Media and Communication, Volume 8, Issue 3
Deadline: November 15, 2019
Editor(s): Johannes Breuer (GESIS—Leibniz Institute for the Social Sciences, Germany), Tim Wulf (LMU Munich, Germany) and M. Rohangis Mohseni (TU Ilmenau, Germany)
- Submission of Abstracts: 1-15 November 2019
- Submission of Full Papers: 15-30 March 2020
- Publication of the Issue: July/September 2020
Since its subject of study is changing constantly and rapidly, research on media entertainment has to be quick to adapt. This need to quickly react and adapt not only relates to the questions researchers need to ask but also to the methods they need to employ to answer those questions. For several decades now, the large majority of quantitative research on the content, uses, and effects of media entertainment has been based on data from surveys, manual content analyses, or lab experiments. While there is no doubt that these studies have produced numerous important insights into media entertainment, they have certain limitations, some of which may entail significant biases. For example, several recent studies have shown that self-reports of media use tend to be unreliable. This is especially problematic if researchers are interested in very specific, rare, or socially undesirable forms of media entertainment. Experimental lab studies, on the other hand, tend to have relatively small samples and often occur in somewhat unnatural settings. And manual content analyses are not suitable for the large amounts of data that new forms of media entertainment generate (e.g., comments on YouTube videos). Over the last few years, the nascent field of computational social science has been developing and using methods for the collection and analysis of data that can help to address some of the limitations of traditional methods. For example, the use of digital trace data, such as data collected via APIs or tracking apps/plugins, can alleviate some problems associated with self-report data, and methods from the area of machine learning can be used to (semi-)automatically analyze large amounts of media content (or reactions to it). For this thematic issue, we invite substantive as well as methodological contributions that employ computational methods—either standalone or in combination with traditional methods—to study the content, uses, and effects of media entertainment. Submissions should either apply computational methods to investigate the content, uses or effects of media entertainment (studies that combine different types/sources of data, such as surveys and digital trace data, are especially welcome) or present and discuss novel computational methodologies for collecting and/or analyzing data on the content, uses or effects of entertainment media.
We invite two types of submissions: (1) late-breaking brief reports (of no longer than 3000 words, inclusive of all manuscript elements) and (2) longer-format manuscripts (of no longer than 6000 words, inclusive of all manuscript elements). Submissions engaging in open science practices will be given particular consideration in the review process (for some practical primers on the adoption of open science practices see https://how-to-open.science or http://psych-transparency-guide.uni-koeln.de). We also especially welcome preregistered studies (for an introduction to preregistration see https://how-to-open.science/plan/preregistration/why or http://psych-transparency-guide.uni-koeln.de/preregistration.html).
Instructions for Authors: Authors interested in submitting a paper for this issue are asked to consult the journal’s instructions for authors and send their abstracts (about 250 words, with a tentative title and reference to the thematic issue) by email to the Editorial Office (email@example.com).
Open Access: The journal has an article publication fee to cover its costs and guarantee that the article can be accessed free of charge by any reader, anywhere in the world, regardless of affiliation. We defend that authors should not have to personally pay this fee and advise them to check with their institutions if funds are available to cover open access publication fees. Institutions can also join Cogitatio’s Membership Program at a very affordable rate and enable all affiliated authors to publish without incurring any fees. Further information about the journal’s open access charges and institutional members can be found here.