#data #twitter
Stephen Harrignton states “A significant amount of attention in the media industry over the last decade has been directed towards the idea of 'convergence’” (237, 2013). If the past 10 weeks have taught us anything, it’s that we are living in an era of convergence. No longer do we stick to a structured set time of viewing a TV show. Now we can access television shows at any convenient time and on whatever device we choose. With this idea of convergence comes the ubiquitous new media site, Twitter. It is, arguably, the most influential social networking site. Several TV shows targets audiences persuading them to tweet along while watching the episode. They set up and use specific hashtags and even go so far as to get the cast from the show to tweet along during the episode. Twitter is a valuable tool to get information from a wide range of people and audiences. It is best to analyse the different levels within Twitter to get a better understanding of how this information, or data, can be best used.
Axel Bruns and Hallvard Moe state that there are “three key layers of communication on Twitter: the micro level of interpersonal communication, the meso level of follower-followee networks, and the macro level of hashtag-based exchanges” (16, 2013). This macro level of hashtag-based exchanges are what most TV shows rely on for engaging audiences. Axel Bruns and Hallvard Moe also suggest “The communicative flows which result from the establishment of active hashtag exchanges, at least in the short term, are usually less predictable than those enabled by follower-followee networks-but they are also amongst the most visible phenomena on Twitter, and most accessible to research” (18, 2013). Research is essential to collecting big data on audiences to enable websites and different media to specifically and accurately target audiences with new content. This data is not only used to target audiences but to also predict new trends. Eric Siegel suggests “Learning from data to predict is only the first step. To take the next step and act on predictions is to fearlessly gamble” (15, 2013).
This is an interesting quote because it can accurately explain how not every bit of data can be spot on. Essentially every click, every sent message, every searched item is a piece of information that create big data that can be analysed. Eric Siegel states “as data piles up, we have ourselves a genuine gold rush. But data isn't the gold. I repeat, data in its raw form is boring crud. The gold is what's discovered therein” (4, 2013). So from every single traceable movement everyone makes there is critically important information hidden within. The best part for data analysts is that Hashtags on Twitter, which are constantly growing, can make getting data and information easier and even produce more accurate results, even if they can be a little less predictable.
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
Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.











