QUT KCB206 2014 Tutorial Group 12

[kcb206group12]

The New Frontier: Big Data

The vast potentials of Big Data are not simple to grasp. The notion that we could somehow harness infinite amounts of information in order to apply it systematically to our everyday routines seems unbelievable. Yet, that is exactly what Big Data and the practise of telemetry intend to do, 'unlocking significant value by making information transparent and usable at a much higher frequency' (McKinsey Global Institute, 2009).

One of the most notable consequences of these developments is the mass impact it will have on industries around the world. More and more companies already, are formulating their big data strategies, or presenting their first big data success cases. Ultimately, the analysis of large data sets is set to become a skill leaders in every sector in every industry will need to master if they intend to stay ahead of the game; becom[ing] a key basis of competition and growth for individual firms (McKinsey Global Institute, 2009).

Exactly how this information will be applied to increase profits will vary, however a report released by McKinsey Global Institute stipulated that any given retailer using big data to its full potential could increase its operating margin by more than 60 percent, whilst the use of big data by the US health sector could create more than $300 billion in value every year (two thirds of that being in the form of reducing US healthcare expenditure by eight percent). You can read the full McKinsey report here.

These kind of propositions are mind blowing, and upon first hearing beg the question that if the changes proposed, which would use big data to revolutionise industries from retail to healthcare, were going to be easy to execute then they would have been implemented already.  

It's also surprising that the general public hear and know so little about these potential developments. It seems, given the relatively small amount of big data that has so far reached the public domain, that perhaps bigger corporations and commercial organisations might prefer to keep it this way. This surely can’t work to the advantage of the potential of big data, as several articles and reports (including the McKinsey report) suggest that in the near future (2018), there will be a significant shortage of people with the analytical know-how and specific skill-set to effectively analyse big data. The number of experts in this particular field is already relatively small considering the demand for it. The report “Big Data Analytics: Adoption and employment trends” found that over 70 percent of organisations find it challenging to hire the specialists they require to handle big data. 

Interestingly, but not surprisingly, given it’s innovative nature, social media appears to be leading the charge when it comes to using big data in order to increase their own levels of success. In 2013, Twitter helped secure its future in big data buy purchasing Bluefin Labs - a leading social TV analytics company. You can see their announcement for it here. 

The question is, how and when will other industries begin following suit? 

Could we ever really have global access to all information? I look forward to finding out.

Bibliography

Borne, Dr. K. "Big Data, Small World". TED Talks, June 2013. School of Physics, Astronomy and Computational Sciences, George Mason University. (https://www.youtube.com/watch?v=Zr02fMBfuRA)

[kcb206group12]

Big Data and Big Corporations

What is big data and how are companies using this tool to benefit business performance? In an interview with the Economist (2012) Andrew Chung highlighted ‘corporations use it within leveraging actions through data gathered from the internet or internal databases.’ Once consumer information is collected it can be scientifically processed to discover useful strategies to support plans and decisions made. Jason Silva (Economist, 2012) indicates ‘this data helps organisations to understand consumers, or even whole civilisations and how best organisations can connect with them.’

  https://www.youtube.com/watch?v=ahZGEusG13A

These actions are currently being implemented by traditional media such as television networks. Within this process new media, such as social media platforms are incorporated within televised programming. This information from these touch-points, is allowing data to be gathered through social media by the sponsors of the television programmes. Master Chef is a great example of this were vast sponsorship is paid by Coles – for the benefit of product placement, internal branding and the company’s own social media and online stores being promoted. “Master Chef’s Facebook page has over one million likes and it has a Twitter profile with 80,000 followers, which is connected to Coles who can then reap the rewards” (Glasner, 2013). When consumers then access these online touch-points, a data market is created and this information is gathered, processed then analysed. Through business intelligence, visualisation can then be used from these analytics to understand the behaviour of consumers and predict their needs and wants. Strategies can then be implemented and strengthened – such as marketing tactics, or even predicting volumes of stock to supply increased demand for products. This allows Coles to be more prepared and strengthens its chances within its internal environments. Raj Dalal chief executive of big data research firm Big Insight, recently stated in an interview with Business Review Weekly (2013) that,  “Coles has used big data not only to pick the best locations to build expensive supermarkets, but also to better guide its marketing campaigns to make them more specific.”

  http://www.experian.com.au/blogs/marketing-forward/2013/02/11/cooking-up-a-winning-sponsorship/

The ethics of how to store and use big data is becoming a huge contentious debate. This is particularly when corporations are taking and trading consumers information through the internet without many consumers understanding that there information is being recorded. Juri Han from IBM (2012) highlighted ‘there is a lag between market adaptation and the appropriate legislative frameworks. Until this is rectified is even more important that big data is managed ethically.’ An example of this is how Target can use its big data resources, to predict when their customers are pregnant, with the purchases they make. With this information Target created promotions which ended up being pushed towards a sixteen year old pregnant women even before her parents had even discovered she was actually pregnant. Reporter Nina Golgowski (2013) indicated ‘this ended up being a very awkward situation for the family and even more embarrassing for Target, when the family confronted the business. It has since turned into a public relations nightmare for the company.’ This situation only supports the fact that big data does create huge power for these corporations – however this power has come from sometimes private information and with all power come responsibility. As this technology is growing rapidly within many industries, it is surely not going to be the last nightmare businesses with this magnitude will encounter.

  http://www.dailymail.co.uk/news/article-2102859/How-Target-knows-shoppers-pregnant--figured-teen-father-did.html#ixzz31PlY2KC4

                Reference List:

Chung Andrew, (2012), Economist, ‘What is big data,’ sourced from; https://www.youtube.com/watch?v=ahZGEusG13A

  Glasner Mathew, (2013), Experian Marketing Solutions, ‘Cooking up a winning sponsorship,’ sourced from; http://www.experian.com.au/blogs/marketing-forward/2013/02/11/cooking-up-a-winning-sponsorship/

  Golgowski Nina, (2012), The Daily Mail, ‘How Target knows when its shoppers are pregnant - and figured out a teen was before her father did,’ sourced from; Read more: http://www.dailymail.co.uk/news/article-2102859/How-Target-knows-shoppers-pregnant--figured-teen-father-did.html#ixzz31PlY2KC4

  Han Juri, (2013), IBM, ‘Living Ethics: Newsletter of the St. James Ethics Centre’

  Mercedes Ruehl, (2013), Business Review Weekly, 11 June 2013, ‘Coles, Woolies and the big data arms race,’ sourced from; http://www.brw.com.au/p/tech-gadgets/coles_woolies_and_the_big_data_arms_4I2P2oieDKZGdev5aY778H

  Silva Jason, (2012), Economist, ‘What is big data,’ sourced from; https://www.youtube.com/watch?v=ahZGEusG13A

[kcb206group12]

How New Media Is Changing Politics:

  The advancements of the internet has allowed new media to evolve. This evolution has created a struggle between those in power and the activism of citizens. Coglan (2013) highlights ‘the perfect recipe of economics within a democratic country; is a balance between financial, social and government economic stability, that results in the best overall outcome that most benefits that society.’

The role of new media within political situations, uses new and traditional media platforms to inform or educate citizens, by providing channels for political actions and allows a voice for the public to be heard. These platforms have provided transparency for the public and for the greater good of societies. It can reveal truth and spin, while allowing access to more information, for people to decide for themselves on how they feel about certain political agendas. Zheng (2005) suggests “anyone with the internet can now connect with more people, to make more change.” This has allowed for stronger people power increasing activism and mobilisation when change is being asked for. Just witness the current occurrences around the world with demonstrations in Russia, Ukraine, Egypt and the Occupy Wall Street campaigns in Western countries. These have all been intensified using new media and without the internet – these group’s agendas may not have been witnessed, then supported by other citizens of other nations.

Although new media has allowed the political atmosphere to become more transparent, there are some such as Julian Assange that argue ‘people within power are taking more control of new media platforms, while using these tools to manipulate policies and the public to empower campaigns that benefit individuals and corporations’ (Capon, 2012). Hedley (2013) suggests ‘these agendas are not to benefit the voting citizens, but to strengthen individual’s own powers, which is against the social economics of a democratic society.’ Mining companies were a good example of this were promotions through YouTube, social media and traditional means were used to manipulate a change in policy. Howcroft (2011) highlights that ‘before the mining companies’ promotions, there was strong support for the mining tax from the public. By the end of this promotion the industry managed to swing public support in the opposite direction.’ This has now created a vote for the mining tax to be eliminated.

http://youtu.be/CREUOpaVYJQ

http://youtu.be/gOdGrTCtbpI

New media has not only had the power to persuade public opinion but, also uncovered and informed citizens of corrupt behaviour. Eddie Obeid who was found to be strengthening his own agendas, through the corruption of using his powers within politics, is now being charged. Gathered support through new media and traditional media, pushed politicians to inform law enforcement. It also has informed the public of the process of the investigation and the depth of corruption.

http://www.abc.net.au/news/2013-07-31/icac-findings-released/4855128

  Although a balance between financial, social and government economic stability should result in the most beneficial outcome for society, Coglan (2013) also indicates that ‘nothing within economics is free.’ With this in mind – one side within a political agenda will gain and contribute their contribution, however a deadweight is created as a result of the opposition losing power. The question then, with a shift in the power struggle between increasing people power (through new media) verse control over new media through censorship and propaganda – how will this struggle, not only change the internet, but societies in general? 

    Reference:

ABC News, (2010), ‘The Association of Mining and Exploration Companies is to relaunch its advertising campaign against the Federal Government’s proposed mining tax’, July 25 2010 , retrieved from; https://www.youtube.com/watch?v=gOdGrTCtbpI

AMEC, (2010), ‘Mining Tax Ad’, retrieved from; https://www.youtube.com/watch?v=CREUOpaVYJQ

Capon Felicity, (2012), The Daily Telegraph, ‘Julian Assange warns of internet danger’, retrieved from; http://www.telegraph.co.uk/culture/books/booknews/9706862/Julian-Assange-warns-of-internet-danger.html

Coglan, Louisa; Hubbard, R. Glenn; McCulloch, Rosalind, (2013), Essentials of Economics, Pearson Australia

Hedley Thomas, (2013), The Australian, ‘How Julia Gillard was ready to censor our free media’, November 16, 2013 12:00am, retrieved from; http://www.theaustralian.com.au/national-affairs/opinion/how-julia-gillard-was-ready-to-censor-our-free-media/story-e6frgd0x-1226761407076#

Howcroft Russel, (2011), Gruen Transfer, ‘Episode 7’, Wednesday 9 November 2011, retrieved from; http://www.abc.net.au/tv/gruenplanet/pages/s3360040.htm

Wells Jamelle, (2013), ABC News, ‘ICAC recommends charges against Obeid’, Mon 26 Aug 2013, 2:09pm AEST, retrieved from; http://www.abc.net.au/news/2013-07-31/icac-findings-released/4855128

Zheng Bijian, (2005), Foreign Affairs; The New American Realism, ‘China’s Rise To Peaceful Great Statas’, Vol. 84, No. 5 (Sep. - Oct., 2005), pp. 18-24, Published by: Council on Foreign Relations, retrieved from; http://www.jstor.org/discover/10.2307/20031702?uid=3737536&uid=2&uid=4&sid=21104052033873

[kcb206group12]

Obsessed with Data

Our obsession with information has caused the inundation of data on the storage capacity of the internet. The collection of data sets has become so large and complex that it is difficult to understand information without the use of processing applications (Snigders, Matzat, Reips, 2012, p 1-2). And these databases are growing at a phenomenal rate. Figures suggest that the volume of data doubled approximately every three since the beginning of the internet in the public domain. In 2007, the volume of data is believed to have reached 295 exabytes and, it is predicted that it will exceed 1000 exabytes by 2015. This is far more information than anyone could process in their lifetime, or even 100 lifetimes (Hilbert, 2011). So why is so much information being created?

Much of this data has come to existence through the prevalence of Big Data mining in almost every information industry. Mass amounts of data are being extrapolated from the keystrokes we use on social media websites, the web pages we visit most often and the videos that hold our attention the longest. In fact, any click or non-click has the potential to be mined, catalogued and sold to any business or government (Siegel, 2013). Big data has become a valuable commodity as the information attained can be used to create economic/scientific/business models and predict the success of a product through patterns in the habits of consumers (Woodford, 2014) (Siegel, 2013). It’s kind of like the way television studios use ratings to create and buy content but on a much more personal scale.

This ability to predict patterns in human behaviour has valuable applications for the rest of society outside of the internet too. A trial is currently underway in Los Angeles where law enforcement officers must rely on the use of data to patrol and respond to crimes. In the most dangerous parts of LA where gang violence is rampant and the production and distribution of narcotics is used to fund criminal activity, the quick response of a law enforcement officer is vital in protecting the innocent. A computer algorithm based on the collection of crime data over the past 80 years attempts to predict the location of a crime before it occurs. By projecting the current data on to our understandings of human behavioural patterns, scientists have been able to further isolate potential crime hot spots, leading to the arrest of offenders before a more serious crime is committed (BBC, 2013). Certainly, this has had a considerable impact on the way law enforcement works in the United States, but when does predicting big data impede on innocence? The potential to abuse criminal data may have detrimental effects on implicating criminals on a crime they haven’t committed. Much like Steven Speilberg’s Minority Report, data has the potential to be manipulated and used for political gain (Yaniv, 2014). With our obsession over data and its application in almost all aspects of life, it’s almost impossible to comprehend its positive or negative potential. Like most innovations, Big Data could be used for either but its up to us to dictate its future.

References:

BBC. 2013. “The Age of Big Data.” BBC Video posted 18 July 2013. Accessed May 9th http://www.bbc.co.uk/programmes/b01rt4c7

Martin Hilbert & Priscila López (2011). The World's Technological Capacity to Store, Communicate, and Compute Information. Science, 332(6025), 60-65.

Mor, Yaniv. 2014. “Big Data and Law Enforcement – Was ‘Minority Report’ Right?” Wired. Accessed May 9th http://www.wired.com/2014/03/big-data-law-enforcement-minority-report-right/

Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.

Snijders, C., Matzat, U., & Reips, U.-D. 2012. ‘Big Data’: Big gaps of knowledge in the field of Internet. International Journal of Internet Science, 7, 1-5

Woodford, Darryl. 2014. “New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford)”. Accessed May 9th http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

[kcb206group12]

Big Data, changing the way we sell ourselves online

Online media, just like many others relies on viewership to gain revenue. The more people reading your stories the better, because of this people are constantly trying to appeal to certain demographics and audiences. 

Online media is heavily influenced by big data, with users creating accounts to view content it is incredibly easy for content creators to understand their audience. 

This is an example of a YouTube analytics page, while this is for my own youtube channel with a total of one video other larger channels have all potential audiences covered. Everything from viewers age and gender to country of origin. All this information helps content creators understand what content they need to create to please their audience.

This can both be good and bad for the consumers and creators of this content. While knowing who is interested in what you have to say helps you create content for them it can also impair the quality of the content. This is commonly referred to as "Selling out" and many online personalities have been accused of this. Instead of creating things they are interested in they pursue revenue. This can backfire for the producer resulting in a lower than expected profit. 

So while there are some slight risks when using Big Data it helps the content creator understand who their audience is.  All forms of media benefit from Big Data though it is easier to collect for online audiences. This information can help creators properly appeal to their audience which is huge in the impersonal media of the internet. 

Sources: https://www.youtube.com/analytics?o=U

https://developers.google.com/youtube/analytics/

http://www.shopify.com.au/blog/6763696-youtube-analytics-10-ways-to-track-video-performance

[kcb206group12]

Big Data. Big Deal?

Just how much information can be found out about you on the internet? Well, after this week’s lecture, I learnt that a lot of information can be discovered, stored and reused in the World Wide Web’s stock market of big data (Woodford, 2014). I’m ashamed to admit, I was actually shocked by how much of my information is accessible to anyone. As a relatively new Twitter user, I am just starting to get my head around the “retweet” and “@reply” concepts. But this information, along with hashtags, is what is gathered to learn about discussion topics, number of contributors and size of audience (Woodford, 2014). Using structural layers on Twitter (macro, meso and micro), data can be analysed to predict current and future trends (Woodford, 2014). I just thought I was retweeting it because I liked the content of the tweet! However this simple action (in relation to particular subjects) is the kind of data that is being analysed by corporations in order to improve promotional campaigns.

  The same data is stored when you surf the internet. By using ‘cookies’ technology, browser information is used to advertise specific content that the user would be interested in. Facebook is a particular example of how big data is transformed into an economy. Facebook (and other social media platforms) are free, in exchange for information. So, thanks to ‘cookies’, when I open my Facebook account on my laptop, I instantly see ads from ASOS and eBay advertising similar leather jackets that I’ve been on the hunt for recently. Siegel discusses this as ‘prediction’ and that computers are now ‘learning’ to produce data that is ‘the new oil’ (2013). Siegel continues to discuss that data will, and is becoming “modern society’s greatest and most potent unnatural resource” (2013). As Albert Einstein explained “The only source of knowledge is experience.” (Siegel, 2014).

Source: Personal Facebook Profile 

So, if all this personal data is being learnt and traded internationally, should I be concerned about my privacy? Unfortunately, I’d say yes. Anyone can see my tweets if they were re-tweets or if they included hashtags. From this, they can gather a pretty good idea that I’m interested in politics, fashion and that I follow a few people that I would be ashamed to admit in a close-knit circle of people (they’re my guilty-pleasures). They can see what I look like (profile picture – duh!), and see what common ‘followers’ we have. This basic data constructs my online identity, contributing to my big data ‘bank’ that can then be used ‘against’ me in future web browsing. At the end of the day, its all about how efficiently you manage your online privacy settings. That, my friend, like so much of the Web 2.0 era, is all about choice. Now, excuse me while I just edit my settings.

  Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc. Retrieved from  https://qutvirtual3.qut.edu.au/qv/olt_material_search_p?p_unit_code=KCB206

  Woodford, Darryl. 2014. “New Media, Big Data & Telemetrics [Lecture Notes]. Retrieved from 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_5232451_1

[kcb206group12]

Predictive Analytics and Targeted Marketing

Today’s marketplace continues to develop in the digital realm with an ever-increasing number of companies now interacting with consumers online.  This technology also means that the vast majority of consumer behaviours can be easily tracked and stored.  For marketing divisions this is a newly discovered gold mine with practically unlimited opportunity if utilised to full potential.  “The process of machines learning from data unleashes the power of this exploding resource. It uncovers what drives people and…with the new knowledge gained, prediction is possible” (Siegel 2013, p.4).  These consumer behaviour predictions are what marketers require for the optimal alignment of product advertising and promotion with their target audience. This method however, also poses ethical dilemmas.  Data tracking could quickly become intrusive, especially given the consumer is often unaware of how they are being targeted or if they are being targeted at all.

Marketers often target consumers with predictive analytics or a “means to drive per-person decisions empirically, as guided by data” (Siegel 2013, p.12).  It is scientifically based research and is much more accurate than traditional forecasting as it eliminates the need for opinion and possibly biased outcomes due to cultural expectations about purchase habits.  It must be recognised however that subjectivity does still play a role when defining sets of data and interpreting why certain consumers behaved as they did.  But possibly of most importance is the question of whether this new technology is really working for corporations?

As an internet user myself, I have witnessed on many occasions a change in advertising after an online purchase.  It suddenly becomes much more targeted to products and services that I should theoretically be interested in however this causes a sense of unease.  A study in the UK largely identifies with this, stating “69% of consumers say they find it creepy the way brands use the information they hold on them” (Strong 2013).  This correlates with the concept of the ‘uncanny valley’, a term which originated in robotics but now is widely used to explain why people feel uncomfortable with businesses knowing too much about personal life choices and behaviours.  Strong explains that “initially consumers enjoy the personalisation or marketing communications…however there appears then to be a line which is crossed where there is too much personalisation for consumers’ comfort” (2013).  This is happening more frequently than ever before with a recent and famous instance including the big brand name, Target.  The retail giant compiles big data about consumer purchases and in this instance advertising material relating to baby products was sent to a teenage girl causing outrage.  Turns out Target had recognised the girl was pregnant due to recent purchases, all before her parents had knowledge of the pregnancy (Masnick 2012).  This scenario highlights how unwittingly intrusive big businesses can be.

As technology develops, the marketing of products will continue to become more targeted and innovative as consumer data is further researched and understood.  Corporations will have to consider however, the most ethical and unobtrusive methods of disseminating advertising material as this will affect how the business is seen amongst the public sphere and ultimately the bottom line.

References

Masnick, M., 2012. Getting Past the Uncanny Valley in Targeted Advertising. [Online] Available at: http://www.techdirt.com/blog/innovation/articles/20120217/03044617792/getting-past-uncanny-valley-targeted-advertising.shtml [Accessed 11 May 2014].

Siegel, E., 2013. Introduction: The Prediction Effect. In: Predictive Analytics: The Power to Predict who will Click, Buy, Lie or Die. Hoboken: Wiley, pp. 1-16.

Strong, C., 2013. The Big Data Arms Race Part Two: Consumer Perceptions. [Online] Available at: http://www.theguardian.com/media-network/media-network-blog/2013/oct/04/consumer-marketing-big-data-perceptions [Accessed 11 May 2014].

 YouTube, 2012. Target Knows When Your Pregnant. [Online] Available at: https://www.youtube.com/watch?v=XH1wQEgROg4 [Accessed 11 May 2014].

[kcb206group12]

The Value of Our Data

Everything that we do online is recorded and potentially able to be accessed by third parties. This is the collection of big data. Big data is created every time we engage with new media channels and is more often than not up for grabs by advertisers. Siegel (2013, 4) explains that it is invaluable to organisations as it helps to ‘uncover what drives people and the actions they take’.

  The types of programs we view online, the websites we visit, particular products we view, pages that we ‘like’ on Facebook and the interests that we list all contribute to big data. A key example is use of hashtags on Twitter. Hashtags are used as a way of coordinating discussion around a topic and as a result they are a simple and ideal way of collecting data for analysis. Hashtags are often used to create a ‘map’ of a certain topic, event or person. These thematic clusters are invaluable to those wanting to gain insight to the popularity of a topic, its key demographic and its associations among other things. For example, the interaction of viewers with television through social media is commonly analysed. Use of hashtags can help producers understand their target demographic and key themes apparent in Tweets about their show. Woodford, Prowd and Bruns (n.d., 1) explain that ultimately they are able to measure the shows success with that particular audience.

  Siegel (2013, 2) states that prediction is a key element of big data that serves the organisation. For example, using the data that users provide enable organisations to create target advertising. Klosowski (2013) explains this involves taking big data to specifically tailor ads to certain consumers. Many users particularly notice this on social media sites such as Facebook. Slegg (2014) asserts that ads are directed to Facebook users based on pages they have ‘liked’ and what information they have listed. This has been particularly obvious to me based on the amount of ads for online shopping websites I constantly see in and around my news feed. In another regard, stores can monitor what products people purchase and where and when they prefer to shop. Kermond (2012) describes that this data gathered from rewards cards and products viewed online can then inform offers and advertisements sent to specific customers.

  Naturally many ethical concerns have been raised surrounding this practice. Of particular concern is that consumers are unaware that the advertisements are targeted to them. Others claim infringement of privacy and the need for consent. Regardless the practice is clearly successful for organisations, with Siegel (2013) explaining that the practice does not merely predict consumer behaviour but also shapes consumption. While there are some ways to get around it, it appears that this practice will be here to stay.

    References

  Kermond, Clare. 2012. “Millions targeted in Coles relaunch of FlyBuys scheme.” The Sydney Morning Herald, April 20. Accessed May 10, 2014. http://www.smh.com.au/business/millions-targeted-in-coles-relaunch-of-flybuys-scheme-20120419-1xa58.html

  Klosowski, Thorin. 2013. “How Facebook Uses Your Data to Target Ads, Even Offline.” Accessed May 10, 2014. http://lifehacker.com/5994380/how-facebook-uses-your-data-to-target-ads-even-offline

  Siegel, Eric. 2013. Predictive Analysis. New York: John Wiley and Sons. 

  Slegg, Jennifer. 2014. “Facebook Gives Advertisers More Targeting Options.” Accessed May 10, 2014. http://searchenginewatch.com/article/2330348/Facebook-Gives-Advertisers-More-Targeting-Options

  Woodford, Darryl, Katie Prowd and Axel Bruns. n.d. “Telemetrics: Towards Measuring Social Media Engagement with Television.” 

[kcb206group12]

Big Data isn't all That Bad

Since the introduction of the World Wide Web, social medias have helped shape society and the way in which we communicate and converse with one another. Arguably, today we are a lot more forthcoming in regards to publicizing our values, attitudes and beliefs online (Gurak, 2008). So much information can be obtained by simply scrolling down one’s Facebook profile page. The issue with this is plain, most who are connected to Facebook are not yet aware of large corporate bodies obtaining this information. Whether it be in aid of a certain marketing strategy or some sort of advertising ploy, third parties have access to your ‘private’ profile page and can skew which advertisements or pop ups are to be previewed throughout your newsfeed (Woodford, 2014). Its annoying yes, but its equally as brilliant.

After one has signed up to Facebook, they are asked to ‘complete their profile.’ Here, a series of questions begin to pop up on your page…What books do you like? What movies do you like?...Do you like this musician?...Do you want to like this page that 67 of your friends have also recently liked? In the beginning I just assumed that Facebook got me, Facebook knew what I was about and knew which pages to throw at me. I didn’t think to look beyond that, beyond Facebook being a considerate piece of new media that didn’t want to leave me out of something that all of my friends were involved in, but when I began to see advertisements pop up that seemed all too well tailored to me and my current fixations, it all got a little too coincidental. After visiting the Contiki website on more than 3 occasions I began to notice Contiki adverts on the side of my screen. Princess Polly, pop cherry, Dissh, student flights, car sales, Disney, even my bank began to haunt me on my social media.

   A lot of negativity is shed on the topic of privacy when it comes down to social media. I for one am all for it. In aid of consumer behaviour research, your information is in the hands of industry professionals who have no intention whatsoever of using your information for defamatory purposes. Its purely used to better structure your home page, to personalize and modify it to suit you, not your friends or your friends friends, but you. Facebook isn’t all that bad. Technology is fast moving, yes, and I understand that certain ethical and legal considerations have to keep up with these advancements to retain some sort of balance between both consumers and producers of data (Wadhwa, 2014). However, in finding out what one’s favourite My Kitchen Rules couple is in order to sway the voting system, is this really all that bad? Must we make an issue out of something as mediocre as this? If whatever her name is from Chloe's ‘Birthday Drinks’ last weekend can view which artists I’ve liked from the 8th grade until now, then why not add some big corporate body to the list also? In comparison to my friends online, at least they're bound to care and consider my reasons for why I like a particular page, good or figure. 

References

Gurak, Laura. 2008. ‪Cyberliteracy: ‪Navigating the Internet with Awareness. Michigan: Yale University Press.

Wadhwa, Vivek. 2014. “Laws and Ethics Can’t Keep Pace with Technology.” MIT Technology Review, April 15. Accessed May 10, 2014. http://www.technologyreview.com/view/526401/laws-and-ethics-cant-keep-pace-with-technology/

Woodford, Darryl. 2014. “New Media, Big Data & Telemetrics.” Retrieved from 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_5232451_1

[kcb206group12]

Big Data: The New Oil

Described as "the new oil", data obtained about our internet use is becoming a crucial aspect to businesses and researchers in determining consumption patterns among users. Raw data obtained from networks such as social media platforms and online shopping sites can be invaluable for a company to understand what customers are saying about their product (Siegel, 2013). Further analysis of this data can help predict what consumers will want as a result of this, alloing companies to produce it.

Big data can help companies predict consumer behaviour and allow them to recommend products based on previous purchases. During a recent search for a new pair of shoes online, I looked through a number of well known sites such as Asos and The Iconic. In the next few days I began noticing advertisements for the shoes I had been looking at appearing in the sidebar of my Facebook newsfeed. It didn't stop there. Soon most of the sites I went on that featured advertising began displaying the exact same shoes and some even continue to do so months after I made a purchase.   

This process is known as Predictive Analytics, where technology learns from data to predict behaviour and suggest other actions (Siegel, 2013). While Predictive Analytics is incredibly useful for companies looking for a competitive advantage, it's slightly disconcerting as a consumer to know that every click and post I make is being collected and can be exploited by big business. While it can make finding the items I want easier, it's also frustrating to have advertising so blatantly in my face, like having the same shoe and brand being pushed at me for months as I mentioned above.

Social media data is beginning to play a key role in tracking television viewership and trends. Twitter has become a popular medium for television viewers to express their opinions on episodes and interact with cast and even the show itself, with programs such as Big Brother showing viewer tweets during episodes. By using official hashtags and tweeting official accounts, researchers can measure audience engagement and calculate an Excitement Index, averaging a rate of tweets-per-minute for different programs (Woodford, 2013). As a Twitter user I often use hashtags to engage in conversation around television, especially for reality shows such as The Voice and my other favourite shows like Teen Wolf. As well as creating buzz and public discussion about programs, Twitter is also useful for gauging audience demographics such as age and gender which can be used by marketers to appeal more accurately to their target market.

Big data is a readily available source of information for people and companies to use to track and record behaviour. Being able to accurately identify traits and habits allows technology to cater to our every need and even begin to predict our purchases. Despite concerns over how data is used by companies, I view Predictive Analytics as an emerging core element of production and marketing.

  References

1. Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.

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

[kcb206group12]

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.

[kcb206group12]

YouTube + Big Data = $$$

Week 10 (Week 9 Content) – Michael Moudakis n8854491

  Big data is considered a crucial currency across industries, used to understand consumer’s habits, needs and expectations for companies.

Big data is an important currency across many industries. An important form of big data is analytics, which allows businesses to see social media & online behavioral data and adjust their marketing strategies accordingly. Analytics is a commodity that is largely sought after to improve companies but it is not something that can be bought, analytics is more of a methodology. This post will analyse the use of big data within the YouTube community and how it affects a YouTubers success and development.

 The study ‘Telemetrics: Towards Measuring Social Media Engagement with Television’ done by Woodford and associates talks about the affect social media has on television engagement; this study could arguably be applied to the YouTube community to further understand the engagement process. Social media acts as an extension to YouTube videos, giving the audience a deeper connection to their favourite YouTuber. In their abstract, Woodford and his associates state that social media is used “as a tool for measuring the success of a show” (Woodford, 2014) or in this case a YouTuber’s channel. Social media gives us a glimpse at a YouTuber’s success and development but YouTube analytics gives a detailed breakdown of an audience, allowing the producer to target their major audience by age, gender or region.

 YouTube Analytics allows the producer to target a certain audience based on age, gender or their subscriber list. This also allows companies to choose which YouTuber would be suitable to promote their brand. Analytics is understood as having the “mindset of wanting to know as much as you can and applying unbiased analytical techniques to that knowledge to drive decision making. It is the mindset of wanting to know as much about your customers buying habits as you possibly can. It is the mindset of wanting to know what your customers will want to buy and when and why.” (Johnson, 2014) YouTube analytics allows content creators to alter or create content to suit their major audience demographic. For example, if a YouTuber’s major age group were 12 to 16 year old girls that YouTuber would alter their use of explicit language, for both their target audience and for companies that wish to promote a product to that audience through that YouTuber.

   Analytics by Device Type, retrieved from Mykes Channel, 2014

Analytics allows you to see views through geography, estimated minutes watched, average view duration but also more detailed analytics. Through further inspection users can also see the devices used to see content, which allows YouTubers to alter problems that occur with certain platforms. For example, annotations to another video can’t be read by mobile platforms; therefore the YouTuber would have to place a link in the description box where that annotation would normally lead them. To conclude, Big Data and Analytics is a successful and efficient way for a producer of content to see their progression in real time, target their major audience and build their success.

  References:

  -       Johnson, David. 2014. “Thoughts on Hockey Analytics”. Hockey Analysis. Accessed May 2, 2014. http://hockeyanalysis.com/2014/04/29/thoughts-hockey-analytics/

-       Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming). “Telemetrics: Towards Measuring Social Media Engagement with Television.” Accessed May 8, 2014.

-       YouTube. 2014. “YouTube Analytics | Mykes Channel”. Accessed May 3, 2014.

[kcb206group12]

Big Data: Utopian or Orwellian?

New media has paved the way for the development of big data, allowing it to become more integrated, and invasive, than ever before. This post will look into what it is, as well as its numerous benefits and disadvantages.

  Before investigating the strengths and weaknesses of big data, the concept needs to be clearly understood. Big data is an umbrella term which refers to the information gathered by methods, in most cases digital, such as data gathering, processing, analytics and visualisation (Woodford, 2014). In terms of new media, this data is gathered from many social media sites. Woodford et al (forthcoming) even came up with the ‘Twitter Excitement Index’, which helps to determine how consistent the level of tweeting is in relation to an event. It is clear from this that new media has allowed big data to become more effective and inclusive, which leads to the question of whether this is a good thing.

  Eric Siegel (2013), talks about the ‘Clairvoyant Computer’, created by “computers automatically developing new knowledge and capabilities by furiously feeding on modern society's greatest and most potent unnatural resource: data.” In other words, computers are able to make predictions through the use of big data. The applications of this are extremely extensive. Siegel (2013) provides examples such as the fact that “Hollywood studios predict the success of a screenplay if produced” or that “the leading career-focused social network, Linkedin, predicts your job skills”. From the last example, it is clear that there are certain disadvantages, at least from the consumers’ perspective.

  Hipponen (2013) goes so far as to say that “Orwell was an optimist” in this TED talk:

He argues that the US government, in order to get access to big data, “take something that is secure and make it less secure on purpose.”  He also says that “whoever tells you that they have nothing to hide simply haven’t thought about it long enough” arguing that ”search engines know more about you than your family.” His main point is that “privacy is the building block of our democracies”, therefore companies, or in his argument, governments taking that away to use for big data take away from freedom and democracy.

  Big data in a new media landscape has allowed for a world where predictions can be made more accurately than ever before, allowing for a much higher quality of goods and services. It does seem, though, that this has come at a high cost to society – its privacy and freedom. The question is how far can it be pushed; at what point do we as a society refuse to pay such a price?

  References

Hypponen, Mikko. 2013. “How the NSA Betrayed the World’s Trust – Time to Act” [Lecture]. Retrieved from https://www.ted.com/talks/mikko_hypponen_how_the_nsa_betrayed_the_world_s_trust_time_to_act#t-1001204

  Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc. Retrieved from  https://qutvirtual3.qut.edu.au/qv/olt_material_search_p?p_unit_code=KCB206

  Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming). “Telemetrics: Towards Measuring Social Media Engagement with Television.” Retrieved from https://qutvirtual3.qut.edu.au/qv/olt_material_search_p?p_unit_code=KCB206

  Woodford, Darryl. 2014. “New Media, Big Data & Telemetrics [Lecture Notes]. Retrieved from 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_5232451_1

[kcb206group12]

The Many Layers of Twitter

Twitter is such a fast-moving platform, with the slightest bit of new information sending the site into a retweeting, @replying frenzy. However, there are many levels to this excitable site. As introduced by Bruns (2013), there are three layers of communication on Twitter: One level is labelled as the meso layer, which comprises solely of “follower-followee networks”. This level is the “default level” of communication, where users can ‘follow’ another user without that user having to reciprocate. Bruns (2013) goes on to label another layer of Twitter communication the macro level, which constitutes “hashtagged exchanges”, resulting in the formation of ad hoc publics and “amongst the most visible phenomena on Twitter, and most accessible to research”. According to Bruns (2013), this layer can operate in one of two ways: the hashtag marks relevance to a certain event (e.g. #auspol) or the hashtag is being used purely as an emotive marker (#facepalm). Finally, Bruns (2013) labels the final layer the micro level, which consists of @reply conversations between users, sometimes from one ordinary user to another or as an attempt to catch the of a celebrity or politician.

  However, when it comes to tweeting a celebrity, chances are your tweet will be lost in the fray. This of course depends on their level of fame, and also if their fan base is mainly young girls, you’re going to have an even more difficult time, as 53% of Twitter users are female and 73.7% of Twitter users are aged 15-25 (beevolve 2012).  As Baym in Bruns (2013) realises, Twitter can be both a hindrance and an asset to celebrities, especially musicians. Twitter themselves proclaim that “The fact is, Twitter provides more authenticity and creative control than any other online medium”. That’s a big claim to make, though it does seem to be true given the amount of successful musicians on Twitter. Although this more intimate connection may make interactions more frequent and genuine, it’s not always good.

When it comes to the fans, Twitter turns a part-time passion into a full-time obsession. Though most of the time followers are only vying for the attention of their favourite celebrity, at the first sign of any form of criticism directed towards their chosen celebrity, they turn volatile. A recent example would be this years Golden Globes. According to Crosley (2014), back in 2013, Tiny Fey and Amy Poehler warned Taylor Swift she’d “better steer clear” of Michael J. Fox’s son. Swift shot back with a quote, saying “There’s a special place in hell for women who don’t support other women”, which was not the best thing to say, considering Poehler and Fey’s contributions to the careers of many women. This year, Tina Fey light-heartedly congratulated Amy on her win, saying “I just want to say congratulations again to my friend Amy Poehler, I love you and there’s a special place in hell for you”.

This was too far for Swift’s fans as they all took to Twitter to fire off thousands of nasty comments towards both Tina and Amy and even setting up a change.org petition which encouraged the Hollywood Foreign Press to fire Tiny Fey and Amy Poehler. So, though Twitter is powerful and great for creating conversations about everything from celebrities to political events, it can lend itself to being used purely to antagonise or stalk celebrities. 

Images sourced from Mapping Online Publics and change.org

Reference List

beevolve. 2012. “An Exhaustive Study of Twitter Users Across the World.” Accessed May 1, 2014. http://www.beevolve.com/twitter-statistics/

Bianco, Robert. 2014. "Amy Poehler goes 'Broad'." Accessed May 1, 2014. http://www.usatoday.com/story/life/tv/2014/01/10/tca-press-tour-amy-poehler-comedy-central/4411183/

Bruns, Axel et al. 2013.  “Chapter 2: Structural Layers of Communication on Twitter” and “Chapter 17: The Perils and Pleasures of Tweeting with Fans.” In Twitter and Society, edited by Katrin Weller et al, 16-20 and 222-223.  New York: Peter Lang.

change.org. 2014. “Fire Tina Fey and Amy Poehler.” Accessed May 1, 2014. https://www.change.org/petitions/the-hollywood-foreign-press-association-fire-tina-fey-and-amy-poehler

Crosley, Hillary. 2014. “Deranged Taylor Swift Fans Attack Tina Fey and Amy Poehler on Twitter.” Accessed May 1, 2014. http://jezebel.com/deranged-taylor-swift-fans-attack-tina-fey-and-amy-poeh-1500312159

[kcb206group12]

Netflix: The power of data

In the convergence era, we’re theoretically able to have great control over how and when we consume content. But with the big data trail we leave behind thanks to new media, are we becoming more of a herd than ever before? As Siegel (2013) noted, prediction is power, and this power offers a competitive advantage to online platforms that have strongly defined audiences. Netflix is an excellent example of a company harnessing the power of data mining: for creating new shows, acquiring and pricing shows aired by other providers and suiting these shows to user tastes, here's a short insight into their work.

  Leonard (2013), argues that data mining turns viewers into mindless puppets, since Netflix uses it’s database of 29 million subscribers to find out who is watching, when they pause, when they stop and what kind of device they watch on. Instead of relying on pilot episodes like most television studios, Netflix made a $100 million gamble on House of Cards through data mining, reducing the risk through predictive analysis (Siegel 2013), which saw Netflix analyse user data on Kevin Spacey movies, political dramas, the original British series of House of Cards as well as producer David Fincher (Leonard 2013). An official competition run by Netflix also awarded $1 million to a team of scientists who improved their recommendation system (Siegel 2013. It’s an interesting comparison between these investments considering how incredibly important this overhaul of the recommendation system was to Netflix, and can further arguments about exploitations through crowdsourcing. This is important to Netflix’s business model before they run on thin margins at only $8 per month, so to continue developing, the subscriber base needs to grow. For this subscriber base to grow, people have to get value out of the package. To get this value, they need recommendations that suit them, so they continue their subscription. This is a complex form of association learning (Furnas 2012).

In a way, despite the fact that broadcasters now offer viewers more ways to catch-up on TV, the user input very much limited due to the strength of recommendations available from data mining. But it also raises questions for the future of television. Will it become a series of cheap thrills, where data makes viewing habits so open and available for exploitation that television finds a way to become even more formulaic? 

The Netflix example also offers interesting perspectives on the engagement between television and social media. In an era where live-engagement with ‘event’ television, can be seen as a prerequisite for a full viewing experience (Harrington 2013), Netflix focuses on wider buzz through its delivery strategy.

Set your clocks and put the coffee on. House of Cards goes live at 12.01 AM PST tonight. pic.twitter.com/7LmeuoSBwE

— House of Cards (@HouseofCards)

February 14, 2014

Season 2 of House of Cards went live at 12.01am American Time on Valentines Day, and despite this, still gained incredible buzz around the world. 

This creates a unique scenario where customers are happy at getting valid recommendations due to their tastes, while Netflix benefits from the data consumers (somewhat unknowingly) share. 

Reference List

Furnas, Alexander. 2012. "Everything You Wanted to Know About Data Mining but Were Afraid to Ask." Accessed May 9, 2013. http://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/. 

Harrington, Stephen. (2013). “Chapter 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. 

Leonard, Andrew. 2013. "How Netflix is Turning Viewers Into Puppets." Accessed May 9, 2013. http://www.salon.com/2013/02/01/how_netflix_is_turning_viewers_into_puppets/. 

Siegel, Eric. 2013. "Introduction - The Prediction Effect." Predictive Analytics, 1-16. Hoboken, NJ: Wiley.

[kcb206group12]

#teamgina #RHOMelbourne #bigdata #followme

Big Data is considered a ‘currency across industry’ (Woodford, 2014) and in today’s new media-focused world, is what is created when we tweet, post a Facebook status or even look up something online. A particular feature of this week’s lecture on Big Data that interested me was Twitter and its involvement in the creation of data. 

In terms of new media, Twitter is a breeding ground for the creation of data through hashtags, trending topics and multiple networks. Hashtags not only serve a purpose for sharing thoughts about which Melbourne Housewife is your favourite (#teamgina for your information), but also provide a simple way to collect data for analysis (Woodford, 2014). Beyond hashtags however, are networks that represent data through interpersonal communication about similar topics (Bruns and Moe, 2013). These networks allow groups of users who share similar interests to communicate in ‘personal publics’ (Bruns and Moe, 2013) and participate in follower-followee networks. This ‘meso’ level of the networks is only superseded by the ‘micro’ level, which is concerned with replies on Twitter (Bruns and Moe, 2013). This particular type of interaction is considered mind-bogglingly extraordinary to a fifteen-year-old girl who spends her life spamming Harry Styles with hundreds of tweets, only to have him respond and make her dreams come true. This type of personal interaction is fleeting on Twitter, but is hardly a possibility on other social networking sites such as Facebook.

  Speaking from experience (six out of eight of the Real Housewives of Melbourne have answered me AND Gina and Chyka follow me, big news I know), it does make you feel personally connected to that other person, whether or not it’s only for a few seconds. That something about what you said made them respond to you, not the hundreds of other people attempting to get a response at the same time. This type of interpersonal communication serves a further purpose rather than simply tweeting under a hashtag, or following your idol on Twitter in the hope that one day, they would follow back (language warning for the video). 

  Until recently, the notion of tweeting under a hashtag has been unique to Twitter (until Facebook decided to job on the bandwagon). Still, it’s not often you see your Facebook news feed littered with comments from every friend about the same show. Unless it’s the weather report, in which case all of my Facebook friends are apparently meteorologists. This idea of discussing and sharing opinions about a particular television show or Hollywood awards event under a particular hashtag allows us to understand how we interact with each other, both nationally and internationally. Twitter has allowed us to communicate without barriers, share opinions with the world and connect with the people we admire most, all while creating big data to be nit-picked and analysed by bigger powers.

Should we be worried? Possibly, but unless we’re having a Too-Much-Information Tuesday or Follow-Me-Around-Friday moment on Twitter, what’s the problem with picking which Housewife’s side you’re on? It may provide an approximation of which housewife has a larger following, and could even encourage a retweet if you mention them, but that’s about it. Rock on, #teamgina.

   Reference List

  Bruns, A & Hallvard, M. 2013. “2. Structural Layers of Communication on Twitter.” In Twitter and Society: An Introduction. Accessed May 8, 2014. http://mappingonlinepublics.net/2013/11/04/announcing-twitter-and-society/

  Woodford, Darryl. 2014. “New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford)”. Accessed May 8, 2014. http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

[kcb206group12]

Social media and predictive consumption.

As social media continues to pervade (what feels like) every facet of our lives, human behaviour becomes monitored, recordable.  The plotting of users’ social media activity allows Facebook, and subsequent marketing firms and advertising strategies to monitor past and predict future habits of consumption.  As a result, audiences are now viewed not as a homogeny, but rather a highly fragmented collection of individuals, to whom advertising is specifically tailored.  As Siegel (2013) puts it, organisations are endowed with “an entirely new form of competitive armament.”  The ethics of this practice have been scrutinised at length, insofar as consumers complaining that they (though legally consenting - by accepting the applicable social media platform’s privacy agreement for use) haven’t consciously consented to the relaying of their online activity.

Harrington (2013) characterises the modern era of television as “post-broadcast” and “post-network”, in that television consumption is no longer hindered by physical televisions and timeslots.  Personally I am a proponent of this trend.  In my day to day life, very little of what I watch is viewed on a television; increasingly I am drawn to services like ABC’s iView and SBS’s On Demand service for their convenience.

Similarly, social interaction has turned to the online.  More than that though, the construction of identity is done online.  The personalisation of Tumblr accounts, the ‘liking’ of Facebook pages that reflect tastes and empathies, the sharing of links to evince political ideology and so on are all palpable examples of social media users’ construction of identity online.  I’m guilty of engaging in this practice: I’ve ‘liked’ and shared many pages that I thought to be interesting.

I soon began to notice the adverts on the sidebar of my Facebook newsfeed becoming strangely relevant to me.  For example, as a follower of music, I received adverts for offshore rare record traders who stocked artists I had liked.  This trend continued though, and soon the advertising was recommending similar artists, clothing for those artists, and tickets for upcoming gigs.  Most interestingly though, one day I received an ad for discounted protein supplements (to mind, I’d neither followed links to supplement pages or even links vaguely pertaining to fitness or muscle building).  This raised the question in my mind though: was I being exposed to this ad simply because I am a male?; or does my omission of fitness pages suggest (to the entity plotting my social media activity) that I am not maintaining my fitness, and by extension, would appreciate protein supplements? 

If the latter is so, Siegel (2013) proffers this would be tangible evidence of social media’s role not only as a predictor of consumer behaviour, but as a veritable shaper of consumption. 

It seems to me that consumers must strive to protect themselves against such sophisticated advertising strategies.  At the very least, whether or not social media users are comfortable with the ethics of their online activity being tracked, they develop an awareness of when they are being manipulated. 

References:

Harrington, S. (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.