Does Being Verified Make You More Credible?: Account Verification's Effect on Tweet Credibility

Many popular social networking and microblogging sites support verified accounts---user accounts that are deemed of public interest and whose owners have been authenticated by the site. Importantly, the content of messages contributed by verified account owners is not verified. Such messages may be factually correct, or not. This paper investigates whether users confuse authenticity with credibility by posing the question: Are users more likely to believe content from verified accounts than from non-verified accounts? We conduct two online studies, a year apart, with 748 and 2041 participants respectively, to assess how the presence or absence of verified account indicators influences users' perceptions of tweets. Surprisingly, across both studies, we find that---in the context of unfamiliar accounts---most users can effectively distinguish between authenticity and credibility. The presence or absence of an authenticity indicator has no significant effect on willingness to share a tweet or take action based on its contents.

[1]  A. Strauss,et al.  Basics of Qualitative Research , 1992 .

[2]  Dragomir R. Radev,et al.  Rumor has it: Identifying Misinformation in Microblogs , 2011, EMNLP.

[3]  Richard Honeck,et al.  Experimental Design and Analysis , 2006 .

[4]  Ward van Zoonen,et al.  The Importance of Source and Credibility Perception in Times of Crisis: Crisis Communication in a Socially Mediated Era , 2015 .

[5]  Q. Mcnemar Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

[6]  Scott Counts,et al.  Tweeting is believing?: understanding microblog credibility perceptions , 2012, CSCW.

[7]  Miriam J. Metzger,et al.  behaviors on the perceived credibility of web-based information The role of site features, user attributes, and information verification , 2007 .

[8]  Jonathan Robinson,et al.  TurkPrime.com: A versatile crowdsourcing data acquisition platform for the behavioral sciences , 2016, Behavior Research Methods.

[9]  Marti A. Hearst,et al.  Why phishing works , 2006, CHI.

[10]  Harrison Si,et al.  Handbook of Research Methods in Social and Personality Psychology: Author Index , 2013 .

[11]  James B. Lemert,et al.  DIMENSIONS FOR EVALUATING THE ACCEPTABILITY OF MESSAGE SOURCES , 1969 .

[12]  Amir Herzberg,et al.  Security and identification indicators for browsers against spoofing and phishing attacks , 2008, TOIT.

[13]  M. Angela Sasse,et al.  Obstacles to the Adoption of Secure Communication Tools , 2017, 2017 IEEE Symposium on Security and Privacy (SP).

[14]  S. Wineburg,et al.  Can Students Evaluate Online Sources? Learning From Assessments of Civic Online Reasoning , 2018 .

[15]  M. Gentzkow,et al.  Social Media and Fake News in the 2016 Election , 2017 .

[16]  Robert M. Mason,et al.  Characterizing Online Rumoring Behavior Using Multi-Dimensional Signatures , 2015, CSCW.

[17]  Ward van Zoonen,et al.  The Importance of Source and Credibility Perception in Times of Crisis: Crisis Communication in a Socially Mediated Era , 2015 .

[18]  Lorrie Faith Cranor,et al.  Are your participants gaming the system?: screening mechanical turk workers , 2010, CHI.

[19]  A. Raftery Bayesian Model Selection in Social Research , 1995 .

[20]  Soo Young Rieh,et al.  Developing a unifying framework of credibility assessment: Construct, heuristics, and interaction in context , 2008, Inf. Process. Manag..

[21]  Melanie C. Green,et al.  Telephone versus Face-to-Face Interviewing of National Probability Samples with Long Questionnaires: Comparisons of Respondent Satisficing and Social Desirability Response Bias , 2003 .

[22]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[23]  James A. Landay,et al.  Utility of human-computer interactions: toward a science of preference measurement , 2011, CHI.

[24]  D. R. Danielson,et al.  How do users evaluate the credibility of Web sites?: a study with over 2,500 participants , 2003, DUX '03.

[25]  HerzbergAmir,et al.  Security and identification indicators for browsers against spoofing and phishing attacks , 2008 .

[26]  B. Erdogan Celebrity Endorsement: A Literature Review , 1999 .

[27]  RiehSoo Young,et al.  Developing a unifying framework of credibility assessment , 2008 .

[28]  G. Geethakumari,et al.  Detecting misinformation in online social networks using cognitive psychology , 2014, Human-centric Computing and Information Sciences.

[29]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[30]  Miriam J. Metzger,et al.  Social and Heuristic Approaches to Credibility Evaluation Online , 2010 .

[31]  A. Acquisti,et al.  Reputation as a sufficient condition for data quality on Amazon Mechanical Turk , 2013, Behavior Research Methods.

[32]  S. Chaiken The heuristic model of persuasion. , 1987 .

[33]  Stuart E. Schechter,et al.  The Emperor's New Security Indicators , 2007, 2007 IEEE Symposium on Security and Privacy (SP '07).

[34]  K. Pearson On the Criterion that a Given System of Deviations from the Probable in the Case of a Correlated System of Variables is Such that it Can be Reasonably Supposed to have Arisen from Random Sampling , 1900 .

[35]  Eszter Hargittai,et al.  Succinct Survey Measures of Web-Use Skills , 2012 .

[36]  Laura A. Dabbish,et al.  Privacy Attitudes of Mechanical Turk Workers and the U.S. Public , 2014, SOUPS.

[37]  A. Anderson Social Media Use in 2018 , 2018 .

[38]  Z. Kunda,et al.  The case for motivated reasoning. , 1990, Psychological bulletin.

[39]  Michael D. Buhrmester,et al.  Amazon's Mechanical Turk , 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

[40]  S. Sundar The MAIN Model : A Heuristic Approach to Understanding Technology Effects on Credibility , 2007 .

[41]  Aniket Kittur,et al.  Crowdsourcing user studies with Mechanical Turk , 2008, CHI.

[42]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[43]  H. Kelley,et al.  Communication and Persuasion: Psychological Studies of Opinion Change , 1982 .

[44]  Lorrie Faith Cranor,et al.  Crying Wolf: An Empirical Study of SSL Warning Effectiveness , 2009, USENIX Security Symposium.

[45]  Klaus Krippendorff,et al.  Answering the Call for a Standard Reliability Measure for Coding Data , 2007 .

[46]  Karl Pearson F.R.S. X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling , 2009 .

[47]  Sunny Consolvo,et al.  Rethinking Connection Security Indicators , 2016, SOUPS.

[48]  Robert H Lyles,et al.  A practical approach to computing power for generalized linear models with nominal, count, or ordinal responses , 2007, Statistics in medicine.

[49]  Sam Wineburg,et al.  Evaluating information: The cornerstone of civic online reasoning , 2016 .

[50]  P. Todd,et al.  Simple Heuristics That Make Us Smart , 1999 .