Scholarly article on topic 'Relationships Between Social TV and Enjoyment: A Content Analysis of The Walking Deads Story Sync Experience'

Relationships Between Social TV and Enjoyment: A Content Analysis of The Walking Deads Story Sync Experience Academic research paper on "Media and communications"

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Academic research paper on topic "Relationships Between Social TV and Enjoyment: A Content Analysis of The Walking Deads Story Sync Experience"

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social media + society

Relationships Between Social TV and Enjoyment: A Content Analysis of The Walking Dead's Story Sync Experience

Social Media + Society July-September 2016: 1-12 © The Author(s) 2016 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/2056305116662170 sms.sagepub.com

Lauren A. Auverset and Andrew C. Billings

Abstract

Some of the most influential conversations about television shows are happening via Twitter, in a process dubbed Social TV. Content analysis of publicly available conversations of The Walking Dead was facilitated through AMC's Story Sync, facilitating Twitter responses. Results show that Social TV is about far more than isolated immediate reactions to television content; rather, Social TV involves behaviors and cognitions before, during, and after consuming TV programming.

Keywords

Twitter, social media, social TV, second screen, content analysis

Facilitating mediated communication among viewers of television programs in real time via the use of social networking sites and mobile applications has been classified as a type of "second screen" experience called "Social TV" (Ainasoja, Linna, Heikkila, Lammi, & Oksman, 2014; Giglietto & Selva, 2014; Nielsen, 2014a). Within such an experience, viewers devote one screen to watching the television program, while dedicating another (typically a smartphone, tablet, or laptop) to concurrently communicating with others about the television program. This new area of research is growing quickly (see Han & Lee, 2014; Ji & Raney, 2015; Van Cauwenberge, Schaap, & Van Roy, 2014), with scholars noting remarkable trends pertaining to the interactivity of Social TV that contribute greatly to a multitude of media content and effects-based lines of scholarship.

The act of watching television has always had a component focusing on the desire for a social, shared experience (Katz & Lazarsfeld, 1955). However, past decades facilitated this experience largely via co-viewing the same program in the same room at the same time. More recently, the addition of social media and portable touchscreen devices (e.g., smartphones and tablets) have facilitated television program viewers to interact in real time. The result has been less co-viewing, along with increased tendencies to watch live megaevents (e.g., Super Bowl and Academy Awards) in real time to enable second-screen and Social TV experiences. More important, these public, social interactions are increasingly accessible for examination. While many researchers have examined the effects second-screen experiences have on viewers (e.g., Giglietto & Selva, 2014; Johns, 2012;

Nielsen, 2013; Smith & Boyles, 2012), further research is needed to expand on this knowledge and better understand the second-screen phenomenon, particularly in terms of how it mirrors and/or differentiates from Social TV.

This study adds to this vein of scholarship, examining a unique new entry into the Social TV experience: Story Sync. In 2012, the television channel AMC (home of popular programs including Breaking Bad, Mad Men, and The Walking Dead) began providing a service on its website allowing viewers to synchronize their commentary with others watching the program—even if people are consuming the program on different days and times. The service, dubbed Story Sync, allows people to have shared experiences while time shifting their viewing habits to fit their personal needs (AMC TV, 2013), decoupling the need to watch something live from the desire to participate in Social TV activity.

As the practice of Social TV increases in both popularity and use, some of the most influential conversations about television shows are happening via Twitter. Rare is the instance in which there is truly a "game-changing" advent to a media platform; however, second-screen viewership seemingly significantly alters and synchronizes two: television viewing and social media use. Previously passive television

The University of Louisiana at Lafayette, USA Corresponding Author:

Lauren A. Auverset, Department of Communication, The University of Louisiana at Lafayette, P.O. Box 43650, Burke-Hawthorne Hall 101, Lafayette, LA 70504, USA. Email: laauverset@louisiana.edu

Creative Commons Non Commercial CC-BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

viewing becomes both active and, indeed, interactive. Concurrently, other forms of a person's social, computermediated identity are intermingled with their television tastes and opinions, making what one consumes more integrally tied to who one purports to be—at least in online form. Thus, viewers use Twitter (via smartphone, tablet, or laptop) to comment on and discuss television programs in real time. Through "live-tweeting" a television program, viewers are able to instantly connect with others who are also watching the show, thereby increasing the social aspects of television viewing. According to Nielsen Social (2014), 36 million people in the United States sent 990 million tweets about television shows in 2013. This is one of the primary reasons why Nielsen Social was created to track digital conversations about television shows using Twitter (Nielsen, 2014b). While other social network sites (e.g., Facebook and Tumblr) generate some social discussion through the use of Social TV, Twitter has generated the most dialogic, real-time, and publicly accessible discussions.

The cable television show The Walking Dead and the social network site Twitter were chosen to collect data for three unique reasons. First, AMC actively encourages social media-centered discussions surrounding The Walking Dead through the use of Story Sync (AMC TV, 2013). Second, Nielsen Social (2014) recently launched a new service called "Nielsen Twitter TV Ratings," with the specific purpose of measuring the total activity and reach of TV-related conversation on Twitter. Third, The Walking Dead ranked first in Nielsen Social's report, "2014 Top 10 Series on Twitter," with an average of 576,000 tweets per episode (Nielsen Social, 2014). The combination of these three factors make Twitter and The Walking Dead stand out as clear choices for analysis.

Within this new platform, the current project utilizes content analysis to explore the relationship between audience social media commentaries and contemporary broadcast scenes in popular television. Through a content analysis of publicly available conversations (via Twitter) during the first three episodes of The Walking Dead, Season 5 (airing October 2014), this study sheds light on key factors motivating viewers to interact with others about the television show on social media while simultaneously viewing the television episode.

Literature Review

The manner in which people communicate has fundamentally changed because of the rise in social media use, allowing individuals to stay constantly connected while providing a continuous means of mediated interaction. Along with increased use and widespread adoption of social networking sites (e.g., Facebook, Twitter, YouTube, Instagram, and Pinterest), communication researchers have frequently employed the uses and gratifications perspective to examine why people find this medium so appealing (Lenhart, Purcell, Smith, & Zickuhr, 2010). Social relationships are frequently

the object of uses and gratifications applications within social media platforms (Raacke & Bonds-Raacke, 2008; Valenzuela, Park, & Kee, 2009) with scholars also focusing on the negotiation of self-representation within these networks (Bonds-Raacke & Raacke, 2010; Quan-Haase & Young, 2010). Similarly, a growing number of studies (e.g., Ainasoja et al., 2014; Doughty et al., 2012; Greer & Ferguson, 2011; Han & Lee, 2014; Harrington, Highfield, & Bruns, 2013; Ji & Raney, 2015; Johns, 2012; Van Cauwenberge et al., 2014) examining the second-screen experience have also utilized the uses and gratifications approach to better understand motivations for participating in this form of second-screen use. For the purposes of this study, Giglietto and Selva's (2014) definition of Social TV will be incorporated, which they define as "the interactions among other viewers and between viewers, the characters, and the producers of the show enabled by the second-screen practice" (p. 260).

The second-screen environment is a unique mixture of social, interactive, and mass media; because of this, the uses and gratifications approach is particularly applicable to the second-screen experience as this mixture, at its core, is predominantly about choices within a plethora of available mediated communication options. A central assumption of the uses and gratifications perspective is that audience members proactively seek out a medium in an attempt to satisfy a specific need or gratification (Blumler & Katz, 1974) and also that media enjoyment (a gratification obtained) is influenced by many different social and psychological factors (Katz et al., 1974; Rubin, 2002; Ruggiero, 2000). A study by Wohn and Na (2011) applied Katz, Blumler, and Gurevitch's (1973) original five uses and gratifications need typologies to the analysis of the second-screen experience; these needs typologies theorize that exposure to media is dependent on the gratification an individual seeks to obtain from that particular medium (Katz, Blumler, & Gurevitch, 1974). The uses and gratifications approach explains media use as a cyclical function, where a combination of individual needs and sociopsychological factors create certain expectations, which affect patterns of media use (Katz et al., 1974). Patterns of media use eventually yield gratifications, which then revert to influence individual needs (Katz et al., 1973). The majority of uses and gratifications research on television use has examined how different expectations, motivations, and sociopsychological factors lead to different media use (Rubin, 1983; Rubin, Perse, & Powell, 1985).

Mobile Device Use and the Second Screen

The most recent Nielsen survey of connected device owners reported that almost half of U.S. smartphone users and tablet users (46% and 43%, respectively) reported using these devices at least once a day while watching television programs as a second screen (Nielsen, 2013). The Nielsen survey shows that viewers are using their second screen for both talking about or learning about the television program they

are watching and also for pursuing other activities related to their media consumption (e.g., seeking general information or browsing the web, visiting social networking sites, as well as commentaries about the program they are watching). Second-screen viewing has also become a popular practice for individuals watching political events such as the 2012 US presidential debates and the State of the Union address (Sasseen, Olmstead, & Mitchell, 2013). Television program producers openly encourage audience members to use the second screen to express their opinions, interact with fellow viewers, and engage with official TV program social media accounts via Facebook and Twitter (Giglietto & Selva, 2014). An official #hashtag is often provided by TV program producers and is shared on social networking sites as well as during the actual broadcast of the TV program—via placement of a "bug" in a corner of the broadcast television screen. This use of social networking sites as a real-time backchannel of communication among television program viewers has been well documented (Boyd, 2010; Johns, 2012).

Twitter and the Second Screen

Several academic studies have investigated the use of Twitter to discuss issues in real time (e.g., Doughty et al., 2012; Huang, Thornton, & Efthimiadis, 2010; Savage, 2011), yet only a relative few specifically examine the use of Twitter to discuss live television offerings (e.g., Ji & Raney, 2015; Schirra, Sun, & Bentley, 2014; Wohn & Na, 2011). Many may consider the content of tweets to be trivial in nature, yet recent studies examining the live tweeting of television programs report that one of the primary motivations for engaging in Social TV is the sense of community and connectedness experienced with others who share interest in the television program (e.g., Doughty et al., 2012; Harrington et al., 2013; Highfield, Harrington, & Bruns, 2013; Ji & Raney, 2015; Schirra et al., 2014). Savage (2011) demonstrates that "tweets range from the inane to the arresting. But taken together, they open a surprising window onto the moods, thoughts, and activities of society at large" (p. 18). For instance, Wohn and Na's (2011) analysis of live tweets surrounding a television program showed that 60% of these tweets contained either emotional or opinionated content.

Twitter enables viewers to participate in a shared, collective, experience via Social TV. Social TV users reported that watching television alone was a key motivation for live tweeting about a television show, suggesting that Social TV activity allows viewers to feel a sense of connectedness—as if they were watching the show with others (Schirra et al., 2014). Huang et al. (2010) identified that live-tweeting users utilize hashtags to create and participate in conversational "micro-memes"—time-sensitive, impromptu discussions surrounding a topic. Schirra et al. (2014) further demonstrated this point in that "conversational tagging is particularly relevant to television live-tweeting, as using show-specific hashtags helps categorize a particular tweet

while also providing a window into wider conversations with friends and strangers about a television show" (p. 2422).

Enjoyment and the Second Screen

Raney (2004) asserted that the level of enjoyment (or gratifications obtained) an individual gains from using a specific medium is more than just a function of a certain type of content; rather, it is also affected by the setting—or environment— in which a mediated event is experienced by an individual. Similarly, Denham (2004) incorporated uses and gratifications approaches, coupled with social psychology theories (social identity theory, disposition theory, and uncertainty reduction theory) to suggest that "social norms and viewing situations [of a medium] are ultimately as central to enjoyment as content is" (p. 370). Therefore, through analyzing the role these second-screen conversations play in the consumption and enjoyment of a television program, deeper understandings of viewer's entertainment experiences can be gained.

Brojakowski (2015) describes the changes that are occurring in television enjoyment by examining the differences in the evolution of television viewing from "a linear, push environment to an unending, viewer-generated pull environment" (p. 24). According to one study ("Digital set to surpass TV," 2014), media consumption (e.g., general Internet use, social media, and streaming television programs) surpassed television viewing for the first time as the dominant form of media use in the United States. Through the creation of interactive, digital content (e.g., applications such as TV Tag, Viggle, and AMC's Story Sync), television networks seem to have embraced Social TV as a new and innovative way to reach their audiences.

The Social Media Case of 'The Walking Dead"

Social media applications such as TV Tag (formerly "Get Glue") and online interactive platforms such as The Walking Dead's Story Sync exist specifically for the purpose of encouraging viewers to participate in the second-screen experience. Additionally, these social backchannels provide valuable real-time feedback to television show producers about their audience's reactions to their content. AMC's Story Sync for The Walking Dead (TWD) is an interactive, guided second-screen experience allowing viewers to vote in polls, answer trivia questions, offer opinions on what will happen next in the episode, and chat with other viewers about the show in real time (AMC TV, 2013). Viewers can participate in Story Sync via their smartphone, tablet, or Internet browser (thewalkingdeadstorysync.com) or via the new (debuting with Season 5) Windows 8 TWD Story Sync app during premieres of new episodes of the television program (AMC TV).

Story Sync is designed to be an interactive, social, second-screen TV viewing experience. There are four primary types of interactive elements within Story Sync: (a) a

judgment poll, asking Social TV participants about their opinion of a character's actions in the most recent scene; (b) a decision poll, asking Social TV participants what action they think the character(s) should make next or what decision the participant would make if they were in the character(s) situation; (c) a threat level meter, asking Social TV participants to rate how much of a threat a certain character or a specific situation is on a 5-point scale (1 = low threat to 5 = severe threat); and (d) a gore gauge, where Social TV participants are shown a freeze-frame picture along with a quote from the scene just after it aired, asking participants to rate how gruesome the image is on a 5-point scale (1 = barely bloody to 5 = total bloodbath).

Non-interactive elements that audience members encounter when using Story Sync include (a) quotes from a previous episode (the quote text is overlaid a still image from the scene the quote is from); (b) remember quizzes where the audience is asked to answer a multiple choice question about something that happened in a previous episode (the text is overlaid a still image from the scene the question is from); (c) flashback scenes where a short video clip from a previous episode is played; (d) freeze frame a poignant image from the current episode is highlighted; (e) kill shot where a still image of a character killing a zombie from the current episode is highlighted; (f) instant replay of an important scene from the current episode; and (g) weapon details about a specific weapon a character in the episode uses.

When TWD Story Sync debuted in 2012 for the show's Season 2 mid-season finale, it was one of the first of its kind to do so (AMC TV, 2013). Story Sync even has a separate set of commercial advertisements timed with the live broadcast, also facilitating the sharing of thoughts and opinions to social network sites, driving both social media buzz and overall awareness of AMC television programs (Bishop, 2014). The number of viewers using Story Sync during a Season 4 episode of TWD was comparable to the number of people live tweeting about the show (Bishop, 2014). Whether it is social media that is attracting viewers to Story Sync, or that Story Sync is encouraging more social media interaction is not yet clear, but the popularity of use of the second-screen is undoubtedly increasing among TWD viewers.

AMC has also included The Talking Dead, the companion talk show of TWD, in their Social TV endeavors. Pasztor and Korn (2015) focus on the Social TV aspects of The Talking Dead to present a new model of television engagement wherein they redefine the concept of parasociality (Horton & Wohl, 1956) as technoprosociality. While paraso-ciality, on one hand, describes the illusionary feeling that a viewer is engaged in a reciprocal relationship with an onscreen persona (e.g., fictional characters, talk show hosts, and celebrities) and is a one-way flow of communication (Rubin et al., 1985), technoprosociality, on the other hand, is the actual exchange of communication between a viewer and an on-screen persona and is a multi-flow form of communication (Pasztor & Korn, 2015). Pasztor and Korn

define technoprosociality as "the integration of social media technology to maximize audience engagement and interpersonal relationship development between celebrities and fans" (p. 184). The Talking Dead's innovative use of Social TV to engage with their audience through social media, online polls, and phone-ins helped to minimize the social distance felt between the show's live audience and the virtual audience, thereby increasing levels of engagement for all viewers (Pasztor & Korn, 2015).

As demonstrated above, previous studies have examined viewer second-screen use while viewing television programs; however, little research has examined how user motivations can be exemplified within engagement in second-screen activities. The extent to which a viewer's level of enjoyment of a television program is affected by the second-screen experience is still relatively unclear, warranting in-depth analysis of the enjoyment of the second-screen experience. Therefore, this study examines how viewers interact with The Walking Dead's Story Sync platform. Doing so can illuminate the gratifications obtained from this behavior while further clarifying the relationship between audience social media commentaries and contemporary broadcast scenes.

Additionally, one final element that has been linked to computer-mediated communication for quite some time (Walther & D'Addario, 2001) involves communication via emoticon use. Emoticons are "graphic representations of facial expressions" (Walther & D'Addario, 2001, p. 324). Unsurprisingly, emoticons are one of the most utilized nonverbal cues used to mediate interpersonal communication between Social TV users (Chorianopoulos & Lekakos, 2008). Chorianopoulos and Lekakos (2008) note that the use of emoticons among Social TV users "promotes a seamless and non-verbal communication among distant viewers" (p. 117). Emoticons are often used to supplement the lack of non-verbal elements on social networking sites such as Twitter (Park, Baek, & Cha, 2014), and similarly, this use of emoticons has been critical to debunking notions of computer-mediated communication being entirely lacking of non-verbal elements (Shao-Kang, 2008). As the use of emoticons has elevated in relevance in more recent years, evidenced by an emoji being named Merriam Webster's word of the year (Ziv, 2015), makes emoticon use within second-screen communication relevant and yet exploratory.

Research Questions

Given the potentially anomalous nature of The Walking Dead when coupled with the new innovation of Story Sync, potential areas of study can be delineated, yet relationships cannot be directly predicted. As such, five issues are tested, all in the form of research questions:

RQ . What message types are Social TV users most likely

to tweet about while watching The Walking Dead?

Table 1. 2 x 2 Matrix of Message Types and Categories (Giglietto & Selva, 2014).

Objective Subjective

Emotion (E) Attention-Seeking (A) INBOUND

Anticipation (AN) Attention-Emotion (AE)

Opinion (O) Interpretation (I) OUTBOUND

Objectivized Opinion (OI) Pure Information (II)

RQ . To what extent do viewers utilize official #hashtags provided by the television show producers/Story Sync?

RQ3a. What proportion of tweets directly refer to a scene featured in Story Sync?

RQ3b. What message types are most likely to emerge in tweets that directly refer to a scene featured in Story Sync?

RQ4a. What proportion of tweets use emoticons?

RQ . What message types are most likely to emerge in tweets that use emoticons?

RQ . What message types are most likely to emerge in tweets that are in response to an interactive Story Sync element?

Methodology

The universe of investigation for this study consisted of publicly available conversations posted to the social networking site Twitter during the premiere of the first three episodes of The Walking Dead, Season 5 (original air dates: 12 October, 19 October, and 26 October 2014). Tweets were gathered 1 hr before, during the episode, and in the hour immediately following the episode, when the related television program, The Talking Dead, airs. Data collection began on all 3 days at 7:00 p.m. (central standard time [CST]) and was extended until 2:00 a.m. (CST) to allow for data collection from viewers observing Pacific Standard Time, as this television program has a delayed premiere time for West coast viewers.

Data were gathered using DiscoverText, a cloud-based, collaborative text analytics platform. DiscoverText utilizes Twitter's "advanced search" options by implementing date and time restrictions to properly work within Twitter's algorithms. The total data collection for all three episodes resulted in a database of over 174,076 tweets (E1 = 59,797; E2 = 34,881; and E3 = 79,396). Next, the data were narrowed by removing all non-English tweets, along with all retweets, leaving only English language, original tweets for analysis. Of these tweets, every 60th tweet was analyzed to make the coding more manageable while also ensuring a stratified random sample within each episode. In all, 2,977 tweets were subject to analysis.

The tweets were selected for collection prior to the episode's premiere with specific #hashtags (official hashtags designated by AMC TV and communicated to viewers through social media and Story Sync). For Season 5,

Episode 1, tweets with the following hashtags were collected: #TWD, #TheWalkingDead, #NoSanctuary, #huntorbehunted, #TalkingDead, and #DeadBuzz. For Season 5, Episode 2, tweets with the following hashtags were collected: #TWD, #TheWalkingDead, #Strangers, #huntorbehunted, #WhoIsFatherGabriel, #TalkingDead, and #DeadBuzz. For Season 5, Episode 3, tweets with the following hashtags were collected: #TWD, #TheWalkingDead, #FourWallsAndARoof, #huntorbehunted, #bobBQ, #AskAndrew, #TalkingDead, and #DeadBuzz. Words, phrases, and/or hashtags that "trended" on Twitter during this time were also monitored and included using Nielsen Twitter TV Ratings (Nielsen, 2014b); any unanticipated trends were retroactively collected for analysis as needed via the DiscoverText GNIP importer.

The codebook was modified from Giglietto and Selva's (2014) coding scheme examining second-screen use of Twitter during political talk shows. Each tweet analyzed was coded as one of the eight message types, and each message type fits into one of the four message categories (subjective, objective, and inbound, or outbound, outlined in Table 1).

The first six message types were derived from Giglietto and Selva's coding scheme and, as Table 1 shows, are defined as the following: Attention-Seeking (A) messages contain text with @mentions (non-RT's) directed toward the producers, directors, actors, or characters in TWD and/or text ending in a question (non-rhetorical); Emotion (E) messages contain text with words expressing feelings or emotions such as excitement, fear, hate, anger, text written in ALL CAPS, tweets with multiple exclamation points, emoticons, and so on; Interpretation (I) messages contain text expressing an opinion framed by a clear reference (e.g., a quote or description of the scene) to the content broadcast during a scene; Pure Information (II) messages contain text that is purely informational (without opinionated or emotional influence), information about the program (e.g., "#TWD airs in just 15 mins on @AMC"), and announcements about what is happening or going to happen next; Opinion-Observation (O) messages contain text where the presence of personal pronouns is the dominant feature (e.g., "I think . . ." and "In my opinion . . ."); and Objectivized Opinion (OI) messages contain tweets clearly expressing an opinion without openly presenting it as such (i.e., a tweet expressing an opinion but not fitting into one of the aforementioned categories).

Because of their substantiated links to other forms of media enjoyment (see Raney, 2006), two additional message categories were developed specifically for the purposes of

Table 2. Descriptive Statistics for Message Types Found in Tweets for Season 5, Episodes 1 -3.

Message type Season 5, Episode 1 Season 5, Episode 2 Season 5, Episode 3 Season 5, Episodes 1-3

Count Percentage Count Percentage Count Percentage Count Percentage

Attention-Seeking 78 7.8 108 10.8 223 23.8 419 14.1

Emotion 165 16.5 177 17.8 144 14.7 486 16.3

Interpretation 104 10.4 143 14.3 267 27.2 514 17.3

Pure Information 56 5.6 93 9.3 80 8.2 229 7.7

Opinion-Observation 79 7.3 150 15.0 46 4.7 269 9.0

Objectivized Opinion 251 25.2 65 6.5 55 5.6 371 12.5

Anticipation 249 24.9 194 19.5 144 14.4 587 19.7

Attention-Emotion 22 2.2 67 6.7 12 1.2 101 3.4

Total 998 100 997 100 981 100 2976 100

this study: Anticipation (AN) messages contain text expressing a sense of urgency about an upcoming episode or scene (e.g., "5 mins til #TWD!") and Attention-Emotion (AE) messages contain text with @mentions (non-RT's) directed toward the producers, directors, actors, or characters in TWD; text ending in a question (non-rhetorical); and text containing words expressing feelings or emotions such as excitement, fear, hate, anger, text written in ALL CAPS, tweets with multiple exclamation points, and emoticons.

Two coders were trained by independently coding a randomly selected section of tweets, consisting of 10% of the total sample. Using Cohen's (1960) kappa statistic for determining acceptable levels of intercoder reliability, consistency was found among coders (K = .917).

Results

The first research question (RQ1) queried the message types emerging in tweets about The Walking Dead. Table 2 reports the overall message types occurring within the tweets that were subject to analysis.

As Table 2 shows, the use of Twitter to express anticipation for the upcoming episode was the most frequent overall (n = 587, 19.7%) and also during Episode 1 (n = 249, 25%) and Episode 2 (n = 194, 19.5%). While there was a slight decline in anticipatory tweets in Episode 3 (n = 144, 14.3%), interpretive tweets were significantly higher than in the first two episodes at 27.2% (n = 267), compared to 10.4% (n = 104) in Episode 1 and 14.3% (n = 143) in Episode 2.

Research Question 2 examined the extent to which viewers used official hashtags provided and promoted by the television show producers and Story Sync. These included (a) hashtags superimposed in the bottom right corner of the television screen during airtime, (b) hashtags seen in social media shared and officially promotional images shared prior to the episode's airtime (e.g., #huntorbehunted and #whois-fathergabriel), (c) hashtags promoted via Story Sync, and (d) hashtags used by the official AMC The Walking Dead Twitter account in relation to the episode (e.g., #TWD, #TheWalkingDeadTONIGHT, and #TalkingDead). Figures 1

to 3 show the frequencies of each hashtag tracked, per hour, for each episode.

As Figures 1 to 3 show, there is a significant increase in promoted hashtag use between 11:00 p.m. and 1:00 a.m. CST. This increase in hashtag use suggests that viewers using Social TV while watching The Walking Dead also time shift their viewing habits.

Research Question 3a examined the proportion of tweets directly referring to a specific scene featured in Story Sync. Table 3 shows descriptive statistics and the percentage counts for the Story Sync referenced tweets for each message type.

As Table 3 illuminates, of the data subject to analysis, 323 tweets (10.9% of the total number of tweets analyzed, n=2,976) directly referenced a featured question or moment featured during Story Sync, providing a succinct answer to RQ3a.

Research Question 3b queried the most prevalent types of messages within Story Sync tweets. Referencing back to Table 3, of the tweets directly referencing a featured question or moment from Story Sync, a full two thirds (n = 213, 65.9%) of the tweets were classified as the "interpretation" message type, with the next highest message type being "emotion" at 13% (n = 42). A cross tabulation and a chi-square test revealed that tweets directly referencing a featured question or moment from Story Sync were significantly more likely to be the "interpretation" message type category than all other message type categories combined (X2 (7) = 627.18, p < .001).

Research Question 4a concerned the proportion of emoti-cons prevalent in tweets within the programs. Table 4 reports the frequency of emoticon use by message type.

As Table 4 shows, of the data subject to analysis, 404 tweets (13.6% of the total number of tweets analyzed, n = 2,976) used emoticons, answering RQ4a.

Research Question 4b concerned message types embedded within emoticon-based tweets. Again utilizing Table 4, of the tweets that used emoticons, 31.9% (n = 129) of the emoticon tweets were classified as the "anticipation" message type, with the next highest being, not surprisingly, the "emotion" message type at 22.8% (n = 92). A cross tabulation and a chi-square test revealed that tweets that used emoticons

Figure 1. Season 5, Episode 1 hashtag frequencies per hour.

Figure 2. Season 5, Episode 2 hashtag frequencies per hour.

were significantly more likely to fall into the "anticipation" and "emotion" message types over any other message type category (x2 (7) = 84.56, p < .001).

The final research question (RQ4c) pertained to the message type of the tweets made in response to an interactive Story Sync element, specifically (a) a judgment poll, asking Social TV participants about their opinion of a character's actions in the most recent scene; (b) a decision poll, asking Social TV participants what action they think the character(s) should make next or what decision the participant would make if they were in the character(s) situation; (c) a threat level meter, asking Social TV participants to rate how much of a threat a certain character or a specific situation is on a

5-point scale (1 = low threat to 5 = severe threat); and (d) a gore gauge, where Social TV participants are shown a freeze-frame picture along with a quote from the scene just after it aired, asking participants to rate how gruesome the image is on a 5-point scale (1 = barely bloody to 5 = total bloodbath). Table 5 shows descriptive statistics and the percentage counts for the specific Story Sync interactive element referenced tweets for each message type.

As highlighted in Table 5, of the data subject to analysis, 323 tweets (10.9% of the total number of tweets analyzed, n = 2,976) directly referenced a featured question or moment featured during that episodes Story Sync; of those, 92 tweets (28.5%) directly referenced a judgment poll question. Of the

Figure 3. Season 5, Episode 3 hashtag frequencies per hour.

Table 3. Descriptive Frequency Statistics for Message Type of Story Sync-Referenced Tweets. Message type

Attention- Emotion Interpretation Pure Opinion- Objectivized Anticipation Attention- Total

Seeking Information Observation Opinion Emotion

Count 16 42 213 4 21 22 1 4 323

Percentage within 5.0 13.0 65.9 1.2 6.5 6.8 0.3 1.2 100.0

Story Sync

Percentage within 3.8 8.6 41.4 1.7 7.8 5.9 0.2 4.0 10.9

message type

Table 4. Descriptive Frequency Statistics for Message Type of Tweets Using Emoticons. Message type

Attention- Emotion Interpretation Pure Opinion- Objectivized Anticipation Attention- Total

Seeking Information Observation Opinion Emotion

Count 32 92 36 24 25 51 129 15 404

Percentage within 7.9 22.8 8.9 5.9 6.2 12.6 31.9 3.7 100.0

emoticons

Percentage within 7.6 18.9 7.0 10.5 9.3 13.7 22.0 14.9 13.6

message type

tweets directly referencing a Story Sync judgment poll question, 65% (n = 60) were coded in the "interpretation" message type, and 19.5% (n = 18) of these tweets were classified as the "emotion" message type. Of the tweets directly referencing content within Story Sync, 112 tweets (35%) directly referenced a decision poll question. The majority of tweets directly referencing a Story Sync decision poll question were coded in the "interpretation" message type (80.4%,

n = 90), significantly more than all other categories combined (x2(7) = 36.15, p < .001).

Of the tweets directly referencing content within Story Sync, 59 tweets (18.3%) directly referenced a threat level meter question with nearly half (n = 28; 47.5%) coded as the "interpretation" message type and nearly a quarter (n = 14; 23.7%) coded as the "objectivized opinion" message type. Of the tweets that directly referenced content within Story

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Sync, 28 (8.7%) tweets directly referenced a gore gauge question, with half (n = 14; 50%) coded as the "interpretation" message type, and the second most (n = 6; 21.4%) classified as the "opinion observation" message type. Thus, Research Question 5c is answered in that the "interpretation" message type is the primary message type directly responding to an interactive Story Sync element.

Discussion

This study clarified the relationship between the use of Social TV and related comments on the social network site, Twitter, within the popular television offering, The Walking Dead. The use of emoticons was also examined in an effort to better understand viewers' use of emoticons as non-verbal cues in Social TV commentary. Contributions abound when relating the results to larger understandings of media consumption and its relationship to the use of other screens, devices, and platforms.

First, the prominence of comments relating to anticipation of events is noteworthy, as the majority of Social TV research focuses on reactions to content, not reactions to content that has not yet aired. Roughly one fifth of all comments could be classified as anticipatory in nature, meaning that viewers do not merely seek to react to these types of dramatic serial offerings but also wish to interact with people to build potential enjoyment when the content ultimately does air.

Second, the study illuminated how set hashtags amplify over the course of time, with results showing peak use of hashtags several hours after an episode aired on the East Coast. While this could be a function of the East Coast still discussing an episode while the West Coast is still initially consumed in its initial airing, the magnitude of the increase suggests other issues are at play. For instance, the hashtags appear to be a funneling mechanism over time, with initial comments being more scattershot and later comments seeking more focused, deeper conversations based on the primary events that unfolded.

Third, approximately 11% of the total number of tweets analyzed directly referenced a featured question or moment featured in Story Sync. Given the novelty of the Story Sync feature, such a result likely shows that these types of offerings will increase over time, offering Social TV in more controlled, focused form, apparently fulfilling a desire of a significant portion of Social TV participants.

Fourth, the interpretation message typology was the most prevalent of all categories, accounting for two thirds of all Story Sync tweets. Moreover, the majority of the subjective, outbound, interpretive Story Sync tweets (71.3%) directly referenced either a judgment poll or a decision poll question. Interestingly, the two most prevalent message typologies in Episode 1 (objectivized opinion and anticipation) were barely or not at all (respectively) represented by the Story Sync tweets, suggesting that Story Sync is offering something unique from the generalized social media experience found on Twitter.

Finally, the use of emoticons is noteworthy, warranting future study at least partly because they were not predominantly used to render comments in the "emotion" category. Instead, anticipation—certainly a correlate, yet not a surrogate for emotion—was the most prevalent type of comment featuring emoticon use. Subsequent scholarship should explore the incorporation of such visual renderings; while seeming innocuous, emoticons are becoming an increasingly prevalent form of communication within online networks (e.g., Jhih-Syuan & Pena, 2011; Park et al., 2014).

Overall, Social TV participants used Twitter to express their personal opinions in the form of scene interpretation, emotional reactions to characters and scenes, and to communicate their anticipatory feelings about the upcoming episode. While these opinions are sometimes addressed to other users—or to the television program or the actor's official Twitter accounts—often these opinions addressed a non-specific, imagined audience (Marwick & Boyd, 2011) of similarly like-minded The Walking Dead fans who might also be engaged in Social TV during an episode premiere.

At first glance, it may seem as though no clear pattern or correlation of the message types was found in the dataset to the use of AMC's Story Sync. The types of interactive elements in each episodes Story Sync, while consistent in style, are not consistent in quantity or format order (e.g., the first episode had five decision polls, as many as the other two episodes combined). However, a clear relationship between the types of Story Sync interactive elements and the message types of the tweets emerges when the specifics of each episode's Story Sync are compared to the message types dominating each episode. As such, the type of interactive elements in each Story Sync may influence the tone of the messages tweeted by Social TV users. Future studies could investigate this link between the interactive elements within each Story Sync (i.e., the order, amount of, and type of interactive elements) to determine whether a significant correlation exists among them.

Exploring the desire for interactivity within an otherwise presumed to be passive medium (television consumption) appears to be one major ramification for ancillary exploration of this work. This analysis of AMC's StorySync documents a rapt interest from an established cadre of fans, yet still represents a smaller subsection of the larger population who view this program, or certainly television, in general. As such, a clear need is established to explore more types of second-screen motivations, to determine whether the desire to interact within television is a mainstream or, conversely, a niche desire.

Additionally, future studies could explore how the entertainment industry measures the success of Social TV applications in areas such as augmenting viewership and bolstering audience engagement. Since Social TV is such a relatively new element of television entertainment and it is difficult to determine what ratings the program may garner without the Story Sync application, future research should investigate

the new awards categories now being issued for Social TV applications. Story Sync for The Walking Dead won the first place award for Best iPad or Tablet Social TV Application at the Social TV Summit's first annual awards show (AMC TV, 2014) and also won the first place award for Best Second-Screen TV App at Variety's first annual Entertainment App Awards show (AMC TV, 2012), demonstrating considerable impact within this new digital space.

As noted above, the attention-seeking message typology applies to tweets asking questions or addressing messages to either the official @WalkingDead_AMC account, @ AMCTalkingDead account, or featuring celebrity guest for that episode's Story Sync or The Talking Dead. This demonstrates how Twitter is used by Social TV participants to engage, either directly or through imagined interaction, in dialogue directly with persons responsible for building the content of the television program. Viewers are seemingly eager to increase the two-way flow of communication with television networks and actors, with Social TV functioning as a successful conduit for facilitating this communication. Future studies could examine this attention-seeking phenomenon in other areas of live-entertainment viewing, such as with sports stars or coaches after a game, with politicians after a public address or debate, or even with reality TV stars after the premiere of an episode.

Conclusion

This study incorporated second-screen media usage within two prongs of nuance: (a) the specific role of Social TV and (b) the direct influence of AMC's new offering, Story Sync. Within this approach, results indicated that Social TV is about far more than isolated immediate reactions to television content; rather, Social TV involves behaviors and cognitions before, during, and after consuming TV programming. As such, the potential for prolonged, deeper conversations warrants future investigation, as this study underscores how online communities build surrounding given programs and increasingly do so less organically, facilitated instead by network platforms designed to elevate discussions to more focused and sophisticated levels.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Ainasoja, M., Linna, J., Heikkila, P., Lammi, H., & Oksman, V. (2014). A case study on understanding 2nd screen usage during a live broadcast: A qualitative multi-method approach.

UBICOMM 2014: The Eighth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, Rome, Italy, 24-28 August, pp. 196-203.

AMC TV. (2012, November). The Walking Dead story sync wins variety award. Retrieved from http://www.amc.com/shows/ the-walking-dead/talk/2012/11/story-sync-variety-award

AMC TV. (2013, December). Story sync. Retrieved from http:// blogs.amctv.com/the-walking-dead/story-sync

AMC TV. (2014, July). The Walking Dead story sync receives social TV award nomination. Retrieved from http://www.amc. com/shows/the-walking-dead/talk/2013/07/the-walking-dead-story-sync-receives-social-tv-award-nomination

Bishop, B. (2014). How a second-screen app made "The Walking Dead" come alive. Retrieved from http://www.theverge.com/ entertainment/2014/2/13/5406498/how-a-second-screen-app-made-the-walking-dead-come-alive

Blumler, J. G., & Katz, E. (1974). The uses of mass communications: Current perspectives on gratifications research. Beverly Hills, CA: Sage.

Bonds-Raacke, J., & Raacke, J. (2010). MySpace and Facebook: Identifying dimensions of uses and gratification for friend networking sites. Individual Differences Research, 8, 27-33.

Boyd, E. (2010). Twitter drives crowds back to must-see, live, appointment TV. Retrieved from https://www.fastcompany. com/1701701/twitter-drives-crowds-back-must-see-live-appointment-tv

Brojakowski, B. (2015). Spoiler alert: Understanding television enjoyment in the social media era. In A. F. Slade, A. J. Narro, & D. Givens-Carroll (Eds.), Television, social media, and fan culture (pp. 23-42). Lanham, MD: Lexington Books.

Chorianopoulos, K., & Lekakos, G. (2008). Introduction to social TV: Enhancing the shared experience with interactive TV. International Journal of Human-Computer Interaction, 24, 113-120.

Cohen, J. A. (1960). Coefficient for agreement of nominal scales. Educational and Psychological Measurement, 20, 37-46.

Denham, B. E. (2004). Toward an explication of media enjoyment: The synergy of social norms, viewing situations, and program content. Communication Theory, 14, 370-387.

Digital set to surpass TV in time spent with US media. (2014). eMarketer. Retrieved from http://www.emarketer.com/Article/ Digital-Set-Surpass-TV-Time-Spent-with-US-Media/1010096

Doughty, M., Rowland, D., & Lawson, S. (2012, July). Who is on your sofa? TV audience communities and second screening social networks. In Proceedings of the 10th Association for Computing Machinery (ACM) European Conference on Interactive Television and Video (pp. 79-86). New York, NY: ACM Press.

Giglietto, F., & Selva, D. (2014). Second screen and participation: A content analysis on a full season dataset of tweets. Journal of Communication, 64, 260-277.

Greer, C. F., & Ferguson, D. (2011). Using Twitter for promotion and branding: A content analysis of local television Twitter sites. Journal of Broadcasting & Electronic Media, 55, 198-214.

Han, E., & Lee, S. W. (2014). Motivations for the complementary use of text-based media during linear TV viewing: An exploratory study. Computers in Human Behavior, 32, 235-243.

Harrington, S., Highfield, T., & Bruns, A. (2013). More than a backchannel: Twitter and television. Participations: Journal of Audience & Reception Studies, 10, 405-409.

Highfield, T., Harrington, S., & Bruns, A. (2013). Twitter as a technology for audiencing and fandom. Information, Communication & Society, 16, 315-339.

Horton, D., & Wohl, R. R. (1956). Mass communication and para-social interaction. Psychiatry, 19, 215-229.

Huang, J., Thornton, K. M., & Efthimiadis, E. M. (2010). Conversational tagging in twitter. In The proceedings of hypertext and hypermedia (pp. 173-178). New York, NY: Association for Computing Machinery (ACM).

Jhih-Syuan, L., & Pena, J. (2011). Are you following me? A content analysis of TV networks' brand communication on Twitter. Journal of Interactive Advertising, 12, 17-29.

Ji, Q., & Raney, A. A. (2015). Morally judging entertainment: A case study of live tweeting during "Downton Abbey." Media Psychology, 18(2), 221-242. doi:10.1080/15213269. 2014.956939.

Johns, M. D. (2012). Exploring the invisible backchannel of TV viewing. In M. Strano, H. Hrachovec, F. Sudweeks, & C. Ess (Eds.), Proceedings of cultural attitudes towards technology and communication (pp. 333-343). Murdoch, Western Australia, Australia: Murdoch University.

Katz, E., Blumler, J., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37, 509-523.

Katz, E., Blumler, J., & Gurevitch, M. (1974). Utilization of mass communication by the individual. In E. Katz & J. Blumler (Eds.), The uses of mass communication (pp. 19-32). Beverly Hills, CA: SAGE.

Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications. Glencoe, IL: Free Press.

Lenhart, A., Purcell, K., Smith, A., & Zickuhr, K. (2010). Social media and mobile Internet use among teens and young adults. Retrieved from www.pewinternet.org/Reports/2010/Social-Media-and-Young-Adults.aspx

Marwick, A. E., & Boyd, D. (2010). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13, 114-133.

Nielsen. (2013). Action figures: How second screens are transforming TV viewing. Retrieved from http://www.nielsen.com/us/en/ insights/news/2013/action-figures-how-second-screens-are-transforming-tv-viewing.html

Nielsen. (2014a). The cross-platform report. New York, NY. Retrieved from http://www.nielsen.com/us/en/insights/reports/2014/more-of-what-we-want.html

Nielsen. (2014b). Social TV. Solutions. Retrieved from http:// www.nielsensocial.com/living-social-how-second-screens-are-helping-tv-make-fans/

Nielsen Social. (2014). Nielsen Twitter TV ratings. Retrieved from http://www.nielsensocial.com/product/nielsen-twitter-tv-ratings

Park, J., Baek, Y. M., & Cha, M. (2014). Cross-cultural comparison of nonverbal cues in emoticons on Twitter: Evidence from big data analysis. Journal of Communication, 64, 333-354.

Pasztor, S. K., & Korn, J. U. (2015). Redefining parasociality as technoprosociality in AMC's #TalkingDead. In A. F. Slade, A. J. Narro, & D. Givens-Carroll (Eds.), Television, social media, and fan culture (pp. 183-200). Lanham, MD: Lexington Books.

Quan-Haase, A., & Young, A. L. (2010). Uses and gratifications of social media: A comparison of Facebook and instant messaging. Bulletin of Science, Technology & Society, 30, 350-361.

Raacke, J., & Bonds-Raacke, J. (2008). MySpace and Facebook: Applying the uses and gratifications theory to exploring friend-networking sites. Cyber Psychology & Behavior, 11, 169-174.

Raney, A. A. (2004). Expanding disposition theory: Reconsidering character liking, moral evaluations, and enjoyment. Communication Theory, 14, 348-369.

Raney, A. A. (2006). Why we watch and enjoy mediated sports. In A. Raney & J. Bryant (Eds.), Handbook of sport and media (pp. 313-329). Mahwah, NJ: LEA.

Rubin, A. M. (1983). Television uses and gratifications: The interactions of viewing patterns and motivations. Journal of Broadcasting, 27, 37-51.

Rubin, A. M. (2002). The uses and gratifications perspective of media effects. In J. Bryant & D. Zillmann (Eds.), Media effects: Advances in theories and research (2nd ed., pp. 525-548). Mahwah, NJ: Lawrence Erlbaum.

Rubin, A. M., Perse, E. M., & Powell, R. A. (1985). Loneliness, parasocial interaction, and local television news viewing. Human Communication Research, 12, 155-180.

Ruggiero, T. E. (2000). Uses and gratifications theory in the 21st Century. Mass Communication & Society, 3, 3-37.

Sasseen, J., Olmstead, K., & Mitchell, A. (2013). The state of the news media 2013. Retrieved from http://stateofthemedia. org/2013/digital-as-mobile-grows-rapidly-the-pressures-on-news-intensify/#the-second-screen-phenomenon

Savage, N. (2011). Twitter as medium and message. Communications of the ACM, 54, 18-20.

Schirra, S., Sun, H., & Bentley, F. (2014, April 26). Together alone: Motivations for live-tweeting a television series. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems—CHI '14 (pp. 2441-2450). Toronto, Ontario, Canada.

Shao-Kang, L. (2008). The nonverbal communication functions of emoticons in computer mediated communication. CyberPsychology & Behavior, 11, 595-597.

Smith, A., & Boyles, J. (2012). The rise of the "Connected Viewer." (Pew Internet & American Life Project, 12). Retrieved from www.pewinternet.com/~/media/Files/Reports/2012/PIP_ Connected_Viewers.pdf Valenzuela, S., Park, N., & Kee, K. F. (2009). Is there social capital in a social network site? Facebook use and college students' life satisfaction, trust, and participation. Journal of ComputerMediated Communication, 14, 875-901. Van Cauwenberge, A., Schaap, G., & Van Roy, R. (2014). "TV no longer commands our fullattention": Effects of second-screen viewing and task relevance on cognitive load and learning from news. Computers in Human Behavior, 38, 100-109. Walther, J. B., & D'Addario, K. P. (2001). The impacts of emotions on message interpretation in computer-mediated communication. Social Science Computer Review, 19, 324-347. Wohn, D. Y., & Na, E. K. (2011). Tweeting about TV: Sharing television viewing experiences via social media message streams. First Monday, 16. Retrieved from www.firstmonday.org/ojs/ index.php/fm/article/view/3368 Ziv, S. (2015, December 15). Merriam Webster's word of the year is also not a word. Newsweek. Retrieved from http:// www.newsweek.com/merriam-websters-word-year-also-not-word-405382

Author Biographies

Lauren A. Auverset (MS, University of Louisiana at Lafayette) is a PhD Candidate at the University of Alabama, and an Instructor of Communication at the University of Louisiana at Lafayette. Her research interests focus on the effects that new media use and interactivity have on organization-public relationships.

Andrew C. Billings (PhD, Indiana University, 1999) is the Ronald Reagan Chair of Broadcasting at the University of Alabama. His research interests focus on societal implications arising from shifts in media content and consumption.