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Today predictive technology is commonplace (Siegel) and you may or may not be surprised to know it affects everyone. In today’s technological world everything is digitized and recorded and creates data. This data has helped us find out things as menial as “Vegetarians miss fewer flights” to more important things such as “Early retirement decreases your life expectancy”. All of our actions (including medical procedures, credit application, spam email and purchases of any kind) are recorded and used by businesses to target advertise to us. But with 2.5 Quintillion bytes of data per day, it’s no wonder predictive technologies are employed to sort through all of the big data.

Image Source - http://storagegaga.files.wordpress.com/2011/10/big-data.jpg

“Prediction serves the organization, empowering it with an entirely new for of competitive armament, corporations positively pounce on prediction.” (Eric Siegel, 2013). These technologies are constantly improving prediction where the technology literally learns to predict - it transforms these doctor visits, credit applications and films you watch into trends and information that these companies can use. There are many companies which use this technology to predict all sorts of things. For instance, Wall Street actually has been known to predict demand by following trends across the general public’s activities on twitter! Other more direct methods include determining age, sex and interests to better advertise to your needs. 

One example of direct target marketing is the franchise Target, which uses certain algorithms that are able to determine pregnancy, so accurately in fact that it is able to zero in on the exact trimester the customer is in and advertise accordingly. They are able to determine this through certain items that the customer has bought. There was a case in the US where a teenager was receiving all of this junk mail from Target advertising cribs, diapers and baby clothes. The father was enraged and complained to Target about how this was promoting teen pregnancy - it turns out the teenager was in indeed pregnant and Target knew even before her father did. I thought this was very disturbing as I believe that our privacy is being exploited with news as intimate as pregnancy. In this particular case I believe it should have been the teenagers daughters decision to tell the father and not have a company indirectly tell him of his daughters pregnancy. Asking the question - is privacy over?

It is undeniable though that new media data collection gives companies that much needed competitive edge. Twitter has had such a positively large impact on accumulating data for big data and telemetrics. Nowadays big companies can use big data to their advantage, using telemetrics to measure the social media engagement with certain television shows. In “Telemetrics: Towards Measuring Social Media Engagement with Television” Woodford, Prowd and Bruns talk about a “Twitter Excitement Index” where they were able to measure the volatility of Twitter discussion throughout the television show being aired. Thus they could perform show-to-show comparison of audience engagement allowing comparisons across genres and between networks as well as countries. I was really interested in this as the filmmakers of the television shows could see what the audience liked and change the future shows accordingly to appeal to the audience. Below is an example of the tracking of the tweet discussions from an episode of American Horror Story.

Image Source: Figure 4 Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming). “Telemetrics: Towards Measuring Social Media Engagement with Television.” pp. 2

I believe the examples I have given above really demonstrate how empowering telemetrics and data collected from new media social networks are for companies. Though at the same time we must take into consideration the ethical implication of our data being used for companies to target market us. 

References - 

Siegel, Eric, (2013). Introduction: The Prediction Effect . In Siegel, Eric, Predictive analytics : the power to predict who will click, buy, lie, or die, (pp.1-16). Hoboken, NJ: Wiley.

Hill, Kashmir, (2012). How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did. Accessed May 10, 2014. Available at:http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

Last week new media enterprises where discussed where we where introduced to “Cloud Labour”. Cloud labour website have been developed such as Amazon, Mechanical Turk, CrowdFlower. These websites are basically “Artificial Artificial Intelligence” (Sauter, 2014), creating the illusion that these programs are automatically coming up with things like tags when in fact it has been people who physically identify these things - such as a picture of a boy should have the tag “boy”. Artificial artificial intelligence could be explain by the following image:

image source: https://todaymade.com/blog/mechanical-turk/

These websites create micro-tasks for collective output while providers gain cheap and diverse workforce. Mechanical Turk rewards “Turkers” with a penny for every micro-task they complete. But there has been a lot of controversy over the pay rate where people are getting as little as $2/hr and no benefits. Fuchs states “the entire planet is today a capitalist factory” (2014, 118). Online users can work to produce productivity and capital for major online companies, without a reasonable wage. The barriers between “production and consumption have become liquid in contemporary capitalism” (Fuchs 2013, 107). However what needs to be reminded is that it is the user’s choice as to whether they partake in these cloud labour activities. How you could think of this though is that it is contract work. You choose the hours you work. The closest thing that I can think of this is my job where I can apply online for jobs that suit my availability. I get paid at a good rate but some weeks I can get up to 20 hours a week and other weeks I can get nothing. So, really my average weekly pay rate goes down significantly - but I get to pick when I can work. So there is upsides and downside to this but it is a job I prefer then having a usual part-time job. So many people who work there might think that this lower rate is okay as they get to sit at home all day to do their work.

Gamification has also entered the picture where people are not being paid at all for the work they do. Websites such as “Playbour”, “Doodle4Google”, “Image Labeller” etc. are websites are crowd sourcing similar tasks as Mechanical Turk but they design them as games so they reap all of the rewards. But Fuchs argues in this weeks readings that being a consumer of social media should be consider work and hence should be paid. The data collected on social media is sold to advertiser (Fuchs, 2013) but if they are enjoying what they are doing should they be paid?

References - Fuchs, Christian, (2013). Chapter 5 : The Power and Political Economy of Social Media. In Fuchs, Christian, Social media : a critical introduction, (pp.97 - 125). London, UK: Sage Publications. 

Sauter, Theresa. 2014. “KCB206 Week 4 New Media Enterprises.” Accessed March 26, 2014. http://blackboard.qut.edu.au/webapps/portal/frameset.jsp?tab_tab_group_id=_4_1&url=%2Fwebapps%2Fblackboard%2Fcontent%2FlistContent.jsp%3Fcourse_id%3D_108110_1%26content_id%3D_5232445_1

Today the internet’s content is said to be created by the the “People formally known as the audience” Jay Rosen. In the short life span of Youtube it has launched the careers of vloggers, musician, and comedians. Nowadays, there are ordinary people making a lot of money on Youtube such as Jenna Marbles, PewDiePie, Daily Grace and Ryan Higa. Their youtube channel aren’t even about anything in particular they just have to connect with consumers. For instance Jenna Marbles makes approximately $300,000 a year in just advertising revenue and merchandise from her vlog where her topics include things from how to trick people into thinking your good-looking to imaginary creature such as “land sharks” or helpful tips on how to do your makeup when your drunk. Racking in almost 13 Million subscribers for her weekly videos made in her home.

My personal experience with spreadability of content on a global scale a short film I made a year ago. After only mere hours of uploading it to youtube I had a comment from someone in Germany. This really taught me about how content can reach every corner of the globe. I didn’t know this person and in fact had no contacts in Germany, but it just goes to show that once you put something on the internet there is no telling who will see it.

Advertising has changed due to the internet, many have began using more subtle ways of publicizing their products. The most recent example of this is Ellen Degenere’s Oscar Selfie which subsequently “broke twitter”. This tweet was the most retweeted thing on twitter, ever. But it turns out that this wasn’t just a spontaneous selfie but product placement for the Samsung phone. According to a “The Wall Street Journal” this stunt cost Samsung around $20 million in advertising. So how do company’s know it’s going to work? In short - they don’t. In terms of going viral Theresa Sauter states “There of course is no way to measure if content is going viral but technologies provide affordances that enable the distribution and re-appropriation of content”.

Though in this particular case the Samsung/Ellen’s selfie ended up being a massive success as it ended up becoming a meme where people created new meaning out of this image. Not only did it go viral but “people formally known as the audience” carried it for a longer time then if it was just the picture.

Memes are a perfect spreadability of content. From the “Y U NO” Guy to “grumpy cat” memes are spreading globally. Websites such as MEME GENERATOR” make it easier to keep these memes alive. Websites like these allow us, as users, to generate new meaning to the memes and pass on particular messages our own network of people. So what makes these means beyond our own network of friends? Subsequently, content spreads through “integrated systems of participatory channels” of technologies, their users and the social, cultural, political and economic contexts they are embedded in (Green and Jenkins 2011, 113).

References -

Green, Joshua and Henry Jenkins. 2011. “Spreadable Media. How Audiences Create Value and Meaning in a Networked Economy.” In The Handbook of Media Audiences edited by Virginia Nightingale, 109-127. Malden, MA: Wiley-Blackwell.

Marwick, Alice. 2013. “Context – Memes”. Accessed March 23, 2014. http://contexts.org/articles/fall-2013/memes/

Rosen, Jay. 2006. “The People Formerly Known as the Audience.” PressThink: Ghost of Democracy in the Media Machine, June 27. Accessed March 23, 2014.   http://archive.pressthink.org/2006/06/27/ppl_frmr.html

Sauter, Theresa. (2013) “What’s on your Mind? ‘Self Writing on Facebook as a Tool for Self-Formation. New Media & Society. pp.1-17

Vranica, Suzanne. 2014. “Samsung’s Ad Strategy Behind Ellen DeGeneres Oscar selfie”. The Wall Street Journal. Accessed March 23, 2014. http://www.theaustralian.com.au/business/wall-street-journal/samsungs-ad-strategy-behind-ellen-degeneres-oscar-selfie/story-fnay3ubk-1226844617666#