Audience Adaptation: Water-cooler to Social Media
A major goal of television executives is to create content that people talk about so that they continue to watch the program, and the station can earn more through desirable advertising spots. Memorable moments from the previous night’s episode of Friends, Seinfeld or the Sopranos were gossiped over so that viewers could disseminate and share their affection for the shows and it’s characters in what became called “water-cooler talk”.
“History shows that new technology rarely result in the displacement of long-standing audience practices, but are typically blended into existing routines and activities instead” (Harrington 2013, 238).
With the rise in popularity of new media content such as the social media platform, Twitter, Harrington’s “long-standing audience practices”, ie water-cooler talk, are blended into the activity of watching a live television program with the phenomenon of live-tweeting, where Twitter users can make live comments and posts referring to what they are watching, that reach potentially millions of people.
HubShout (2013).
But these tweets offer more than just allowing fans to feel more connected.
The promotion from television networks to get audiences to join the conversation and live-tweet during programs is beneficial for the television program and the broadcasting networks that receive this free advertising, but also for market research companies that can analyse the digital data such tweets create and the data stores they contribute to.
Market researchers are now able to able to process information from the internet, and more specifically social media to understand audience behaviours and to identify target markets for products and services (Pérez-Latre, Portilla & Blanco 2011, 68). Similarly, networks can use this data to understand the activities of their viewers during certain programs, and allow them to make alterations that may attract more viewers of similar ilk. Through a process of “machine learning”, computers and analytics are able to use past data to provide a predictive analysis of future activities (Siegel 2013, 4).
Predictive analysis changes the game in that instead of providing forecasts, ie how many people will watch a program, it predicts, and says who will be watching the program (Siegel 2013, 12).
Highfield, Harrington and Bruns (2013,1) describe Twitter as a “technology of fandom” in that “it serves as a backchannel to television and other streaming audiovisual media, enabling users to offer their own running commentary on the universally shared media text of the event as it unfolds live”.
The activities such as water cooler talk that we took part in a decade ago aren’t dead, we’ve just converged them with new technologies, and adapted them to enable us to be a part of the conversation, wherever that may be. As Kevin Spacey said in his Edinburgh Television Festival address, “The water cooler has gone virtual, because the discussion is now online” (2013).
References
Francisco Javier Pérez-Latre, Portilla, I. & Blanco, C. S. 2011. “Social Networks, Media and Audiences: A Literature Review.” Communicacion Y Sociedad 14 (1): 63 - 74.
Harrington, Stephen. 2013. “Ch 18 Tweeting about the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt & Cornelius Puschmann, 237-248. New York, NY: Peter Lang.
Highfield, T., Harrington, S. and Bruns, A. 2013. ‘Twitter as a Technology for Audiencing and Fandom: The #Eurovision phenomenon’. Information, Communication & Society, 16 (3), 315-339.
Hubshout. 2013. "Nielsen Includes Twitter in TV Ratings: Impact on Social Media Marketing [VIDEO & INFOGRAPHIC]." Accessed May 1, 2014. http://hubshout.com/?Nielsen-Includes-Twitter-in-TV-Ratings:-Impact-on-Social-Media-Marketing-%5BVIDEO-&-INFOGRAPHIC%5D&AID=1039.
Spacey, Kevin. 2013. “James MacTaggart Memorial Lecture.” YouTube video, posted August 23. Accessed May 1, 2014. https://www.youtube.com/watch?v=P0ukYf_xvgc.
Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.






