Companion on World Wide Web. ACM, 249--252.

[29]

A. Gupta and P. Kumaraguru. 2012. Credibility ranking of tweets during high impact events. In Proceedings of the 1st Workshop on Privacy and Security in Online Social Media. ACM, 2.

[30]

J. Berger. 2011. Arousal increases social transmission of information. Psychol. Sci. 22, 7 (2011), 891--893.

[31]

E. M. Cotter. 2008. Influence of emotional content and perceived relevance on spread of urban legends: A pilot study. Psychol. Rep. 102, 2 (2008), 623--629.

[32]

K. Peters, Y. Kashima, and A. Clark. 2009. Talking about others: Emotionality and the dissemination of social information. Eur. J. Soc. Psychol. 39, 2 (2009), 207--222.

[33]

F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. 2010. Detecting spammers on Twitter. In Proceedings of the Collaboration, Electronic Messaging, Anti-abuse and Spam Conference (CEAS’10). 12.

[34]

K. Lee, B. D. Eoff, and J. Caverlee. 2011. Seven months with the devils: A long-term study of content polluters on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’11).

[35]

B. Wang, A. Zubiaga, M. Liakata, and R. Procter. 2015. Making the most of tweet-inherent features for social spam detection on Twitter. Arxiv Preprint Arxiv:1503.07405.

[36]

K. Lee, J. Caverlee, and S. Webb. 2010. Uncovering social spammers: Social honeypots+ machine learning. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 435--442.

[37]

D. Gayo Avello and D. J. Brenes Martínez. 2010. Overcoming spammers in Twitter—A tale of five algorithms. In Proceedings of the Spanish Conference on Information Retrieval.

[38]

A. H. Wang. 2010a. Don't follow me: Spam detection in Twitter. In Proceedings of the International Conference on Security and Cryptography (SECRYPT’10). IEEE, 1--10.

[39]

A. H. Wang. 2010b. Detecting spam bots in online social networking sites: A machine learning approach. In Proceedings of the IFIP Annual Conference on Data and Applications Security and Privacy. Springer, Berlin, 335--342.

[40]

S. Lewandowsky, U. K. Ecker, C. M. Seifert, N. Schwarz, and J. Cook. 2012. Misinformation and its correction: Continued influence and successful debiasing. Psychol. Sci. Public Int. 13, 3 (2012), 106--131.

[41]

R. E. Sanders. 1988. Dan Sperber and Deirdre Wilson, Relevance: Communication and Cognition, Oxford: Basil Blackwell, 1986, pp. 265. Lang. Soc. 17, 4 (1988), 604--609.

[42]

N. Schwarz. 1994. Judgment in a social context: Biases, shortcomings, and the logic of conversation. In Advances in Experimental Social Psychology, Vol. 26. Academic Press, 123--162.

[43]

M. R. Morris, S. Counts, A. Roseway, A. Hoff, and J. Schwarz. 2012. Tweeting is believing?: Understanding microblog credibility perceptions. In Proceedings of the ACM Conference on Computer Supported Cooperative Work. ACM, 441--450.

[44]

Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia. 2010. Who is tweeting on Twitter: Human, bot, or cyborg? In Proceedings of the 26th Annual Computer Security Applications Conference. ACM, 21--30.

[45]

Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia. 2012. Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Trans. Depend. Secure Comput. 9, 6 (2012), 811--824.

Digital Library

[46]

C. M. Zhang and V. Paxson. 2011. Detecting and analyzing automated activity on Twitter. In Proceedings of the International Conference on Passive and Active Network Measurement. Springer, Berlin, 102--111.

[47]

E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic. 2012. The role of social networks in information diffusion. In Proceedings of the 21st International Conference on World Wide Web. ACM, 519--528.

[48]

J. Leskovec, L. A. Adamic, and B. A. Huberman. 2007. The dynamics of viral marketing. ACM Trans. Web 1, 1 (2007), 5.

Digital Library

[49]

A. Anagnostopoulos, R. Kumar, and M. Mahdian. 2008. Influence and correlation in social networks. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 7--15.

[50]

E. Bakshy, B. Karrer, and L. A. Adamic. 2009. Social influence and the diffusion of user-created content. In Proceedings of the 10th ACM Conference on Electronic Commerce. ACM, 325--334.

[51]

C. F. Manski. 1993. Identification of endogenous social effects: The reflection problem. Rev. Econ. Studies 60, 3 (1993), 531--542.

[52]

S. Aral, L. Muchnik, and A. Sundararajan. 2009. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Natl. Acad. Sci. U.S.A. 106, 51 (2009), 21544--21549.

[53]

E. Adar, J. Teevan, and S. T. Dumais. 2009. Resonance on the web: Web dynamics and revisitation patterns. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1381--1390.

[54]

L. A. Adamic and E. Adar. 2003. Friends and neighbors on the web. Soc. Netw. 25, 3 (2003), 211--230.

[55]

A. Marin. 2004. Are respondents more likely to list alters with certain characteristics?: Implications for name generator data. Soc. Netw. 26, 4 (2004), 289--307.

[56]

G. Kossinets and D. J. Watts. 2009. Origins of homophily in an evolving social network. Amer. J. Sociol. 115, 2 (2009), 405--450.

[57]

M. S. Granovetter. 1977. The strength of weak ties. In Soc. Netw. 347--367.

[58]

D. Z. Levin and R. Cross. 2004. The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Manage. Sci. 50, 11 (2004), 1477--1490.

Digital Library

[59]

B. K. Chae. 2015. Insights from hashtag# supplychain and Twitter analytics: Considering Twitter and Twitter data for supply chain practice and research. Int. J. Prod. Econ. 165, 247--259.

[60]

J. Song, S. Lee, and J. Kim. 2011. Spam filtering in Twitter using sender-receiver relationship. In Proceedings of the International Workshop on Recent Advances in Intrusion Detection. Springer, Berlin, 301--317.

[61]

H. Kwak, C. Lee, H. Park, and S. Moon. 2010. What is Twitter, a social network or a news media? In Proceedings of the 19th International Conference on World Wide Web. ACM, 591--600.

[62]

M. Cha, H. Haddadi, F. Benevenuto, and P. K. Gummadi. 2010. Measuring user influence in Twitter: The million follower fallacy. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’10). 10--17.

[63]

R. Aswani, S. P. Ghrera, A. K. Kar, and S. Chandra. 2017. Identifying buzz in social media: A hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection. Soc. Netw. Anal. Min. 7, 1 (2017), 38.

[64]

R. Aswani, A. K. Kar, S. Aggarwal, and P. V. Ilavarsan. 2017. Exploring content virality in Facebook: A semantic-based approach. In Proceedings of the Conference on e-Business, e-Services, and e-Society. Springer, Cham, 209--220.

[65]

R. Aswani, S. P. Ghrera, S. Chandra, and A. K. Kar. 2017. Identifying popular online news: An approach using chaotic cuckoo search algorithm. In Proceedings of the International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS’17). IEEE.

[66]

S. Stemler. 2001. An overview of content analysis. Pract. Assess. Res. Eval. 7, 17 (2001), 137--146.

[67]

P. L. Hsu. 1938. Contribution to the theory of Student's t-test as applied to the problem of two samples. Statistical Research Memoirs. Cambridge University Press.

[68]

A. K. Rathore, A. K. Kar, and P. V. Ilavarasan. 2017. Social media analytics: Literature review and directions for future research. Decis. Anal. 14, 4 (2017), 229--249.

Digital Library

[69]

A. K. Kar. 2016. Bio inspired computing--A review of algorithms and scope of applications. Expert Syst. Appl. 59, 20--32.

Digital Library

[70]

A. Chakraborty and A. K. Kar. 2017. Swarm intelligence: A review of algorithms. In Nature-Inspired Computing and Optimization. Springer, Cham, 475--494.

[71]

R. Tang, S. Fong, X. S. Yang, and S. Deb. 2012. Integrating nature-inspired optimization algorithms to K-means clustering. In Proceedings of the 7th International Conference on Digital Information Management (ICDIM’12). IEEE, 116--123.

[72]

X. S. Yang. 2010. Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2, 2 (2010), 78--84.

Digital Library

[73]

X. S. Yang. 2010. A new metaheuristic bat-inspired algorithm. In Proceedings of the Conference on Nature Inspired Cooperative Strategies for Optimization (NICSO’10). Springer, Berlin, 65--74.

[74]

A. H. Gandomi, X. S. Yang, and A. H. Alavi. 2013. Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Eng. Comput. 29, 1 (2013), 17--35.

[75]

K. Zhai, J. Boyd-Graber, N. Asadi, and M. L. Alkhouja. 2012. Mr. LDA: A flexible large-scale topic modeling package using variational inference in mapreduce. In Proceedings of the 21st International Conference on World Wide Web. ACM, 879--888.

[76]

A. Dobele, A. Lindgreen, M. Beverland, J. Vanhamme, and R. Van Wijk. 2007. Why pass on viral messages? Because they connect emotionally. Bus. Horiz. 50, 4 (2007), 291--304.

[77]

J. L. Aaker. 1997. Dimensions of brand personality. J. Market. Res. 347--356.

[78]

N. Dalkey and O. Helmer. 1963. An experimental application of the Delphi method to the use of experts. Management Science 9, 3 (1963), 458--467.

[79]

S. Mirjalili, S. M. Mirjalili, and A. Lewis. 2014. Grey wolf optimizer. Adv. Eng. Software 69, 46--61.

Digital Library

[80]

R. Aswani, S. P. Ghrera, and S. Chandra. 2016. A novel approach to outlier detection using modified grey wolf optimization and k-nearest-neighbors algorithm. Indian J. Sci. Technol. 9, 44.

[81]

D. Karaboga and B. Basturk. 2007. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J. Global Optimiz. 39, 3 (2007), 459--471.

Digital Library

[82]

D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga. 2014. A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artific. Intell. Rev. 42, 1 (2014), 21--57.

Digital Library

[83]

S. Mirjalili. 2015. How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl. Intell. 43, 1 (2015), 150--161.

Digital Library

[84]

J. L. Gastwirth, Y. R. Gel, and W. Miao. 2009. The impact of Levene's test of equality of variances on statistical theory and practice. Stat. Sci. 24, 3 (2009), 343--360.

[85]

A. M. Kaplan and M. Haenlein. 2010. Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz.s 53, 1 (2010), 59--68.

[86]

M. Del Vicario, A. Bessi, F. Zollo, F. Petroni, A. Scala, G. Caldarelli, H. E. Stanley, and W. Quattrociocchi. 2016. The spreading of misinformation online. Proc. Natl. Acad. Sci. U.S.A. 113, 3 (2016), 554--559.

[87]

N. A. Karlova and K. E. Fisher. 2013. A social diffusion model of misinformation and disinformation for understanding human information behavior. Info. Res. 18, 1 (2013), 573.

[88]

A. J. Mills. 2012. Virality in social media: The SPIN framework. J. Public Affairs 12, 2 (2012), 162--169.

[89]

A. Arora, S. Bansal, C. Kandpal, R. Aswani, and Y. Dwivedi. 2019. Measuring social media influencer index-insights from Facebook, Twitter, and Instagram. J. Retail. Consum. Serv. 49 (2019), 86--101.

[90]

P. Grover, A. K. Kar, and G. Davies. 2018. “Technology enabled health”—Insights from Twitter analytics with a socio-technical perspective. Int. J. Info. Manage. 43 (2018), 85--97.

[91]

N. Joseph, A. K. Kar, P. V. Ilavarasan, and S. Ganesh. 2017. Review of discussions on internet of things (IoT): Insights from Twitter analytics. J. Global Info. Manage. 25, 2 (2017), 38--51.

Digital Library

[92]

C. Shirky. 2011. The political power of social media: Technology, the public sphere, and political change. Foreign Affairs 90, 1 (2011), 28--41.

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Experience: Managing Misinformation in Social Media—Insights for Policymakers from Twitter Analytics

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cover image Journal of Data and Information Quality

Journal of Data and Information Quality Volume 12, Issue 1

ON THE HORIZON, CHALLENGE PAPER, REGULAR PAPERS, and EXPERIENCE PAPER

March 2020

110 pages

ISSN:1936-1955

EISSN:1936-1963

DOI:10.1145/3372130

Editor:
Tiziana Catarci
Sapienza University of Rome, Rome, Italy

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Publication History

Published: 16 November 2019

Accepted: 01 June 2019

Revised: 01 June 2019

Received: 01 April 2018

Published in JDIQ Volume 12, Issue 1

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