Agenda setting and active audiences in online coverage of human trafficking

† This paper is a further development of a paper first presented at the 2015 American Sociological Association annual meeting in Chicago, IL, at the Section on Communication, Information Technology and Media Sociology paper session.

ABSTRACT

Online news platforms and social media increasingly influence the public agenda on social issues such as human trafficking. Yet despite the popularity of online news and the availability of sophisticated tools for analyzing digital texts, little is known about the relations between news coverage of human trafficking and audiences’ reactions to and interpretations of such coverage. In this paper, we examine journalists’ and commenters’ topic choices in coverage and discussion of human trafficking in the British newspaper The Guardian from 2009 to 2014. We use latent semantic analysis to identify 11 topics discussed by both journalists and readers, and analyze each topic in terms of the degree to which journalists and readers agree or disagree in their topic preferences. We find that four topics were preferred equally by journalists and commenters, four were preferred by journalists, and three were preferred by commenters. Our findings suggest that theories of ‘agenda setting’ and of the ‘active audience’ are not mutually exclusive, and the scope of explanation of each depends partly on the specific topic or subtopic that is analyzed.

KEYWORDS:

• Human trafficking
• online news
• agenda setting
• active audience
• topic models
• latent semantic analysis

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Maria Eirini Papadouka is a Doctoral Candidate at the Sociology Department of the University of North Texas. Her research interests involve human trafficking, new media, text mining, and organized crime. She received her Master’s in Criminology, Criminal Justice and Social Research from the University of Surrey, UK. [email: [email protected]]

Nicholas Evangelopoulos is an associate professor of Decision Sciences at the University of North Texas. His research interests include statistics and text mining. His publications include articles appearing in MIS Quarterly, Communications of the ACM, Decision Sciences, and many others. [email: [email protected]]

Gabe Ignatow is an associate professor of Sociology at the University of North Texas. His research interests are in the areas of sociological theory, text mining, new media, and information policy. Gabe’s current research involves adapting text mining methods developed in computer science and related disciplines for social science applications. [email: [email protected]]

ORCID

Nicholas Evangelopoulos http://orcid.org/0000-0001-9866-6828

Notes

† This paper is a further development of a paper first presented at the 2015 American Sociological Association annual meeting in Chicago, IL, at the Section on Communication, Information Technology and Media Sociology paper session.