Political relevance in the eye of the beholder: Determining the substantiveness of TV shows and political debates with Twitter data

Abstract

Addressing the call to move beyond a simple genre classification of TV shows as either substantive (hard) news or non-substantive (soft) infotainment, we propose using social media reactions to determine a program’s political relevance. Such an approach provides information that goes beyond genre or content characteristics and reflects what really reaches an audience. Analyzing tweets about two Dutch talk shows and four U.S. primary debates, we show that audience responses to television programs differ considerably regarding their political relevance. Thereby, we demonstrate how examining online audience reactions can be employed as a sophisticated and valid way to assess the political relevance of TV programs.

Moral Expressions in 280 Characters or Less: An Analysis of Politician Tweets Following the 2016 Brexit Referendum Vote

“…After removal of stopwords, the 10 most frequent words per moral foundation that were not commonly shared with the other foundations were extracted (i.e., the word did not appear in the top 50 most frequently used words in the other foundations), and calculated the log-likelihood (LL) value to indicate overuse or underuse respectively, in one foundation relative to tweets that are not labeled in that foundation ( Boukes and Trilling, 2017 ; Arlt et al, 2019 ). In other words, the LL value shows how frequently a word appears in one group of tweets over another (i.e., belonging to one moral foundation over others).…”

Saving social media data: Understanding data management practices among social media researchers and their implications for archives

“…Social scientists use social media data to study a range of topics such as economic and consumer behavior (Antenucci, Cafarella, Levenstein, Ré, & Shapiro, 2014;Asur & Huberman, 2010) , cultural differences (Hochman & Schwartz, 2012) , social capital (Ellison, Vitak, Gray, & Lampe, 2014;Gil de Zúñiga, Jung, & Valenzuela, 2012) , feminist and anti-racist movements (Brock, 2012;Dixon, 2014;Freelon, McIlwain, & Clark, 2016) , political activism (Boulianne, 2015;Freelon, 2015;Roback & Hemphill, 2013) , the relationship between social and traditional media (Jungherr, 2014;Papacharissi & de Fatima Oliveira, 2012;Shapiro & Hemphill, 2017;Soroka, Daku, Hiaeshutter-Rice, Guggenheim, & Pasek, 2018) , and the impact and reach of research (Haustein et al, 2016;Thelwall, Haustein, Larivière, & Sugimoto, 2013) . In our analysis of research that used Twitter data we found a similar breadth of research topics, ranging from audience interactions around television shows (Boukes & Trilling, 2017;e.g., Williams & Gonlin, 2017) to social justice movements under hashtags such as #Ferguson (e.g., Barnard, 2017) , and many political discussions around the world (e.g., . Several studies used Twitter to characterize social networks of followers of particular hashtags, to test its effectiveness as a communication medium, or to identify characteristics of tweets associated with concepts like trustworthiness or utility.…”

The Twitter parliamentarian database: Analyzing Twitter politics across 26 countries

“…The results are shown in Fig 4 . While the set-based method used here is intuitive and therefore useful for exploratory analysis, a common statistical way of comparing corpora for the most overrepresented words is Log-Likelihood [ 84 ]. This analysis can be found in S3 Table , and largely matches the results in Fig 4 .…”