Please cite this as: Murthy, D. (2013), “’Hate-watching’ and Twitter”, iSociology,

In my Twitter research, I have been exploring ‘social television watching’. There are two key points which come to mind regarding how the rise of social media has impacted our approach to television-viewing generally. First, scholarship on television has seen TV viewing as ‘parasocial’, an unbalanced relationship where television viewers feel ‘intimacy at a distance’ with celebrity actors (MR Levy 1979). In other words, TV viewers perceive an intimate relationship, where they have the illusion that they ‘know’ the celebrity on screen; however, the celebrity knows nothing of the television watcher. The promise, or at least perception, of social media such as Twitter is that this parasociality may be broken down or challenged one @-mention at a time by the likes of Ashton Kutcher. So the impact here is a perceived decrease of parasociality (a decrease in the traditional gap between television viewer and celebrity on the screen).

Additionally, social media, by encouraging television audience members to be quite active, tweeting under #thefollowing or #dexter can form engaged public audiences, which include both fans who know each other and fans who do not. This social TV watching is powered by social media use via mobile smartphones and laptops. Interestingly, an argument can be made that this increases our sociability while engaging in an activity which many have viewed as becoming less and less social (in juxtaposition to historical TV watching as a family or other group).

Hate-watching has been a part of television since its inception. It’s not that Twitter and other social media encourage hate watching per se, but that social media make it easier to publicly hate-watch. In other words, shows like Single Ladies or Toddlers & Tiaras would have had hate-watchers with or without social media. It is not only the publicness of hate watching that has changed, but also that it is real time, global, and not limited to one’s living room or to a single telephone call. What social media does is enable a ‘networked public’ (see ‘Networked’ by Barry Wellman) to hate-watch together. Additionally, after Emily Nussbaum’s 2012 New Yorker piece, ‘Hate-Watching “Smash”‘, Twitter has seen many incidents of people tweeting under #hatewatching and similar hash tags shows that are the top of their hate-watch list. Twitter can ultimately egg people on to hate watch (especially during the network broadcast of a show or in the immediate days after the show is aired).

As mentioned above, social media creates new social communities and formations. Often, these are focused around particular events (something I discuss in my book). In the case of television, these events are particular TV shows. Events encourage social responses. My view is that hate-watching is not more popular today per se. Rather, it is the way in which we hate-watch that has changed. Melrose Place in the 90s comes to mind here! Though by no means the same as hate-watching, the best term to describe similar historical processes of this type of television watching is ‘guilty pleasure’. An Associated Press article in 2002 refers to Jerry Springer shows in this vein.

What is different, and why we may perceive hate-watching to be more popular today, is that Twitter makes it very easy to publicly broadcast your hate-watching. Not only that, but the social engagement of rapid @-mentions between hate-watchers leads us to think it is on the rise. Not only is it very public and in your face now, but it is highly networked, real-time, and global.

[This post should be cited when referenced and not reproduced without prior permission]

The Great British Class Survey was undertaken by Mike Savage of the LSE and other academics at the London School of Economics, the University of Manchester, City University and the Universities of York, Bergen in Norway, and the Université Paris Descartes, France.

The survey raises important questions regarding the construction and maintenance of class in the UK.

Like others, I took the online ‘What Class Are You?’ calculator at the BBC website. It measures income variables, one’s social network, and one’s social/cultural interests.

I collected 1426 tweets from April 4 2013 – April 11, 2013 which include the URL (a main URL used to link to the GBCS calculator. Some initial results of examining these data are below. Please comment on ideas for better understanding these data.

Top Hashtags in Tweet in Entire Graph Entire Graph Count
whatsyourclass 1061
britain 12
class 11
fb 5
bbc 5
elite 3
rofl 3
poorgeois 3
sociology 2
joke 2
Top Words in Tweet in Entire Graph Entire Graph Count
class 2052
new 1140
whatsyourclass 1062
system 1029
britain 1007
s 1006
group 999
m 983
out 935
found 919
Top Word Pairs in Tweet in Entire Graph Entire Graph Count
class,system 1027
new,class 1015
s,new 999
britain,s 998
group,britain 959
system,whatsyourclass 942
found,out 918
out,m 914
middle,class 426
class,group 408

I collected 1204 tweets from April 4 2013 – April 11, 2013 which include the hashtag #whatclassareyou (a key hashtag used to discuss the GBCS). Some initial results of examining these data are below.

Top URLs in Tweet in Entire Graph Entire Graph Count 1117 7 4 2…not 1 1 1 1 1 1
Top Hashtags in Tweet in Entire Graph Entire Graph Count
whatsyourclass 1204
britain 12
class 6
bbc 5
fb 4
elite 3
rofl 3
joke 2
sticazzi 2
in 2
Top Words in Tweet in Entire Graph Entire Graph Count
class 1502
whatsyourclass 1203
new 1129
system 1023
britain 1021
s 979
group 968
m 953
out 928
found 899
Top Word Pairs in Tweet in Entire Graph Entire Graph Count
class,system 1021
new,class 1005
s,new 975
britain,s 972
system,whatsyourclass 966
group,britain 939
found,out 898
out,m 892
class,group 399
middle,class 397


In my research of large Twitter data sets, I have been regularly studying trending topics. Most of the time, trending topics encourage monologic behavior on Twitter. For example, see the relatively monologic network graph below of #ThreeWordsSheWantsToHear (a hashtag which warrants serious  critical, dialogic engagement of gender issues).

#ThreeWordsSheWantsToHear Network Graph

I have been investigating the #accidentalracist hashtag from this past Monday (April 8, 2013). The hashtag formed in response to the new Brad Paisley and LL Cool J song titled ‘Accidental Racist’ (video below).

The Huffington Post labeled the song ‘a controversial one, to say the least’ and features the singer donning a Confederate flag. Forbes observed that the song set ‘off a firestorm on social media’.

Given the controversial nature of this hashtag, I was curious whether the tag was encouraging monologic or more dialogic behavior. It turns out that the 1504 tweets I sampled (from 9:56 p.m. to 11:18 p.m. UTC on April 8, 2013) actually exhibited far more interaction than one would expect in a trending topic-based network. Take a look at the social network analysis map below and let me know your thoughts!

#accidentalracist social network map

#accidentalracist social network map


And, as @sociographie, mentioned in a Twitter chat we had, it is important not to solely prioritize relationships in a network graph of trending topics. That is why I have retained isolates. Below, you can also find some aggregate information on the tweets.

Top URLs in Tweet in Entire Graph Entire Graph Count 28 21 17 10 5 4 3 3 2 2
Top Domains in Tweet in Entire Graph Entire Graph Count 28 27 23 17 13 10 4 4 4 3
Top Hashtags in Tweet in Entire Graph Entire Graph Count
accidentalracist 1493
llcoolj 26
bradpaisley 23
intentionalbeatdown 17
thewalkingdead 7
negrobush 5
occupy 5
ows 5
oppression 5
llcooljoke 4



I have been avidly watching election-related tweets yesterday and today. In both my classes today, I included discussions of tweets which either mentioned Obama, Romney, #election2012, and #election2012 America. The latter was particularly useful in highlighting tweets from non-American tweeters. Supporting the idea that Twitter functions like a diary or chronicle of one’s daily life are tweets which inclue “I just voted”, “just voted”, or “on my way to vote”.

Some interesting tweets which emerged from both my searching and that by students are:

  • NBC confirms a voting machine malfunctioning, changing votes for Obama to Romney in Pennsylvania

[Unsurprisingly, tweets recirculating news stories have been extremely popular today. Engagement by the public with news or even with campaigns via social media is noted as significant by Himelboim (2012)]

  •  if Obama loses because y’all dumbass are posting your ballots on social networks, your ass shouldn’t have the privileges to vote!

[The interesting trend of voters using their smart phones to take pictures of their ballots and post them on social media supports arguments in the literature which discuss how ubiquitous computing makes it possible for us to increasingly publish things which are normally very much in our private sphere. Instagramming one’s ballot seems particularly popular!]

  •  ⬜ Romney ⬜ Obama ✔ Glitter, Victoria’s Secret, and tiaras.

[The ‘third option’ genre of tweets has been used frequently within the #election2012 hash tag]

  •  I’ve been praying all day for a #Romney victory

[Tweets in the hash tags provide empirical support that the religious right is actively engaged within Twitter. This supports findings in the literature of the conservative movement’s use of digital media technologies (Bennett 2012)]

[Interestingly, Paul McCartney’s tweet was the most retweeted #election2012 tweet at one point today (#beatles #ukinvasion) ;)]

  • Whether you vote Republican, Democrat or 3rd Party, you should still celebrate with @(name of brand) #America #election2012

[Unsurprisingly, lots of businesses, brands, and bands are using the #election2012 hash tag for promotion purposes. The use of hash tag manipulation is discussed by Page (2012)]


1.            Page, R., The linguistics of self-branding and micro-celebrity in Twitter: The role of hashtags. Discourse & Communication, 2012. 6(2): p. 181-201.

2.            Bennett, W.L., The Personalization of Politics: Political Identity, Social Media, and Changing Patterns of Participation. The ANNALS of the American Academy of Political and Social Science, 2012. 644(1): p. 20-39.

3.            Himelboim, I., et al., Social Media and Online Political Communication: The Role of Interpersonal Informational Trust and Openness. Journal of Broadcasting & Electronic Media, 2012. 56(1): p. 92-115.

While I was writing my book about Twitter (Twitter: Social Communication in the Twitter Age), I took an interest in tracking the US Republican primary as it was being constructed within Twitter. Last year, I started collecting all geo-located tweets  (tweets with location information turned on) for the 50 most populous urban American cities (according to U.S. Census statistics ). Because of the geographical richness of this data set, I thought it would be a perfect source to use to study twitter activity surrounding the US Republican primary. Working with  Alexander Gross and Stephanie Bond, I designed and developed a tool to visualize this specific geographically-anchored landscape.

The 2012 US presidential election provided another opportunity to leverage this data. Twitter has been extremely active in terms of election-related discourse. Our Election 2012 Twitter Visualization Tool uses emergent big data research methodologies to visualize the election. The visualization tool has been optimized for the Safari browser (and is known to have some issues in other browsers).

The goal of our research is to explore urban American responses to the 2012 presidential candidates on Twitter. In order to create a representative sample of tweets from urban centers in the United States, we collected tweets from Twitter by location. We took the 50 most populous American cities according to the U.S. Census and instructed Twitter to send us tweets that were within 7-12km of the locations of these cities.

Our software collects these geo-located tweets and uses the data to chart the relative buzz surrounding candidates in the 2012 presidential election. The tool charts the relative popularity of each primary candidate as measured by the number of tweets which we have collected over the last 24 hours and identified with a particular candidate. For a tweet to be counted as referring to a particular candidate, the tweet must contain the candidate’s first and last name separated by a space e.g. “Mitt Romney” or the candidate’s official campaign twitter account name or the account name eg @mittromney. A single mention as reported by the chart’s dynamic legend is equivalent to one tweet which contains one of the candidate names. Tweets which contain more than one candidate name will be counted as mentions for both candidates. These stringent rules prevent unecessary possible over counting of tweets for a candidate. Though the frequency of the tweet count in our visualization is low because of this, the data collected is very robust. Specifically, all tweets visualized do refer to Obama or Romney.

Please visit the tool’s webpage at my lab, the Social Network Innovation Lab, for more detailed information.


“I stand, a trespasser in his camp, hearing echoes—Chink, gook, Jap, Charlie, GO HOME, SLANT-EYES!—words that, I believe, must ‘have razored my sister Chi […] What vicious clicking sounds did they make in her Vietnamese ears, wholly new to English?” (Pham 1999: 6-7)

 I recently travelled to Utrecht, Holland and couldn’t help but recall the lines above from Pham’s Catfish and Mandala. I had just taken a train from Amsterdam and was standing in Utrecht Station gobsmacked.

A restaurant called ‘Charlie Chiu’s’ was busily filling up with Dutch customers. Pham’s words rushed into my head. ‘Charlie Chiu’s’ had as its logo: ‘Charlie’ with ‘SLANT-EYES’. Its heavily racialized logo was clearly drawing from pejorative essentialisms which encompass ‘gook’ and ‘Jap’ (and many other Asian slurs).

The reason I was in Utrecht was to attend the Digital Crossroads Conference, a conference exploring race and migration online. After a short walk from the station, I was soon listening to Lisa Nakamura’s keynote address, which discussed forms of online racism (including ‘trash talk’ on YouTube). As she spoke about race and racism on YouTube, the image of Charlie Chiu’s remained and reminded me of the continued pervasiveness of racism in Holland. Particularly, Geert Wilders, the right-wing Dutch politician, has encouraged open Islamophobia in the Netherlands (Wieringa 2011). Though my work has investigated ubiquitous online racism, I often forget to write about persistent racism offline. ‘Charlie Chiu’s’ is emblematic of larger currents of race in the Netherlands (Essed and Trienekens 2008). I would be very interested in hearing from readers about their thoughts.


Essed, P. and Trienekens, S. 2008 ”Who wants to feel white?’ Race, Dutch culture and contested identities’, Ethnic and Racial Studies 31(1): 52-72.

Pham, A. X. 1999 Catfish and mandala : a two-wheeled voyage through the landscape and memory of Vietnam, 1st Edition, New York: Farrar, Straus and Giroux.

Wieringa, S. E. 2011 ‘Portrait of a Women’s Marriage: Navigating between Lesbophobia and Islamophobia’, Signs 36(4): 785-793.

Please cite this as: Murthy, D. (2012), “Some Musings on Facebook As It Marches Towards 1 Billion Users”, iSociology,

Keywords: Facebook, online social networks, pervasiveness, ubiquitous computing, Facebook audience configuration, Facebook app, Facebook games


As the user base of Facebook approaches 1 billion users (Emerson 2012), the medium continues to gain international attention. Despite the Pope’s 2011 warning of the risk of alienation due to online social networks (Pullella 2011), Facebook is not considered by users as a ‘substitute’ for phone calls or face-to-face interactions. Rather, it is considered to be a different communications medium which enables updates amongst friend networks. Additionally, the types of information/social interactions occurring on Facebook are often qualitatively different from phone calls and face-to-face interactions. Some of this has to do with the fact that large parts of one’s network on Facebook are composed of ‘weak ties’, connections who you are not particularly close to but have some acquaintance with (Granovetter 1983). This is combined with clusters or a core of ‘strong ties’,  connections who you have an ‘affective’ relationship with (involving some level of ‘emotional intensity and intimacy’ (Krackhardt, et al. 1992: 217)), generally trust, and have more than a passing acquaintance with. The latter has been tested in the literature through questions such as would that person be prepared to lend you $100 if you are in a difficult situation (Gilbert and Karahalios 2009). Facebook provides a means by which to maintain or even reinforce relationships with strong ties. Additionally, Facebook creates a clustered space where, if one has a large quantity of weak ties, they will receive a greater proportion of updates from this category of friends over strong ties. Some see this construction of friend networks as highly beneficial in that they can keep abreast of the happenings – however quotidian – of old classmates, distant friends, or previous colleagues. Prima facie, Facebook appears best at maintaining strong or ‘stronger’ ties rather than fostering weak or ‘weaker’ ties. However, many use Facebook primarily to connect with ‘weak ties’ (DiMicco, et al. 2008).

The ways in which people access Facebook is also an important point to consider. Specifically, research reveals that the Facebook app is one of the most commonly used apps on mobile devices (Maier, et al. 2010). Prima facie, this would not seem to affect Facebook as a social medium. However, the fact that users are increasingly updating their profiles while on the move has significant implications for the regularity of their activity, their content, and their perception of the medium. Specifically, mobile technologies foster increased real-time information sharing (Freifeld, et al. 2010). This is not restricted to Facebook, but the medium is highly affected by it. This move towards ubiquitous computing encourages users to, for example, update their Facebook friends about what they are eating, what movie they are about to watch, or how they are feeling at the moment. They can also use Facebook as a means to pass time while in a queue or to quickly take a picture and upload it to their profile.

In terms of audiences, updates on Facebook have produced new audience configurations. Specifically, individuals have a constellation of ‘friends’ who can see these updates, but, this audience is continually changing minute by minute depending on who is logged onto Facebook at the time. This is well illustrated by ‘status updates’. Though these short messages are often trivially banal (e.g. ‘saw a cool purple shirt I should have bought’), these messages are circulated as ‘news’, which Facebook automatically distributes to your group of ‘friends’, selected individuals who have access to your Facebook ‘profile’ (your personalized web page on the site).

The game culture on Facebook is very important to the medium’s social configuration. The popular Facebook game, FarmVille, where Facebook users maintain farms and can have friends as neighbors, has been an important part of the Facebook experience to a substantial number of users. Friends can be invited to one’s farm to help accomplish particular tasks and gifts can be given to friends using the game’s currency. As parodied on a popular episode of SouthPark[i], the meaningfulness of the game to Facebook users can be extremely high. Additionally, the game has involved corporate partnerships in which Facebook users can visit ‘company farms’ such as that of McDonald’s, which recently offered free McDonald’s-branded Farmville products. Other games such as CityVille and The Sims Social have also become very popular. Manovich (2001: 9) argues that as culture becomes more computerized, this ‘not only leads to the emergence of new cultural forms such as computer games and virtual worlds [but, also] it redefines existing ones such as photography and cinema’. It is critical for us to examine the impact of ‘computerization’ and indeed digitization on pre-computerized cultural forms and, I would add, practices, rather than merely looking at the new forms such as Facebook games which emerge.

Part of understanding Facebook as having redefined existing cultural practices involves considering it as a socially mediated ‘space’. Rosenthal’s (2008) idea of ‘socioformation’ is useful here. She argues that any part of ‘cyberspace can be socioformed, turned into space where humans can form societies’ (Rosenthal 2008: 160). Facebook does not constitute a ‘society’ per se, but rather it is a space where new social configurations and communities are formed and existing cultural practices are redefined. Martínez Alemán and Wartman (2009) studied the usage of Facebook within college campuses in the United States. Respondents to their first questionnaire were solicited through an invitation posted on Facebook that was visible to members of the large, private institution they were researching. Martínez Alemán & Wartman (2009: 52) found that having respondents in front of a computer navigating through their Facebook profiles allowed the researchers to ‘make meaning’ of what they saw on the screen and what the respondents did. This is important in that it can be tempting to dismiss Facebook as ‘meaningless’ or a ‘waste of time’. However, the observation of users interacting within Facebook reveals social meaning. Though 1 billion users may be wrong, one thing is for certain: a significant number of those see Facebook as socially meaningful to them.



DiMicco, J., Millen, D. R., Geyer, W., Dugan, C., Brownholtz, B. and Muller, M. 2008 ‘Motivations for social networking at work’ Proceedings of the 2008 ACM conference on Computer supported cooperative work, San Diego, CA, USA: ACM.

Emerson, R. 2012 ‘Facebook Users Expected To Pass 1 Billion In August: iCrossing ‘ Huffington Post, Vol. January 14, 2012: Huffington Post.

Freifeld, C. C., Chunara, R., Mekaru, S. R., Chan, E. H., Kass-Hout, T., Ayala Iacucci, A. and Brownstein, J. S. 2010 ‘Participatory Epidemiology: Use of Mobile Phones for Community-Based Health Reporting’, PLoS Med 7(12): e1000376.

Gilbert, E. and Karahalios, K. 2009 ‘Predicting tie strength with social media’ Proceedings of the 27th international conference on Human factors in computing systems, Boston, MA, USA: ACM.

Granovetter, M. 1983 ‘The Strength of Weak Ties: A Network Theory Revisited’, Sociological Theory 1: 201-233.

Krackhardt, D., Nohria, N. and Eccles, R. 1992 ‘The Strength of Strong Ties: The Importance of Philos in Organizations’ Networks and Organizations: Structure, Form,and Action: Harvard Business School Press.

Maier, G., Schneider, F., Feldmann, A., Krishnamurthy, A. and Plattner, B. 2010 ‘A First Look at Mobile Hand-Held Device Traffic Passive and Active Measurement’, Vol. 6032: Springer Berlin / Heidelberg.

Manovich, L. 2001 The language of new media, Cambridge, Mass. ; London: MIT Press.

Martínez Alemán, A. M. and Wartman, K. L. 2009 Online social networking on campus : understanding what matters in student culture, 1st Edition, New York, NY: Routledge.

Pullella, P. 2011 ‘Pope warns of alienation risk in social networks’ Reuters Online, London: Reuters.

Rosenthal, A. 2008 ‘Gerald M. Phillips As Electronic Tribal Chief: Socioforming Cyberspace’, in T. Adams and S. A. Smith (eds) Electronic tribes : the virtual worlds of geeks, gamers, shamans, and scammers, 1st Edition, Austin: University of Texas Press.