Analyzing Blending Social and Mass Media Audiences Through the Lens of Computer-Mediated Discourse

Analyzing Blending Social and Mass Media Audiences Through the Lens of Computer-Mediated Discourse

Asta Zelenkauskaite
Copyright: © 2018 |Pages: 25
DOI: 10.4018/978-1-5225-5637-4.ch050
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In recent years, mass media content has undergone a blending process with social media. Large amounts of text-based social media content have not only shaped mass media products, but also provided new opportunities to access audience behaviors through these large-scale datasets. Yet, evaluating a plethora of audience contents strikes one as methodologically challenging endeavor. This study illustrates advantages and applications of a mixed-method approach that includes quantitative computer-mediated discourse analysis (CMDA) and automated analysis of content frequency. To evaluate these methodologies, audience comments consisting of Facebook comments and SMS mobile texting to Italian radio-TV station RTL 102.5 were analyzed. Blended media contents through computer-mediated discourse analysis expand horizons for theoretical and methodological audience analysis research in parallel to established audience analysis metrics.
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Social media have transformed not only media consumption and production, but also the audiences (Livingstone, 2004). Increased non-professional audience content integration in mass media settings thus is an observed trend, rather than an exception (Karlsen, Sundet, Syvertsen, & Ytreberg, 2009). User contributions through social media in the past years constitute a large variety of online content exchanges (Doyle, 2010). In addition to call-in participation, mass media companies have expanded audience contributions through text-based communication, known as “backchannels” (Herring, 2004). Audiences contribute to the mass media programs – ranging from entertainment to political debates, and news – with tweets, Facebook messages, and mobile texting. Currently, text-based participation expanded through social networking sites. Increased user contributions, particularly in online environments, have been ascribed to “prosumer culture” (Jenkins, 2006). Bruns (2010) discussed professional and amateur content blurring in terms of “produsage” where users not only read contents but also share, rate, and exchange comments on social media outlets. In light of increased social media, audience research is presented with new challenges to meaningfully analyzing audiences’ contents.

While challenging, text-based contributions opened opportunities to study audience behaviors where user-based programming has been particularly popular, especially in European, Latin American contexts with growing popularity in Asia. Audience contribution through text messaging formats was analyzed in Norwegian contexts (Beyer, Enli, Maasø, & Ytreberg, 2007; Enli, 2007). User interactions through text were studied in Italian settings (Zelenkauskaite & Herring, 2008a). Texting as a form of personal ads were analyzed in a Lithuanian (Zelenkauskaite & Herring, 2008b), Spanish and Colombian cases were compared by Mafé, Blas, and Tavera-Mesías (2010), to name a few. In these text-based, mediated environments, user interaction with the program was predominantly based via text-based technologies such as mobile phone texting. Subsequently, audience contributions were extended to social networking sites such as Facebook (Beyer et al., 2007; Enli, 2007; Enli & Syvertsen, 2007; Mafé et al., 2010; Zelenkauskaite & Herring, 2008a; Zelenkauskaite & Herring, 2008b).

With growing popularity of social media and increasing amount of user-based contents, methodological challenges are still lingering. Challenges include the large-scale dataset management, time sensitivity of user-generated content (UGC), the dynamic nature of the content, and no predefined consistency in content flows. These challenges, related to audience contributions, highlight the need to re-evaluated audiences’ analysis methodologically. The need to re-configure methodological trajectories in studying audiences becomes especially pronounced in the recent decade when media are becoming more multiplatform (Blythe & Cairns, 2009; Livingstone, 1999; 2004) in the context of “convergence culture” (Jenkins, 2006). Despite new opportunities that social media and interpersonal media bring to the study of mass media audiences, little research has been devoted to the analysis of text-based audience interactions through social media.

To address methodological issues, computer-mediated discourse analysis has proven to be a useful tool to analyze user communicative practices and behaviors in online settings (Herring, 2001; 2004; 2007). CMDA has been further extended to account for convergent media settings that combine mass media and social media (Herring, 2009; 2013). The goal here is to examine advantages and shortcomings of automated tools comparing them with manually coded CMDA and to evaluate the utility of CMDA as an approach to large-scale datasets.

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