Murder, she tweeted
The commonality between this weekâs three readings can be distilled down to a very simple take-away message: your data is powerful. Your browsing habits, your tweets, your friends on Facebook: these are an everyday, sometimes banal, collection of your personal minutiae and donât mean anything to you. But in the right hands, these collections are invaluable; theyâre aggregates of your most intimate behaviours and contain codes that allow advertisers to craft traps and steal your hard won gold and rare items (Siegel, 2013). In other hands, your data patterns connect you with a community of like-minders, or suggests you watch Murder She Wrote because you loved Star Trek (Harrington, 2013).
Image credit: Buzzfeed.
But who are the ominous âtheyâ and is it really as ominous as all of that? The âtheyâ here are corporations who buy Big Data to better understand what they can convince consumers they need and, as usual, it is both good and bad.
Itâs bad that companies you donât know, trust or choose to help can buy your data to streamline their exploitation of you. Itâs bad you have no agency in whether your data is mined.
Itâs good that predictive technology has applications in medicine and law-enforcement (Siegel, 2013: 6, 7). Itâs good in our day-to-day lives â predictive text, though often hilarious, usually save time. Itâs good to be connected with friends and projects that are relevant to you, based on past connections. Itâs bad (and creepy) to encounter targeted advertising, suggestively showing you a product you browsed last week.
Internet advertising sometimes lacks integral human intuition. Image credit: Buzzfeed.
Itâs good that longitudinal surveys of behaviour can prevent emergency situations. For example, the British National Grid plan their schedule around the television show Eastenders. During ad-breaks on Eastenders, 1.75 million kettles get switched on resulting in a huge surge of electricity usage (Raby, 2013). If they werenât able to mine data and determine what was causing such a huge surge, people would experience black-outs. Data analysis allows them to plan ahead.
Really, really lack it. Image credit:Â Buzzfeed.
A side note: Harringtonâs article discusses the Twitter discourse that happens online while a show is airing but I wonder â how has torrenting changed this culture? The recurring example of âGame of Thronesâ comes to mind, as here in Australia we are able to download GoT on Monday at lunch-time and then we watch it at various times during the night. Thereâs not one synchronised viewing schedule â will we get the full experience of online discourse, or is the appeal dampened if itâs not in âreal timeâ? For me it is, hence why I tweet for qanda and not got GoT. Woodford, Prowd and Brunsâ model for the Twitter Excitement Index supposes that the majority of the audience is viewing the show live. But our culture of downloading shows to keep up with the Jones (the U.S. and U.K.) and the dearth of affordable on-demand packages means that we view new releases in a staggered way and miss out on real-time discourse.
But I digress. As with every investigation of this nature the outcome is: advances in technology always have both good and bad applications. The best way to keep your head above water is to learn about both to manipulate the good to your advantage and the avoid pitfalls of the bad. And take Netflicks suggestions with a grain of salt.
And sometimes they just really know their audience. Image credit:Â Buzzfeed.
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Harrington, Stephen. 2013. âCh 18 Tweeting about the Telly: Live TV, Audiences, and Social Media.â 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. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.Hoboken: Wiley Publishing.
Raby, Mark. 2013. âTea time in Britain causes predictable, massive surge in electricity demandâ. Accessed 10th May, 2014. http://www.geek.com/news/tea-time-in-britain-causes-predictable-massive-surge-in-electricity-demand-1535023/
Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming). âTelemetrics: Towards Measuring Social Media Engagement with Television.â Accessed 10th May.