Research challenges of digital misinformation: Toward a trustworthy web

AI Magazine

Volumn 39, Issue 1, 2018, Pages 65-74

  Ciampaglia, Giovanni Luca ^a   Mantzarlis, Alexios ^b   Maus, Gregory ^a   Menczer, Filippo ^a  

^a INDIANA UNIVERSITY   (United States)

^b Poynter Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

FREEDOM OF SPEECH; IN-BUILDINGS; OFFLINE; PUBLIC OPINIONS; RESEARCH CHALLENGES; SOCIAL BOTS; SOCIAL MEDIA;

SOCIAL ASPECTS;

EID: 85046075215     PISSN: 07384602     EISSN: None     Source Type: Journal    
DOI: 10.1609/aimag.v39i1.2783     Document Type: Article

References (48)

1. Alfifi, M., and Caverlee, J. 2017. Badly Evolved? Exploring Long-Surviving Suspicious Users on Twitter. In Proceedings of SocInfo 2017, International Conference on Social Informatics. Lecture Notes in Computer Science 10539, 218-233. Berlin: Springer. doi.org/10.1007/978-3-319-67217-5_14
2. Arif, A.; Shanahan, K.; Chou, F.-J.; Dosouto, Y.; Starbird, K.; and Spiro, E. 2016. How Information Snowballs: Exploring the Role of Exposure in Online Rumor Propagation. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW'16), 466-477. New York: Association for Computing Machinery. doi.org/10.1145/2818048.2819964
3. Bode, L., and Vraga, E. K. 2015. In Related News, That Was Wrong: The Correction of Misinformation Through Related Stories Functionality in Social Media. Journal of Communication 65(4):619-638. doi.org/10.1111/jcom.12166
4. Conover, M.; Ratkiewicz, J.; Francisco, M.; Gonçalves, B., Menczer, F.; and Flammini, A. 2011. Political Polarization on Twitter. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM), 89-96. Palo Alto, CA: AAAI Press. www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2847
5. Dailey, D., and Starbird, K. 2014. Journalists as Crowd-sourcerers: Responding to Crisis by Reporting with a Crowd. Computer Supported Cooperative Work 23(4-6): 445-481. doi.org/10.1007/s10606-014-9208-z
6. Davis, C. A.; Ciampaglia, G. L.; Aiello, L. M.; Chung, K.; Conover, M. D.; Ferrara, E.; Flammini, A. 2016. OSoMe: The IUNI Observatory on Social Media. PeerJ Computer Science 2(October):e87. doi.org/10.7717/peerj-cs.87
7. Davis, C. A.; Varol, O.; Ferrara, E.; Flammini, A.; and Menczer, F. 2016. BotOrNot: A System to Evaluate Social Bots. In WWW'16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web, 273-274. Cambridge, MA: W3C. doi.org/10.1145/2872518.2889302
8. Diakopoulos, N. 2017. Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens. In Transparent Data Mining for Big and Small Data, eds. T. Cerquitelli, D. Quercia, F. Pasquale, 25-43. Studies in Big Data 32. Berlin: Springer. doi.org/10.1007/978-3-319-54024-5_2
9. Diakopoulos, N., and Koliska, M. 2017. Algorithmic Transparency in the News Media. Digital Journalism 5(7): 809-828. doi.org/10.1080/21670811.2016.1208053
10. Diaz, F.; Gamon, M.; Hofman, J. M.; Kıcıman, K.; and Rothschild, D. 2016. Online and Social Media Data as an Imperfect Continuous Panel Survey. PLoS ONE 11(1): e0145406. doi.org/10.1371/journal.pone.0145406
11. Ferrara, E.; Varol, O.; Davis, C.; Menczer, F.; and Flammini, A. 2016. The Rise of Social Bots. Communications of the ACM 59(7): 96-104. doi.org/10.1145/2818717
12. Fredheim, R. 2017. Robotrolling. Riga, Latvia: NATO Strategic Communications Centre of Excellence. (www.stratcomcoe.org/robotrolling-20171)
13. Funk, C., and Kennedy, B. 2016. The Politics of Climate. Washington, DC: Pew Research Center. (www.pewinternet.org/2016/10/04/the-politics-of-climate)
14. Garrett, R. K.; Weeks, B. E.; and Neo, R. L. 2016. Driving a Wedge Between Evidence and Beliefs: How Online Ideological News Exposure Promotes Political Misperceptions. Journal of Computer-Mediated Communication 21(5): 331-348. doi.org/10.1111/jcc4.12164
15. Glenski, M.; Pennycuff, C.; and Weninger, T. 2017. Consumers and Curators: Browsing and Voting Patterns on Reddit. IEEE Transactions on Computational Social Systems 4(4): 196-206. doi.org/10.1109/TCSS.2017.2742242
16. Glenski, M., and Weninger, T. 2017. Rating Effects on Social News Posts and Comments. ACM Transactions on Intelligent Systems and Technology 8(6): 78. doi.org/10.1145/2963104
17. Huang, Y. L.; Starbird, K.; Orand, M.; Stanek, S. A.; and Ped-ersen, H. T. 2015. Connected Through Crisis: Emotional Proximity and the Spread of Misinformation Online. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW'15). New York: Association for Computer Machinery. doi.org/10.1145/ 2675133.2675202
18. Kaghazgaran, P.; Caverlee, J.; Alfifi, M. 2017. Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond. In Proceedings of the 11th International AAAI Conference on Web and Social Media (ICWSM 2017), 560-563. Palo Alto, CA: AAAI Press. (www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15659)
19. Kang, J. H., and Lerman, K. 2015. VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP 2015). Lecture Notes in Computer Science 9021, 101-110. Berlin: Springer. doi.org/10.1007/978-3-319-16268-3_11
20. Kosslyn, J., and Yu, C. 2017. Fact Check Now Available in Google Search and News around the World. Google Blog, April 7. (www.blog.google/products/search/fact-check-now-available-google-search-and-news-around-world)
21. Lerman, K., and Hogg, T. 2014. Leveraging Position Bias to Improve Peer Recommendation. PLoS ONE 9(6): e98914. doi.org/10.1371/journal.pone.0098914
22. Li, C.; Ma, J.; Guo, X.; and Mei, Q. 2017. DeepCas: An End-to-End Predictor of Information Cascades. In Proceedings of the 26th International Conference on World Wide Web (WWW'17), 577-586. Cambridge, MA: W3C. doi.org/ 10.1145/3038912.3052643
23. Ma, J.; Gao, W.; Mitra, P.; Kwon, S.; Jansen, B. J.; Wong, K.F.; and Cha, M. 2016. Detecting Rumors from Microblogs with Recurrent Neural Networks. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI '16), 3818-3824. Palo Alto, CA: AAAI Press. www.ijcai.org/Abstract/16/537
24. Maddock, J.; Starbird, K.; Al-Hassani, H. J.; Sandoval, D. E.; Orand, M.; Mason, R. M. 2015. Characterizing Online Rumoring Behavior Using Multi-Dimensional Signatures. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW'15), 228-241. New York: Association for Computing Machinery. doi.org/10.1145/2675133.2675280
25. McGrew, S.; Ortega, T.; Breakstone, J.; and Wineburg, S. 2017. The Challenge That's Bigger Than Fake News. American Educator (Fall): 4-9.
26. Mikkelson, D. 2016. We Have a Bad News Problem, Not a Fake News Problem. Snopes.com, November 17. (www.snopes.com/2016/11/17/we-have-a-bad-news-prob-lem-not-a-fake-news-problem)
27. Mosseri, A. 2016. News Feed FYI: Addressing Hoaxes and Fake News. Menlo Park, CA: Facebook. (newsroom.fb.com/ news/2016/12/news-feed-fyi-addressing-hoaxes-and-fake-news)
28. Mustafaraj, E., and Metaxas, P. T. 2010. From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search. Paper presented at Wec Science 2010: Extending the Frontiers of Society On-Line (WebSci10), Raleigh, NC, April 26-27. (repository.wellesley.edu/computersciencefaculty/9)
29. Mustafaraj, E., and Metaxas, P. T. 2017. The Fake News Spreading Plague: Was It Preventable? In Proceedings of the ACM Conference on Web Science (WebSci'17), 235-239. New York: Association for Computing Machinery. doi.org/10. 1145/3091478.3091523
30. Nematzadeh, A.; Ciampaglia, G. L.; Menczer, F.; and Flammini, A. 2017. How Algorithmic Popularity Bias Hinders or Promotes Quality. arXiv Preprint. arXiv:1707.00574v2 [cs.CY]. Ithaca, NY: Cornell University Library.
31. Nikolov, D.; Oliveira, D. F. M.; Flammini, A.; Menczer, F. 2015. Measuring Online Social Bubbles. PeerJ Computer Science 1:e38. doi.org/10.7717/peerj-cs.38
32. Pariser, E. 2011. The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. London: Penguin.
33. Pew Research Center. 2017. State of the News Media. Washington, DC: Pew Research Center. (www.pewresearch.org/topics/state-of-the-news-media)
34. Qazvinian, V.; Rosengren, E.; Radev, D. R.; and Mei, Q. 2011. Rumor Has It?: Identifying Misinformation in Microblogs. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP'11), 1589-1599. doi:10.1038/s41562-017-0132
35. Qiu, X.; Oliveira, D. F. M.; Shirazi, A. S.; Flammini, A.; and Menczer, F. 2017. Limited Individual Attention and Online Virality of Low-Quality Information. Nature Human Behav-iour 1(7): 0132. doi:10.1038/s41562-017-0132
36. Resnick, P.; Carton, S.; Park, S.; Shen, Y.; and Zeffer, N. 2014. RumorLens?: A System for Analyzing the Impact of Rumors and Corrections in Social Media. Paper presented at the Computation + Journalism Symposium, New York, October 24-25. (nicole.zeffer.com/cj2014.pdf)
37. Resnick, P., and Sami, R. 2008. Manipulation-Resistant Rec-ommender Systems through Influence Limits. ACM SIGecom Exchanges 7(3): 10. doi.org/10.1145/1486877.1486887
38. Shao, C.; Ciampaglia, G. L.; Varol, O.; Flammini, A.; and Menczer, F. 2017. The Spread of Fake News by Social Bots. arXiv Preprint. arXiv:1707.07592v2 [cs.SI]. Ithaca, NY: Cornell University Library.
39. Shiralkar, P.; Flammini, A.; Menczer, F.; and Ciampaglia, G. L. 2017. Finding Streams in Knowledge Graphs to Support Fact Checking. In Proceedings of the IEEE International Conference on Data Mining (ICDM). Los Alamitos, CA: IEEE Computer Society.
40. Silverman, C. 2017. Inside The Partisan Fight For Your News Feed. BuzzFeedNews, August 8. (www.buzzfeed.com/craigsilverman/inside-the-partisan-fight-for-your-news-feed?utm_term=.beN9PdexE#.skpv67JG9)
41. Sunstein, C. R. 2002. The Law of Group Polarization. The Journal of Political Philosophy 10(2): 175-195. doi.org/10. 1111/1467-9760.00148
42. Sunstein, C. R. 2009. Republic.com 2.0. Princeton, NJ: Princeton University Press.
43. Suwajanakorn, S; Seitz, S. M.; and Kemelmacher-Shlizerman, I. 2017. Synthesizing Obama: Learning Lip Sync from Audio. ACM Transactions on Graphics 36(4): 95. dx.doi.org/10.1145/3072959.3073640
44. Thies, J.; Zollhöfer, M.; Stamminger, M.; Christian T.; and Nießner, M. 2016. Face2Face: Real-Time Face Capture and Reenactment of RGB Videos. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pis-cataway, NJ: Institute for Electrical and Electronics Engineers. IEEE. doi.org/10.1109/CVPR.2016.262
45. Varol, O.; Ferrara, E.; Davis, C. A.; Menczer, F.; and Flammini, A. 2017. Online Human-Bot Interactions: Detection, Estimation, and Characterization. In Proceedings of the Eleventh International AAAI Conference on Web and Social Media (ICWSM). Palo Alto, CA: AAAI Press. (aaai.org /ocs/index.php/ICWSM/ICWSM17/paper/view/15587)
46. Varol, O.; Ferrara, E.; Menczer, F.; and Flammini, A. 2017. Early Detection of Promoted Campaigns on Social Media. EPJ Data Science 6:13. doi.org/10.1140/epjds/s13688-017-0111-y
47. Weng, L.; Flammini, A.; Vespignani, A.; and Menczer, F. 2012. Competition among Memes in a World with Limited Attention. Scientific Reports 2: 335. doi.org/10.1038/ srep00335
48. Zhao, Z.; Resnick, P.; and Mei, Q. 2015. Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts. In Proceedings of the 24th International Conference on World Wide Web (WWW'15), 1395-1405. Cambridge, MA: W3C. doi.org/10.1145/2736277.2741637