What Social Media Platforms Can Learn from Audience Measurement: Lessons in the Self-Regulation of 'Black Boxes'

45 Pages Posted: 2 Feb 2018 Last revised: 13 Aug 2018

Date Written: January 31, 2018

Abstract

The range of concerns that have arisen around the operation of social media platforms has prompted more intensive discussions about the possibility greater federal or self-regulation of social media. Self-regulation has a long tradition in the media sector, with the motion picture, music, television, and videogame industries all adopting self-imposed and -designed content ratings systems. There is, however, another media-related self-regulatory context that better reflects the nature of the concerns and challenges surrounding social media – the audience measurement industry. The goals of this paper are to explore the parallels between the audience measurement and social media industries; to review the self-regulatory apparatus in place for the audience measurement industry; and to consider the lessons that the self-regulation of audience measurement might offer to the design and implementation of self-regulatory approaches to social media. Audience measurement and social media are similar in a number of respects. Audience measurement suffers from an absence of robust competition, which may be reflective of the innate characteristics of the industry. Social media, like many sectors of the platform economy, seems to be exhibiting similar tendencies toward monopoly. Also, like social media platforms, the activities of audience measurement systems can have significant social repercussions. Potential under-representation of minority audiences has raised concerns about cultural diversity; not unlike the way concerns about fake news and filter bubbles on social media have raised concerns about whether these platforms are discouraging access to diverse and trustworthy sources of news. Finally, both audience measurement firms and social media platform share an interest in keeping significant details of their operations proprietary. In the case of audience measurement, key methodological details are kept proprietary for competitive reasons. Similarly, in social media, the details regarding algorithmic curation and recommendation systems are kept largely proprietary. Both audience measurement systems and social media algorithms frequently are characterized as “black boxes.” The audience measurement industry operates under a self-regulatory model overseen by a multi-stakeholder consortium known as the Media Rating Council (MRC). The MRC has two primary responsibilities: setting standards and accreditation. In the standard-setting realm, the MRC establishes and maintains minimum standards pertaining to the quality and the integrity of the process of audience measurement. In terms of accreditation, the MRC conducts confidential audits of audience measurement systems in order to certify that they are meeting minimum standards of methodological rigor and accuracy. This paper will explore whether, in its role in establishing and applying standards of accuracy, reliability, and rigor to the increasingly complex process of audience measurement, the MRC offers a potentially useful template for more rigorous oversight and evaluation of the processes of algorithmic design, implementation, and adjustment that are central to the operation of social media platforms. In conducting this analysis, this paper will draw upon the origins and history of the MRC, examples of its activity and output, and critiques and supportive positions regarding its necessity and effectiveness, in an effort to identify lessons and practices that could potentially inform self-regulatory initiatives for social media.

Keywords: Social media, algorithms, self-regulation, audience measurement

JEL Classification: L5, L86

Suggested Citation

Napoli, Philip M., What Social Media Platforms Can Learn from Audience Measurement: Lessons in the Self-Regulation of 'Black Boxes' (January 31, 2018). TPRC 46: The 46th Research Conference on Communication, Information and Internet Policy 2018. Available at SSRN: https://ssrn.com/abstract=3115916 or http://dx.doi.org/10.2139/ssrn.3115916

Philip M. Napoli (Contact Author)

Duke University ( email )

Durham, NC 27708-0204
United States

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