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ABSTRACT
Self-communication platforms have generated a myriad of outlets and news producers that represent a challenge for modern societies. Therefore, it is relevant to explore new measurements that can help understand whether a specific outlet disseminating news could be considered reliable or not. This study is based on the expertise from the U.S. Intelligence Community to offer a statistical model that replicates the reliability measurements based on intelligence expertise. The results suggest that a classification algorithm could be useful to measure news media reliability. Additionally, different variables were identified to predict perceptions of media reliability.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1. Carey, Communication as Culture.
2. Castells, Network Society, 28.
3. Benkler, Wealth of Networks, 460.
4. Castells, Networks of Outrage, 20.
5. Lima, Politico.
6. Chen et al., Misleading Online Content, 15.
7. Johnston, Analytic Culture, 75; Friedman and Zeckhauser, “Assessing Uncertainty”, 824; Hutchins, Pirolly, and Card, Critical Decision Method, 237.
8. Clapper, Threat Assessment, 2; Gil & Ratnakar, Trusting Information, 162; Patterson and Woods, Cognitive Task Analysis, 439.
9. Cardenas, Manadhata, and Rajan, Data Analytics and Security, 74.
10. Entman, “Framing”, 52; Iyengar, How Television Frames Politics.
11. Vasu et al., “National Security in the Post-Truth Era.”
12. The Source Assessment protocol was directly provided by U.S. intelligence analysts.
13. Johnston, Analytic Culture, 35.
14. Johnston, Analytic Culture, 66.
15. Zeng et al., “Social Media Analytics and Intelligence”, 13.
16. Hillebrand, News Media and Intelligence, 704.
17. Chang and Izard, “Coverage of Patriot Act”, 6.
18. See above 15., 16.
19. Bakir, News and Intelligence Analysis, 131.
20. Wang et al., “Social Computing”, 80.
21. Freelon, “Communication and Social Computing”, 59.
22. Schubert & Glänzel, “References and Citations”, 409.
23. Hudson, “Citations”, 237; Saha, Saint, & Christakis, “Impact Factor”, 42.
24. Callaham, Wears, & Weber, “Publication Bias”, 2847.
25. Eisinger, Veenstra, & Koehn, “Media Bias”, 34; Entman, Media Affect People, 370; Groseclose & Milyo, “Measure of Bias”, 1191; Iyengar & Hahn, Ideological Selectivity, 38; Straud, Selective Exposure, 343.
26. Ibid.,
27. Oxford Dictionaries, Algorithm.
28. Lior, Decision Trees, 25.
29. R Project, The R Project for Statistical Computing.
30. Therneau, Atkinson, & Ripley, Package ‘rpart.’
31. Torgo, Package ‘DMwR.’
32. See above 4., 314.
33. Brin & Page, Web Search Engine, 3825.
34. Lee and Ma, News Sharing in Social Media, 331.
35. Canavero et al., Bibliometric Survey, 17; Greenwood, Reliability of Impact Factor, 48.
36. Domke et al., Conservative Elites, 35.
37. Iyengar & Hahn, “Ideological Selectivity”, 19.
38. Jamieson & Cappella, Echo Chamber, 237.
39. See above 21., 74.
Additional information
Funding
Notes on contributors
Hector Rendon
Dr. Hector Rendon is a Research Scholar in the Laboratory for Analytic Sciences at North Carolina State University. He earned a Ph.D. in Communication, Rhetoric and Digital Media from NC State, a M.A. in Digital Media at Hochschule für Künste Bremen, in Germany, and a Bachelor in Communication Science at the National Autonomous University of Mexico. His research focuses on the intersection of data analytics, communication and media coverage of minorities. He mostly studies issues related to international communication, media reliability, ethnic and intercultural communication, news framing and public policy. In addition, Dr. Rendon has also studied issues related to coverage of migrants in ethnic and general market news outlets using quantitative methods. His work has been published by international journals and he has presented his work at major conferences, including the National Communication Association (NCA) and the International Communication Association (ICA). During his academic career, Dr. Rendon has been distinguished with grants from several institutions, including a Fulbright grant from the U.S. Department of State, a Digital Humanities grant from HASTAC and a DAAD grant from the German Academic Exchange Service.
Alyson Wilson
Dr. Alyson Wilson is a Professor in the Department of Statistics and Principal Investigator for the Laboratory for Analytic Sciences at North Carolina State University. Prior to joining NCSU, Dr. Wilson was a research staff member at the IDA Science and Technology Policy Institute in Washington, DC (2011–2013), an associate professor in the Department of Statistics at Iowa State University (2008–2011), a project leader and technical lead for Department of Defense Programs in the Statistical Sciences Group at Los Alamos National Laboratory (1999–2008) and a senior statistician at Cowboy Programming Resources (1995–1999). She is a member of the Sandia National Laboratories Predictive Engineering Science Panel (2008–present). Dr. Wilson received her Ph.D. in Statistics from Duke University, her M.S. in Statistics from Carnegie-Mellon University and her B.A. in Mathematical Sciences from Rice University.
Jared Stegall
Jared Stegall is a Research Associate in the Laboratory for Analytic Sciences (LAS) at North Carolina State University. His research mainly focuses on the laboratory’s efforts to automate the assessment of news media reliability and to scientifically evaluate the effectiveness of several Korean-to-English transliteration schemes. Prior to working at LAS, Jared interned as an Information Technology Business Analyst at Credit Suisse LLC and worked as an undergraduate programming assistant for a joint research project between NC State University and the United States Department of Agriculture. He is a graduate of NC State University with a Bachelor of Science in Statistics and a Bachelor of Science in Economics. He was a 4-year member of the NC State University Scholars Program and graduated with Cum Laude honors. During his senior year, he served as President of the NC State Statistics Club and as Vice President of the Economics Society.