[Page Nr. 129 starts here] Christopher Buschow, Beate Schneider and Simon Ueberheide Tweeting television: Exploring communication activities on Twitter while watching TV Published in: Communications – European Journal of Communication Research, 39 (2), 2014 Page 129-149 Abstract: “Social TV”, described as the use of social media such as Twitter or Facebook stimulated by TV programs, is highly topical in the television industry. Communication re-search has fallen behind in addressing this issue. In this paper we explore the simultaneous communication activities of Twitter users while watching TV. Additionally, we tested wheth-er different TV programs stimulate different communication activities. The main findings of our quantitative content analysis of approximately 30,000 messages show that communication within the Twitter community as well as evaluations of shows and actors are the main subjects of the explored tweets. We also found that different TV programs evoke different communi-cation activities. While talent shows produce expressions of fandom and critiques of the can-didates in the show, live events evoke a critical debate about the show itself and what’s hap-pening on screen. Political talk shows can stimulate a public discourse. Keywords: television, communication activities, Twitter, Social TV, content analysis DOI 10.1515/commun-2014-0009 1 Introduction Due to the emergence of new alternatives to linear TV reception such as web video, video on demand, and Internet TV, the future of traditional linear televi-[130]sion is generally consid-ered to be one of the most important issues facing today’s media industry. Such developments create a challenge for the players in the market and raise the question as to how they should react. As one strategy for legacy media companies, practitioners (e.g., Bulkeley, 2010; Erics-son ConsumerLab, 2011; Nielsen, 2011; Richardson, 2010) and researchers (e.g., Baillie, Fröhlich, and Schatz, 2007; Chorianopoulos and Lekakos, 2008; Geerts, 2009; Schatz, Baillie, Fröhlich, and Egger, 2008; Summa 2011) propose merging television and social media tech-nologies into ‘Social TV’. Social TV opens up new possibilities of value creation for TV pro-ducers and writers (Andrejevic, 2008), networks and channels (HMR International, 2011), advertisers (Zigmond and Stipp, 2010) as well as agencies (Nielsen, 2011). Practitioners argue that networks and channels in particular can benefit from Social TV because users could re-turn to linear TV driven by simultaneous communication in social media. Ynon Kreiz, the former CEO of Endemol Group, stressed the importance of this development when he said: “We think that social media meets television is the next big thing […] Whoever figures it out will be the next Steve Jobs of this generation” (Bergman, 2011a).

Despite this practical relevance, only a handful of studies addresses interpersonal and group communication while watching TV (e.g., Barker, 1997; Hepp, 1998; Klemm, 2000; Lee and Lee, 1995; Westerik, 2009). Research on simultaneous communication emerged prior to the development of the Internet: Often based on qualitative research, this literature emphasiz-es communication when groups watch together at a single location. Traditionally, the research

has centered on family members’ face-to-face-interactions in front of a single TV screen (e.g., Morley, 1986; Westerik, 2009). From this point of view, “TV has always been social”, as Christy Tanner, CEO of TVGuide.com, puts it (Bergman, 2011b).

Today however, new technologies and social media have extended interpersonal com-munication beyond the boundaries of family, friends, and geographical regions. On social networking sites such as Facebook and microblogging services like Twitter, users chat in real time parallel to TV reception. This additional function of television creates a particularly in-teresting challenge for communication research. TV is no longer just the linear “lean-back medium” (Dewdney and Ride, 2006, p. 289), but rather actively involves its users and stimu-lates interpersonal communication in social media – including among strangers. Social TV has transformed television into an ‘active medium’. For example, Twitter’s relevance for TV audience research has only recently been highlighted by academic research (e.g., Deller, 2011; Wohn and Na, 2011). Twitter offers the possibility to not only focus on particular shows, but on all types of television programs (Deller, 2011). This is in direct contrast to the investiga-[131]tion of specific online fan communities in earlier research (e.g., Franz, 2008 on Ally McBeal; Godlewski and Perse, 2010 on reality television shows) and enables comparative analysis of different TV programs.

This paper focuses on the phenomenon of Social TV and empirically explores users’ communication activities in social media while watching TV. We conducted a study where we examined communication on Twitter in Germany during the broadcast of different Ger-man television programs. In the following, we introduce Social TV and explain its relevance in Germany. We review the literature with regard to different communication activities and describe the methods used to explore our research questions. 2 Social TV as a phenomenon Social TV relates to the communicative exchange about linear television content, or that which is at least stimulated by it. Based on the several available definitions (e.g., Cesar and Chorianopoulos, 2008; Chorianopoulos and Lekakos, 2008; Research and Markets, 2010), it is not always clear whether pre-communication and follow-up communication are included (see Fig. 1).

Fig. 1: Differentiation of communication activities by time of occurrence.

Source: own research

In our research we focused only on the parallel communication about certain TV programs during their broadcast. To be relevant for our investigation, communication had to be trans-ferred by technology: here, via the social media platform Twitter (Deller, 2011; Wohn and Na, 2011). 2.1 Twitter as a communication platform

The microblogging service Twitter allows users to post updates up to 140 characters (so-called tweets), answering the question “What’s happening?” About 340 million tweets are sent (tweeted) daily. People also use Twitter to retweet [132](RT) messages, thereby recom-mending content they like or wish to promote. Tweets are commonly marked using a hashtag (#) indicating the issue they reference. Every word that is complemented by # is a hashtag that identifies the tweet’s topic. With more than 500 million active users worldwide and 4.1 mil-lion users in Germany (Schmidt, 2012), Twitter is one of the most popular social networking sites. Although in Germany the microblogging service is nowhere near as popular as in the US or in the UK, its popularity is growing fast: The number of unique visitors from Germany rose by 37% between March 2011 and March 2012 (ComScore, 2012). Many of the issues discussed on Twitter are related to TV programs (Deller, 2011; Wohn and Na, 2011). For ex-ample, German TV networks have more than 1.2 million followers on Twitter (BITKOM, 2012). Nevertheless, compared to the average viewing behavior of the wider public, Social TV remains a niche phenomenon in Germany. A recent online survey by TNS Infratest (2013) reports that only 28% of 14- to 64-year-old Germans are frequently online while watching TV; moreover, their activities are not always connected to TV. Only one-third of these users are involved in activities directly related to a particular program (TNS Infratest, 2013).

3 Conceptual framework: Categories of communication activities Research on face-to-face-communication prior to the advent of Internet technologies has shown that different types of communication activities can occur during TV reception (e.g., Hepp, 1998; Holly, Püschel, and Bergmann, 2001; Klemm, 2000; Weisz et al., 2007; Wohn and Na, 2011). Thus, communications activities are categorized in terms of (1) Evaluation of shows and actors, (2) Emotions, (3) Abstraction, and (4) Connecting with the community. 3.1 Evaluation of shows and actors Positive and negative evaluations of specific programs and actors have been identified as one of the most common communication activities (Hepp, 1998; Klemm, 2000). Wohn and Na (2011) found “opinion (a subjective message about the program)” to account for 38% of us-ers’ tweets concerning a speech by Barack Obama and for 32% concerning an episode of So You Think You Can Dance. In a content analysis of communication activities with regard to a cartoon, Weisz et al. (2007) classified 14% of activities as evaluations of the shown [133]video content. However, the main weakness in their study is that program-related activi-ties account for 42% and also include ratings and reviews of plots, characters, and actors (Weisz et al., 2007, p. 884). 3.2 Emotions Studies show that viewers react emotionally to TV content (Godlewski and Perse, 2010; Wohn and Na, 2011). Therefore, emotions also play a major role in Social TV. For example, in Wohn and Na’s (2011) study, emotions account for about one quarter of all tweets in each investigated case. Godlewski and Perse (2010) show that negative emotion while watching a favorite program is a significant predictor of participation in online activity. Emotions also play a relevant role for Weisz et al. (2007): The authors point out that the emotion “joy” alone (operationalized as “laughter”) accounts for about 9% of all messages. 3.3 Abstraction

Viewers associate TV content with their own lives. The television experience is integrated into the immediate environment and reflected either on a personal or societal level (Matthew-son, 1992; Holly, Püschel, and Bergmann, 2001). On the personal level, abstraction focuses on the meaning of a plot, a discussion, or an actor for the viewer’s own life. On the societal level, it highlights the role of the content for society as a whole. 3.4 Connecting with the community In the study by Weisz et al. (2007), about 23% of activities were not directly related to the content, but rather were part of private conversations. Two percent were “blurtings” (Goffman, 1978). Blurtings are minimal expressions, such as “uh” or “wow”, releasing emo-tions in a simplistic way (Klemm, 2000, p. 153). Examples from Twitter are “Blablablablablablabla. #illner” or “laaaaaaame #tatort”. 3.5 Genres of TV programs Several studies have investigated the effect of different genres on Social TV activities (e.g., Ducheneaut, Moore, Oehlberg, Thornton, and Nickell, 2008; [134]Ericsson ConsumerLab, 2011; Geerts, Cesar, and Bulterman, 2008; Geerts, 2009): “Having exposed participants to various content genres, we found that […] the type of TV content has a significant influence on communication” (Schatz et al., 2008, p. 1). Despite that shared conception, researchers disagree on the genres and formats deemed suitable for Social TV. On the one hand, for ex-ample, sporting events and reality shows are considered appropriate formats by many authors (Ducheneaut et al., 2008; Ericsson ConsumerLab, 2011; Geerts, 2009). On the other hand, there is little consensus: Ducheneaut et al. (2008) name cartoons, documentary films, and ac-tion and adventure films, while the authors of the Ericsson ConsumerLab (2011) list news magazines, debates/talk shows, and TV events. In contrast to Ducheneaut et al. (2008), Erics-son ConsumerLab (2011) argues that movies and series are rather irrelevant for Social TV. In this regard there is still considerable ambiguity. 3.6 Overview of the research questions Having presented Social TV as a phenomenon and Twitter as a community for Social TV as well as having theoretically differentiated the communication activities of TV viewers during reception, we will address the following research questions:

[RQ1] What communication activities do Twitter users engage in on the microblogging service while watching TV in parallel?

From the conceptual framework we derive the following research sub-questions for RQ1:

[SRQ1] How relevant are evaluations of shows and actors? [SRQ2] How relevant are emotions? [SRQ3] How relevant are abstractions? [SRQ4] How relevant are connections to the community?

A review of the literature has also shown that further investigation of TV genres and their respective influence on communication activities is still required:

[RQ2] How do different TV programs influence the communication activities of Twitter users?

[135]Our design therefore focuses on a wider sample of different TV shows. These shows differ with respect to such basic conditions as themes, story lines, or actors. Our research does not focus on analyzing such shows in detail, but on gathering varieties of possible communi-cation activities. Using the same standardized instrument, we aimed at comparing a diverse variety of shows to uncover similarities or differences of these communications. 4 Method A quantitative content analysis seems an appropriate method for investigating the proposed research questions (Krippendorff, 2004; Neuendorf, 2001). The material collected consisted of communication activities on Twitter about certain TV programs during broadcast. The unit of analysis employed was every single tweet relating to a certain program and within the broadcasting time. Fortunately, tweets are public and contain a timestamp. Tweets were iden-tified by their hashtag. Hashtags seemed to represent an ideal applicability criterion for this investigation, because they enable the easy identification of relevant tweets (Deller, 2011). In the case of the selected TV shows (see Table 1), the official hashtags were determined by the broadcasters and announced on TV, on the websites, or on broadcasters’/actors’ Twitter ac-counts. Overall, the sample consisted of 31,388 tweets relating to seven different TV pro-grams. A codebook was developed and a group of 45 students was trained by the researchers to code the tweets. After a pretest was conducted, Holsti’s coefficient was calculated before the field phase (Holsti, 1969; Neuendorf, 2001). The codebook’s pretest produced an overall coefficient of .916, with formal categories .958 and contentual categories .908, and can thus be interpreted as very good reliability between coders. 4.1 Selection of TV programs We chose to apply different criteria in the selection of TV programs:

(1) The programs needed to be of an appropriate genre for Social TV, such as reality shows, talk shows, TV events, and sports (e.g., Ducheneaut et al., 2008; Ericsson Con-sumerLab, 2011; Geerts, Cesar, and Bulterman, 2008; Geerts, 2009). Every genre had to be represented in the sample.

(2) The programs had to be broadcast in German Free TV within the inquiry period which ran from January to April 2012.

(3) The programs had to have public relevance so that active communication on Twitter could be expected. As an indicator of public relevance, the ratings by audience re-search GfK were used.

(4) The programs had to more or less reflect the German TV market (dual system: private broadcast/public service broadcast).

(5) The programs were partnered up to allow for pair-wise comparisons between shows of the same genre (except for Tatort and Schlag den Raab).

According to these criteria the following programs, presented in Table 1, were selected. Sports were excluded from the sample because it is unclear whether communication can be identified as a Social TV activity or as communication evoked by the live experience or other media.

[136]Table 1: Selection of TV programs.

Genre Program Channel Hashtag

Talent show

Deutschland sucht den Superstar (Pop Idol) RTL* #dsds

The Voice of Germany Pro7* / Sat.1* #voice

Ich bin ein Star, holt mich hier raus! (I’m a Celebrity, Get Me Out of Here)

RTL* #ibes

TV event Schlag den Raab (Beat the Star) Pro7* #sdr

Tatort (Crime series) ARD** #tatort

Talk show / Debate Maybritt Illner (Political talk) ZDF** #illner

Günter Jauch (Political talk) ARD** #jauch Source: own research; *private broadcast; **public service broadcast

Deutschland sucht den Superstar is the German equivalent of the British program Pop Idol or the American program American Idol. The Voice of Germany is based on the international reality-singing competition The Voice. Ich bin ein Star, holt mich hier raus is based on the British program I’m a Celebrity, Get Me Out of Here. While it is not a typical talent show, viewers are asked to vote for candidates. As a TV event, Schlag den Raab (Beat the Star) was chosen. It is a typical live event where a candidate challenges the host Stefan Raab in quizzes and sport games. Tatort is a crime series that has been televised in Germany regularly on Sunday evenings since 1970. Because of its character as a regular TV event and its role in the history of German television along with its consistently high ratings, it was selected for this investigation. However, for both of these latter shows, no match for comparison could be found. For the category talk shows, we selected the popular political debates of Maybritt Ill-ner and Günter Jauch.

[137]Analysis of the communication showed that none of these shows – expect for The Voice – had a Social TV strategy at the time of investigation. German broadcasters did not stimulate online discussion via, for example, referring to twitter hashtags within the program or creating their own apps. Compared to the US or other European countries, Social TV strat-egies are still underdeveloped in Germany (Buschow, Schneider, Carstensen, Heuer, and Schoft, 2013). 4.2 Variables and operationalization For the investigation of what communication activities occurred on Twitter during the broad-cast of TV programs, we used categories identified in earlier studies. Table 2 presents a sum-mary of the investigated communication activities as well as their operationalization, a textual example, and the reliability of the constructs.

The category Evaluation was operationalized by four different variables (posi-tive/negative, show/actor) representing the findings from the literature. To identify Emotions within a tweet they had to be explicitly expressed. For example, “joy” was present when a tweet contained a phrase like “I’m happy” or “I’m glad” (“I’m happily looking forward to

Florence! @TheVoiceGermany #Voice it’s gonna be awesome!”). Abstraction was – as pro-posed in the literature – differentiated into two forms: “personal abstraction” and “societal abstraction”. Personal abstraction occurred when the users connected topics from the program to their personal life or personal experiences, for example: “I could easily live without chan-cellor Merkel. #illner”. Societal abstraction takes place when a topic is deemed to generally refer to society as a whole, for example: “A problem with politics is that you don’t make enough money. Without bribery! #illner”. Connecting with the community summarized four types of interaction between the members of the Twitter community. (1) “Reference” means explicit addressing a specific user. Such an explicit address can be identified by @ and the name of another user (“@_holger I didn’t say she is bad”). If this combination appeared with-in a tweet, a reference was identified. (2) A “retweet” is a Twitter-specific way to quote an-other user. Retweets were identified by the prefix “RT”. (3) “Contact” refers to addressing Twitter users in general, and not specific users. This is the case when the tweet uses “you” as a general pronoun, or when there is a question without a @-specification, for example: “#sdr the last game! Who’s going to win?!” (4) A “call to action” occurs in game and talent shows where viewers are an important part of the show because they are asked to vote for candi-dates. Such calls can be identified by imperatives like “vote”/“call [for XYZ]” (“Vote for Ivy!! #voice”). We also addressed some stylistic means

[138]Table 2: Operationalization and data management.

Category Variable name Textual example Reliability*

Evaluation

Negative evalua-tion of the show

“disability as a punishment of God ... Medie-val TV in 2012. #facepalm #Jauch”

.77

Positive evaluation of the show “the Frankfurt #Tatort I like most”

Negative evalua-tion of an actor “Holger in #Tatort is a poor fellow”

Positive evaluation of an actor “Stefan Raab is just awesome! #sdr”

Emotions

Joy “I’m happily looking forward to Florence! @TheVoiceGermany #Voice it’s gonna be awesome!”

.96

Anger “At #SDR I always get angry! Raab can nev-er keep to the rules and must discuss about everything”

Fear “#Tatort today scares me. So dark...”

Sadness “#ibes so sad that micaela is out D’:”

Disgust “The hooker was so disgusting! #Tatort”

Pity “I just hope that Luca will not cry if Daniele wins, I cannot see him cry again :'( I cannot stand it ... #dsds”

Abstraction

Personal abstrac-tion

“I could easily live without chancellor Mer-kel. #illner”.

.90 Societal abstrac-tion

“A problem with politics is that you don’t make enough money. Without bribery! #ill-ner”

Connecting with the community

Reference “@[…] I don’t say she is bad #ibes”

.9 Retweet** “RT @[…] The #FDP will change nothing

#Illner”

Contact “#sdr the last game! Who’s going to win?!”

Call to action “Vote for Ivy!! #voice”

Stylistic means

Swear words “Booooah F*CK #dsds”

.93

Spam** “Go, visit my website [...] #dsds” Typical abbrevia-tions on the inter-net**

“#voice :-) #xavier naidoo <3<3<3”

Restricted speech code** “Ain’t nobody goin to f*ck wit raab #sdr”

Source: own research; *Holsti’s coefficient (Holsti, 1969) **not included in cluster analysis

[139]such as swear words, spam, specific abbreviations and emoticons, and others irrelevant to this investigation. All variables used in the study were binary coded with “1=present” and “0=not present”. 4.3 Data management: Cluster analysis 31,388 cases were gathered during the inquiry period in accordance with the applicability criteria. To reduce the complexity within such a volume of cases we used a cluster analysis to identify groupings of tweets in data. Cluster analysis allows cases to be typified according to certain variables, resulting in internally homogeneous groups that are as dissimilar as possible from each other (Everitt, Landau, Leese, and Stahl, 2011; Mooi and Sarstedt, 2011). Most of the variables described above (see Table 2), except for some of the stylistic aspects, were used in the cluster analysis. Pearson’s correlation coefficient was calculated for each variable pair to check for a correlation. The highest value of Pearson’s correlation coefficient between a pair was approximately r=.2. Thus, every variable was considered in the cluster analysis.

A hierarchical cluster analysis with the nearest-neighbor method was realized to identify any unexpected spikes (Everitt et al., 2011). The distances were measured by the squared Eu-clidean distance. As no spikes were identified, every case was considered in the cluster analy-sis. Subsequently, a hierarchical cluster analysis using Ward’s method was conducted to iden-tify a reasonable number of clusters (Everitt et al., 2011). The distances were measured by the squared Euclidean distance. The number of clusters was determined by the variance criterion. An erratic gain of the sum of squares at a certain step of clustering leads to a reasonable num-ber of clusters. A scree plot helped to visualize this gain, indicating that a solution with either three or seven clusters was reasonable. For each of the solutions a k-means cluster analysis was generated, allowing a comparison of the interpretability. Finally, a solution with seven clusters was chosen. 5. Results

[RQ1] Communication activities and their specific relevance (SRQ 1-4) The first research question was answered by descriptive statistics as well as cluster analysis. The dataset included the cases of all TV programs investigated.

[140]Table 3: Relevance of specific communication activities by category.

SRQ Category %

1 Evaluation of shows and actors Positive about show Positive about actor Negative about show Negative about actor

24* 3 9 6 7

2 Emotions Joy Anger Fear

4* 1 1 .2

Sadness Disgust Pity

.3

.3

.4 3 Abstraction

Personal Societal

5* 4 1

4 Connecting with the community Reference Contact Call to action

49* 31 15 3

Source: own research; N=31388; *differences due to rounding errors; multiple categories per tweet possible

Table 3 shows the distribution of communication activities differentiated by category. The data shows that 24% of all tweets are from the category “Evaluation of shows and

actors” (SRQ1). In the case of actors we see a ratio of 56% positive to 44% negative ratings. Programs were more often criticized than actors (67% negative ratings). The “Emotion” cate-gory (SRQ2) was a very rare phenomenon. Only 4% of all tweets contained explicit expres-sions of emotions. “Abstraction” (SRQ3) was also relatively infrequent – only 5% of all tweets contained personal or societal abstraction. 31% of the tweets contained a reference to another user, while 15% sought to establish contact with the entire community and 3% called for the community to act in a certain way. In sum, 49% of all tweets were categorized as “Connecting with the community” (SRQ4).

Furthermore, the data challenges some popular clichés about Twitter often articulated in the public discourse. Firstly, the debates on Twitter are not regularly ‘vulgar’. Only 5% of all analyzed tweets contained swear words. We collected a very marginal amount of spam: Only 0.4% of the tweets could be identified as such. The level of language is higher than expected. Only 11% of

[141]Table 4: Description and size of the clusters.

Cluster no.* Cluster N %

1 Debate about the program 14142 45.1

2 Fandom 1686 5.4

3 Critique 1548 4.9

4 Insult 1143 3.6

5 Retweet 8294 26.4

6 Contacting the community 3088 9.8

7 Discourse 1487 4.7

31388 99.9* Source: own research; N=31388; *differences due to rounding errors

tweets could be identified as employing ‘restricted speech code’. The identification depended on the correct use of grammar and the diction of the tweet.

Overall, the tweets seem to be motivated by two desires: to interact with the community and/or to engage with the program. The interpretation of the cluster analysis confirms this assumption and allows a more detailed look at the patterns within our sample. Table 4 lists the clusters and the number of tweets assigned to each one.

The first cluster accounts for 45% of all tweets. It is characterized by positive as well as negative evaluations of actors and shows, and is hence referred to as “Debate about the pro-gram”. Cluster 2 is characterized by positive evaluations of actors and is designated as “Fan-dom”. Clusters 3 and 4 constitute the opposite: Both are dominated by negative evaluations of actors. While “Insults” (Cluster 4) is characterized by swear words, “Critiques” (Cluster 3) is more well-mannered. All of these clusters (representing 59% of all tweets) primarily focus on the TV content itself.

Clusters 5 to 7 mainly relate to interactions between users. The fifth cluster is the se-cond largest, accounting for 26.4% of all tweets. It is dominated by retweets and also includes communication that addresses specific users; we have designated it “Retweets”. Cluster 6 comprises communication addressing the whole community, for example, calls to action; we refer to it as “Contacting the community”. Cluster 7 is characterized by direct communication between certain users (@-tag) and by communication that addresses the entire community, and is referred to as “Discourse”. [142] [RQ2] Influence of TV programs on communication activities Investigating the second research question entails highlighting the significance of TV pro-gram-related tweets that are assigned to a specific cluster. Table 5 shows the TV programs by row and the clusters by column. The standardized residuals presented here are an expression of the comparison of the empirical count with the expected count and provide information about the significance of differences (Bakeman and Robinson, 1994). The bold numbers show whether the number of tweets relating to a certain TV program within a cluster is significantly high (+) or low (-).

The results show that different types of TV programs evoke different kinds of tweets. Moreover, there are also differences between TV shows within a single genre. Every TV show has its own specific structure of tweets. The reality-singing competition The Voice of Germany mainly produces tweets that are related to the show’s candidates. “Fandom” – meaning positive ratings of actors – is an important issue within the communication about The Voice. However, there are also a larger number of tweets which can be considered as critique toward the show’s actors. The regular appearance of actors in the show leads to a strong base of fans that cheer for their candidate and criticize competing actors. However, the program itself is a less popular topic.

Significantly, Deutschland sucht den Superstar (Pop Idol) often evoked insulting tweets and “Retweets” although it is of the same genre as The Voice. Overall, swear words appeared rarely (5% of all tweets; for an example tweet, see Table 2), but a significant portion of these tweets are related to this show and its actors. In comparison to The Voice, the tweets are much more polemic or even insulting. It would seem that Deutschland sucht den Superstar has been unable to generate a stable fan base that cheers for certain actors.

Tweets related to Ich bin ein Star, holt mich hier raus (I’m a Celebrity, Get Me Out of Here) mostly deal with the program itself and comprise critiques as well as expressions of “Fandom”. The significantly high amount of tweets in “Contacting the community” reflects the viewers’ special relationship to the actors competing on the program: The community is mobilized when it is time to vote and is requested to vote for a certain candidate.

Schlag den Raab (Beat the Star), the live game show, evokes tweets that include debates about the show, comprise critiques, and create a discourse within the community. The number of tweets in “Fandom” is significantly low, which may be explained by the fact that the can-

didates only compete in a single episode. This inhibits the proper development of a relation-ship between viewers and candidates; rather, the actual competitions and games on the show are in focus.

[143]Table 5: Standardized residuals of cluster assignments of TV program-related tweets.

TV program

Cluster 1:

Debating the program (n=14142)

2: Fandom (n=1686)

3: Critique (n=1548)

4: Insult

(n=1143)

5: Retweet (n=8294)

6: Contacting community (n=3088)

7: Discourse (n=1487)

The Voice of Germany -7.1 16.9 5 -2 2.8 -4.4 0.2

Deutschland sucht den Superstar (Pop Idol)

-4.8 1.8 -7.1 4.2 8.1 -2.7 1,3

Ich bin ein Star, holt mich hier raus! (I’m a Ce-lebrity, Get Me Out of Here)

2.8 9.9 9.6 -0.2 -14.8 8.2 -5.6

Schlag den Raab (Beat the Star) 6.2 -13.2 2.5 -0.2 -4.7 0.6 2.7

Günter Jauch (Political talk) -0.3 -1.8 -0.5 -1.3 6.4 -4.6 -3.8

Maybritt Illner (Political talk) -4.7 -3.4 -0.1 -3.5 8.6 -1 2.5

Tatort (Crime series) 8 -5.6 -7.7 -0.7 -5.9 5.4 -4.1

Source: own research; standardized residuals: values ≥ |2| indicate a significant difference on a 5% level (p<.05), values ≥ |2.6| on a 1% level (p<.01) (Bakeman & Robinson, 1994, p. 22-23). Differences are marked in bold.

[144]Tweets about the political talk shows Maybritt Illner and Günter Jauch are often “Retweets”. Those related to Maybritt Illner also partly comprise a discourse. Users general-ize the topics of the political talk show and reflect them on a societal level. In this case it is interesting to see that even programs with the same concept evoke different kinds of tweets

The crime series Tatort evokes a debate about the plot which can be characterized main-ly as a quality discourse: Users typically discuss the story, cast, and production value of the current episode. 6 Conclusion and outlook The main goal of this study was to explore the communication activities occurring on Twitter while people watch TV simultaneously. The results show how Twitter, as a relatively new communication technology, as well as different TV programs encourage specific (and new) types of communication. The two key findings of our study are:

1. Social TV on Twitter comprises a communicative exchange about linear television content. Nevertheless, Social TV also relates to communication within the community which is not necessarily textually connected to the content.

2. We explored those TV programs for which former research expected the biggest rele-vance for Social TV. We can now state which types of communication these programs evoke – at least in Germany.

The following discussion focuses primarily on these aspects. 6.1 Communication activities relating to TV content – and beyond The data explore the communicative use of Twitter for Social TV in Germany. The finding that evaluations play a major role in the data is in line with assumptions from the literature (Hepp, 1998; Klemm, 2000; Weisz et al., 2007; Wohn and Na, 2011). Based on earlier stud-ies, we expected evaluations to account for around one-third of all activities. The investigation confirmed this assumption. However, emotions are less important than predicted in the litera-ture (Godlewski and Perse, 2010; Weisz et al., 2007). The limited relevance of emotions need not imply that German Twitter users are less emotional. Weisz et al.’s (2007) study that clas-sified 9% of communication activities as “joy” [145]used a cartoon as stimulus. Our study did not include such humorous material. The “Abstraction” category was rare and very much de-pendent on specific genres (see below).

The most striking finding seems to be the large number of community-oriented tweets (clusters 5 to 7). In one-third of all communication activities we see some interaction with specific individuals. Today, these people also include strangers and not only family members and friends, as was the case prior to the advent of the social media technologies. As suggested by Klemm’s (2000) observations of families, joint TV reception stimulates interpersonal communication beyond the program’s broadcast. A large number of activities also constitute retweets of other users’ posts along with recommendations to spread them. However, this practice also highlights the self-referentiality of tweeting. 6.2 Influence of specific TV content One of the few consistencies between researchers in this field is the assumption that reality shows are suited for Social TV (Duchenaut et al., 2008; Ericsson ConsumerLab, 2011; Geerts et al., 2008). Our investigation confirms this hypothesis: The Voice of Germany, Deutschland sucht den Superstar (Pop Idol), and Ich bin ein Star, holt mich hier raus (I’m a Celebrity, Get Me Out of Here) stimulate communication that is particularly characterized by an evaluation of the participants on the show – positive as well as negative. Candidates polarize the com-munity and arouse expressions of fandom as well as critiques. Upon closer inspection, a spe-cial kind of interaction plays an important role: In the last minutes of the show the number of tweets calling for users to take action increases significantly. This is caused by users who call on other users to vote for their favorite candidate.

From the literature we have seen that there is also some consensus that debates and talk shows are appropriate for Social TV. The political talk shows Maybritt Illner and Günter Jauch received less attention than reality shows. However, the tweets referring to them fo-cused more on interaction than on evaluation of the show and its participants. The users refer to each other and start a discourse. Looking at the objects of this discourse it is obvious that users abstract topics from the show and elevate them to a higher societal level. With respect to political talk shows, we see a potential for activating debates and a “critical discourse” (Ha-bermas, 1989) accompanying the broadcast. However, closer inspection reveals that there are indeed differences between both political talk shows. Even though the show’s concepts are

very similar, the influence of the discussed topic as well as the show’s host should not be un-derestimated.

[146]Tatort (crime series) stimulated a debate within the community in relation to the quality of the episode or story or the production value. As a crime series (Duchenaut et al., 2008) and public broadcast TV program with a momentous history, viewers seem to have certain expectations and feelings relating to the series. Therefore, for Social TV in Germany Tatort has specific potential, although discussions are not always positive. Similar to Tatort, tweets on Schlag den Raab (Beat the Star) debated the show and represent a discourse within the community. In contrast to Tatort, the discourse here is not primarily about quality. Com-munication activities focus on the games. From the data it is clear that the rate of tweets per minute rises significantly during the games compared to the rest of the broadcast. 6.3 Limitations of the study If Social TV is part of the future of traditional linear television, as suggested by practitioners and researchers, further studies are needed to understand the phenomenon more precisely.

Our results have been exclusively obtained from consciously selected TV programs. These programs are not necessarily representative of all German TV programs or of interna-tional formats. Data has only been collected from Twitter. However, we also see a lively ex-change about TV on other social networking sites such as Facebook. In our study we excluded communication activities occurring before and after broadcast. There is however some re-search – for example by Godlewski and Perse (2010) – that would help to examine pre- and follow-up communication in further investigations.

On the one hand, more research focusing on international comparisons and/or the com-parison of different technologies/infrastructure is needed. Comparing face-to-face communi-cation in one location and communication transferred by technology could also be an interest-ing perspective. Such research would additionally stimulate some viral cooperation of inter-group communication research and communication technology research. On the other hand, (qualitative) case study research should investigate certain shows in more detail. Against this background, data from this study could be juxtaposed with viewers’ social demographic data. This would allow for a more detailed interpretation of the communication activities of the shows’ respective audiences.

Furthermore, we would advocate survey research: Questionnaires of ‘heavy users’ or non-users could explore modes of usage as well as motivations for ‘tweeting television’. [147] References

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