Can an Algorithm Reduce the Perceived Bias of News? Testing the Effect of Machine Attribution on News Readers’ Evaluations of Bias,Anthropomorphism,and Credibility

Abstract

Although accusations of editorial slant are ubiquitous to the contemporary media environment, recent advances in journalism such as news writing algorithms may hold the potential to reduce readers’ perceptions of media bias. Informed by the Modality-Agency-Interactivity-Navigability (MAIN) model and the principle of similarity attraction, an online experiment (n = 612) was conducted to test if news attributed to an automated author is perceived as less biased and more credible than news attributed to a human author. Results reveal that perceptions of bias are attenuated when news is attributed to a journalist and algorithm in tandem, with positive downstream consequences for perceived news credibility.

Keywords

computational journalism media bias message credibility MAIN model similarity attraction

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References

Amazeen

M. A.

Muddiman

A. R.

(2018). Saving media or trading on trust? The effects of native advertising on audience perceptions of legacy and online news publishers. Digital Journalism, 6, 176-195. doi:10.1080/21670811.2017.1293488

Anderson

C. W.

(2013). Towards a sociology of computational and algorithmic journalism. New Media & Society, 15, 1005-1021. doi:10.1177/1461444812465137

Appleman

Sundar

S. S.

(2016). Measuring message credibility: Construction and validation of an exclusive scale. Journalism & Mass Communication Quarterly, 93(1), 59-79. doi:https://doi.org/10.1177/1077699015606057

Bartneck

Suzuki

Kanda

Nomura

(2007). The influence of people’s culture and prior experiences with Aibo on their attitude towards robots. AI & Society, 21, 217-230. doi:10.1007/s00146-006-0052-7

Bellur

Sundar

S. S.

(2014). How can we tell when a heuristic has been used? Design and analysis strategies for capturing the operation of heuristics. Communication Methods and Measures, 8, 116-137. doi:10.1080/19312458.2014.903390

Buhrmester

Kwang

Gosling

S. D.

(2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6, 3-5. doi:10.1177/1745691610393980

Byrne

(1997). An overview (and underview) of research and theory within the attraction paradigm. Journal of Social and Personal Relationships, 14, 417-431. doi:10.1177/0265407597143008

Carlson

(2016). Automated journalism: A post man future for digital news? In Franklin

Eldridge

(Eds.), The Routledge companion to digital journalism studies (pp. 226-234). New York, NY: Routledge.

Carter

R. F.

Greenberg

B. S.

(1965). Newspapers or television: Which do you believe? Journalism & Mass Communication Quarterly, 42, 29-34. doi:10.1177/107769906504200104

10.

Chaiken

Liberman

Eagly

A. E.

(1989). Heuristic and systematic information processing within and beyond the persuasion context. In Uleman

J. S.

Bargh

J. A.

(Eds.), Unintended thought (pp. 212-252). New York, NY: Guilford Press.

11.

Chen

Chaiken

(1999). The heuristic systematic model in its broader context. In Chaiken

Trope

(Eds.), Dual-process theories in social psychology (pp. 73-96). New York, NY: Guilford Press.

12.

Clerwall

(2014). Enter the robot journalist: Users’ perceptions of automated content. Journalism Practice, 8, 519-531. doi:10.1080/17512786.2014.883116

13.

Clifford

Jewell

R. M.

Waggoner

P. D.

(2015). Are samples drawn from Mechanical Turk valid for research on political ideology? Research & Politics, 2(4). doi:10.1177/2053168015622072

14.

Coddington

(2015). Clarifying journalism’s quantitative turn: A typology for evaluating data journalism, computational journalism, and computer-assisted reporting. Digital Journalism, 3, 331-348. doi:10.1080/21670811.2014.976400

15.

Cohen

Hamilton

J. T.

Turner

(2011). Computational journalism. Communications of the ACM, 54(10), 66-71. doi:10.1145/2001269.2001288

16.

Dörr

K. N.

(2015). Mapping the field of algorithmic journalism. Digital Journalism, 4, 700-722. doi:10.1080/21670811.2015.1096748

17.

Edwards

Spence

P. R.

Shelton

A. K.

(2014). Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter. Computers in Human Behavior, 33, 372-376. doi:10.1016/j.chb.2013.08.013

18.

Eveland

W. P.

Shah

D. V.

(2003). The impact of individual and interpersonal factors on perceived news media bias. Political Psychology, 24, 101-117. doi:10.1111/0162-895X.00318

19.

Giner-Sorolla

Chaiken

(1994). The causes of hostile media judgments. Journal of Experimental Social Psychology, 30, 165-180. doi:10.1006/jesp.1994.1008

20.

Graefe

(2017). Guide to automated journalism. Tow Center for Digital Journalism. Retrieved from http://towcenter.org/research/guide-to-automated-journalism/

21.

Graefe

Haim

Haarmann

Brosius

H.-B.

(2016). Readers’ perceptions of computer-generated news: Credibility, expertise, and readability. Advance online publication. Journalism. Advance online publication. doi:10.1177/1464884916641269

22.

Hayes

A. F.

(2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press.

23.

Hayes

A. F.

Preacher

K. J.

(2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, 67, 451-470. doi:10.1111/bmsp.12028

24.

Houston

J. B.

Hansen

G. J.

Nisbett

G. S.

(2011). Influence of user comments on perceptions of media bias and third person effect in online news. Electronic News, 5, 79-92. doi:10.1177/1931243111407618

25.

Hovland

C. I.

Weiss

(1951). The influence of source credibility on communication effectiveness. Public Opinion Quarterly, 15, 635-650. doi:10.1086/266350

26.

Iyengar

Hahn

K. S.

(2009). Red media, blue media: Evidence of ideological selectivity in media use. Journal of Communication, 59, 19-39. doi:10.1111/j.1460-2466.2008.01402.x

27.

Kang

Bae

Zhang

Sundar

S. S.

(2011). Source cues in online news: Is the proximate source more powerful than distal sources? Journalism & Mass Communication Quarterly, 88, 719-736. doi:10.1177/107769901108800403

28.

Knobloch-Westerwick

Meng

(2009). Looking the other way: Selective exposure to attitude consistent information and counterattitudinal political information. Communication Research, 36, 426-448. doi:10.1177/00936502093330

29.

Lee

E.-J.

(2012). That’s not the way it is: How user-generated comments on the news affect perceived media bias. Journal of Computer-Mediated Communication, 18, 32-45. doi:10.1111/j.1083-6101.2012.01597.x

30.

Lee

K. M.

Peng

Jin

S.-A.

Yan

(2006). Can robots manifest personality? An empirical test of personality recognition, social responses, and social presence in human-robot interaction. Journal of Communication, 56, 754-772. doi:10.1111/j.1460-2466.2006.00318.x

31.

Lokot

Diakopoulos

(2015). News bots: Automating news and information dissemination on Twitter. Digital Journalism, 4, 682-699. doi:10.1080/21670811.2015.1081822

32.

McCroskey

J. C.

(1966). Scales for the measurement of ethos. Speech Monographs, 33, 65-72. doi:10.1080/03637756609375482

33.

Media Insights Project. (2016). Who shared it? How Americans decide what news to trust on social media. Retrieved from http://mediainsight.org/Pages/%27Who-Shared-It%27-How-Americans-Decide-What-News-to-Trust-on-Social-Media.aspx

34.

Metzger

M. J.

Flanagin

A. J.

Eyal

Lemus

D. R.

Mccann

R. M.

(2003). Credibility for the 21st century: Integrating perspectives on source, message, and media credibility in the contemporary media environment. Annals of the International Communication Association, 27, 293-335. doi:10.1080/23808985.2003.11679029

35.

Metzger

M. J.

Flanagin

A. J.

Medders

R. B.

(2010). Social and heuristic approaches to credibility evaluation online. Journal of Communication, 60, 413-439. doi:10.1111/j.1460-2466.2010.01488.x

36.

Montoya

R. M.

Horton

R. S.

(2013). A meta-analytic investigation of the processes underlying the similarity attraction effect. Journal of Social and Personal Relationships, 30, 64-94. doi:10.1177/0265407512452989

37.

Moskowitz

G. B.

Skurnik

Galinsky

A. D.

(1999). The history of dual-process notions, and the future of preconscious control. In Chaiken

Trope

(Eds.), Dual process theories in social psychology (pp. 12-40). New York, NY: Guilford Press.

38.

Nass

Moon

(2000). Machines and mindfulness: Social responses to computers. Journal of Social Issues, 56, 81-103. doi:10.1111/0022-4537.00153

39.

Nowak

K. L.

Biocca

(2003). The effect of the agency and anthropomorphism on users’ sense of telepresence, copresence, and social presence in virtual environments. Presence: Teleoperators and Virtual Environments, 12, 481-494. doi:10.1162/105474603322761289

40.

Nowak

K. L.

Hamilton

M. A.

Hammond

C. C.

(2009). The effect of image features on judgments of homophily, credibility, and intention to use as avatars in future interactions. Media Psychology, 12, 50-76. doi:10.1080/15213260802669433

41.

Nowak

K. L.

Rauh

(2008). Choose your “buddy icon” carefully: The influence of avatar androgyny, anthropomorphism and credibility in online interactions. Computers in Human Behavior, 24, 1473-1493. doi:10.1016/j.chb.2007.05.005

42.

O’Keefe

D. J.

(2015). Trends and prospects in persuasion theory and research. In O’Keefe

D. J.

(Ed.), Persuasion: Theory and research (pp. 214-240). Thousand Oaks, CA: SAGE.

43.

Reeves

Nass

(1996). The media equation: How people treat computers, television, and new media like real people and places. Stanford, CA: Cambridge University Press.

44.

Schwartz

M. S.

(2017). Corporate social responsibility. London, England: Routledge.

45.

Simons

H. W.

Berkowitz

N. N.

Moyer

J. R.

(1970). Similarity, credibility, and attitude change: A review and a theory. Psychological Bulletin, 73, 1-16. doi:10.1037/h0028429

46.

Sundar

S. S.

(2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In Metzger

M. J.

Flanagin

A. J.

(Eds.), Digital media, youth, and credibility (pp. 73-100). Cambridge, MA: The MIT Press.

47.

Sundar

S. S.

Jia

Waddell

T. F.

Huang

(2015). Toward a theory of interactive media effects: Four models for explaining how interface features affect user psychology. In Sundar

S. S.

(Ed.), Handbook of psychology of communication technology (pp. 47-86). Malden, MA: Wiley Blackwell.

48.

Sundar

S. S.

Nass

(2001). Conceptualizing sources in online news. Journal of Communication, 51, 57-72. doi:10.1111/j.1460-2466.2001.tb02872.x

49.

Todorov

Chaiken

Henderson

M. D.

(2002). The heuristic-systematic model of social information processing. In Dillard

J. P.

Pfau

(Eds.), The persuasion handbook: Developments in theory and practice (pp. 195-212). Thousand Oaks, CA: SAGE.

50.

Vallone

R. P.

Ross

Lepper

M. R.

(1985). The hostile media phenomenon: Biased perception and perceptions media bias in coverage of the Beirut massacre. Journal of Personality and Social Psychology, 49, 577-585. doi:10.1037/0022-3514.49.3.577

51.

Wise

Bolls

P. D.

Shaefer

S. R.

(2008). Choosing and reading online news: How available choice affects cognitive processing. Journal of Broadcasting & Electronic Media, 52, 69-85. doi:10.1080/08838150701820858

52.

Young

M. L.

Hermida

(2014). From Mr. and Mrs. Outlier to central tendencies: Computational journalism and crime reporting at the Los Angeles Times. Digital Journalism, 3, 381-397. doi:10.1080/21670811.2014.976409

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