QUT KCB206 2014 Tutorial Group 2

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Big Data & Telemetrics

Finally, we can talk about one of my favourite things to do to relax. Watch TV!

Okay, so I’m sure that there are a great many people out there who would say that the fact that there are huge companies collecting our data and analyzing it and doing who knows what else to it, let’s be honest.. If it makes for a better TV show are how much do we actually care?

I guess, it’s not really as big a deal for me personally. You see I’m not on twitter and I won’t distract myself by going into detail on that I actually just don’t see the appeal but on the other hand, those times where I do get sucked into watching what ever reality TV show is showing for the season (Okay, I actually love The Voice and can’t wait to watch the blind auditions) I do enjoy reading those witty remarks all those twitter uses share with us! Good work, love your stuff!

But what does this have to do with the title, “Big Data”? Well think about every piece of information you have produced; facebook post, movies recommendations, purchase history, emails, tweets and so on. It all get’s collected as one big piece of data which scarily enough grows “by an estimated 2.5 quintillion bytes per day”. (Siegel 2013) (If you’re not up to scratch on your quintillion, it’s a lot!) So basically, all of this data that is being accumulated by a great number of machines “unleashes the power of this exploding resource.” (Siegel 2013) By use of this data companies, analysts or whoever it is sifting through this information is able to learn a great many things about what drives people to make a decision or take a certain action.

So let’s take this back to TV. For the purposes of trying to help myself get around all these concepts, let me attempt to use myself as part of an example. In a research paper produced by Woodforrd, Prowd and Brus looking at social media engagement with television they have some very interesting figures and finds to show. Let’s try using an example!

You see I LOVE the TV show Once Upon A Time! If you haven’t watched it, I will admit it’s a little slow to get into but once you’re hooked, it’s the best! And I continually feel blown away and a little envious of the amazing talent and creativity of the writers of the show. While unfortunately, we don’t have access to American Television, fortunately there are a great many sites I am able to access to watch my favourite shows, which I never use! Anyway, one of the things I have found with a couple of shows I keep up with that are aired on the American ABC channel is that they pretty much air each season in two parts. I’ve always found this really frustrating and to be honest have never given a great thought as to why this might occur. Important American events maybe, who knows?

Well, I’ve finally figured it out! Based on this document I referred to before they have found that when a TV series is split it to two parts rather then running continuously over the course of however many weeks there is a variance in the number of viewers. When there is a break, they are able to draw back viewers they have potentially lost with their “mid-season return” and have more viewers over the course of a season when it’s show split due to time in which they can advertise and build back up excitement within viewers.

How clever for them but still annoying for me!

Still, I shouldn’t complain. You see, you know all those twitter users I spoke about before. Well, because of this information collection that keeps on occurring, over the past few years TV show writers have been able to connect more with audiences and tailor their TV shows more to how viewers are responding. So that really annoying character in that show you love, depending on response looks like they could stay or go! Anything could happen!

To conclude, I do feel very TV is concerned it is a kind of cool to be able to use data in this way but I shouldn’t be too carefree as always when it comes to collecting information about people, there can be a very fine line not to be crossed!

    References 

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.

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

  Images

http://www.pinterest.com/pin/83527768060945481/ Retrieved 13th May 2014

https://www.google.com.au/search?q=twitter+memes&es_sm=119&tbm=isch&tbo=u&source=univ&sa=X&ei=E6x4U49Pyu6SBe2CgagE&ved=0CCkQsAQ&biw=1172&bih=568#facrc=_&imgdii=_&imgrc=YxdP4ddRVVev8M%253A%3BhHb87EUeZKRYIM%3Bhttp%253A%252F%252Fmlbfancave.mlb.com%252Fassets%252Fimages%252Fcustom%252FChipper_meme_4_i52ytr7o.png%3Bhttp%253A%252F%252Fmlbfancave.mlb.com%252Ffancave%252Fblog%252Farticle.jsp%253Fcontent%253Darticle%2526content_id%253D35953864%3B539%3B363  Retrieved 13th May 2014

http://www.pinterest.com/pin/129900770476707803/ Retrieved 13th May 2014

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The commonality between this week’s 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 don’t mean anything to...

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I foolishly posted final blog to my personal account on Sunday night and so here is a reblog:

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Week 10 - Will books survive in this big data world?

“Our world is drowning in data”. (The Dark Side of Big Data, 2013) Oh, really? And still everybody continue to use, crate and upload new stuff on Internet?! When I hear the word drowning does it remind me of something awful, so drowning is maybe not the right or most descriptive word to connect data with since most people do not think about something awful when they hear about how much data there is online. Another way to describe how much data there is could be by saying that data is taking over our world. 

An example is by comparing “normal” printed books with online books. As long as people have had the capability to write, has there been created new books and reading materials continuously. The only difference today is that many authors choose to publish their work online instead of in printed format. It is hard to find statistics on how many books that gets published online and how many in book format. But it is for fact that when a book is published online is it a part of our big data allowance. As long as people want both, will it still be available, but the thought that online books will take over for regular books is in the minds of some people. Gomez, (2008) write that people have some specials feeling for the old, good printed book, which can be touched with our fingertips and be laid on our bedside tables.  The author writes that the printed book will not be replaced. “Instead, the printed word is being supplanted by any number of other online attractions:” (Gomez, 2008 p.15)

At the same time as people love the good old book, do they love the idea that everything is easily available. I overheard an episode where a little boy came up with the idea that he could just Google it, since his parents did not have the answer he was looking for. Do we want all of our questions to be that easily answered? I don’t think so. “Meanwhile, the needs of an entire generation of ´Digital Natives´- kids who have grown up with the Internet, and are accustomed to the entire world being only a mouseclick away – are going unanswered by traditional print media like books, magazines and newspapers. For this generation – which Googles rather than going to the library – print seems expensive, a bore and a waste of time. What can a book give them that a blog or website can’t?” (Gomez, 2008 p.4)

How can we find what we are looking for when everything we can imagine is available on Internet? Is it a jungle of data materials only a mouseclick away. When I prepared my five minutes review last week for this topic I got my eyes up for how much data there is floating around. It is especially frightening to think about how fast everything spreads and grows. The overview on the screenshot I took of the YouTube movie made me think about what kind of units of measurement we are used to use today, and what type of measurement we will be using in ten years for example. It will grow tremendously and fast. Imagine if all data also had to be in a library, then I would got dizzy from the big selection. (Big data, 2013)

  References:

Gomez, J. (2008). Print is dead: Books in our digital age. Palgrave Macmillan.

  The Economist. 2012. The Dark Side of Big Data. YouTube video, posted 26. June. Accessed 3. May, 2014.

  The Economist. 2013. Big data. YouTube video, posted 24. Feb. Accessed 3. May, 2014.

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The Big Data Era

The web, designed to share information from one to another, creating and generating a wealth of data for those involved to share. Tim Smith explains in the video above the beginnings of 'big data' and how the simple process of sharing scientific research data between Europe and America showed the world the importance of collective information. Big data encompasses most all aspects of life today, and surprisingly the majority of people aren't aware of it. Thanks to Julian Assange, WikiLeaks and Edward Snowden, big data is now at the forefront of global discussions; not so much what it is, but how it can be used.

Every computing device stores data. I myself having built and maintained many computers know that it is quite difficult to remove data off a harddrive. Big data is the great 'ocean' of information created each second that is captured into databases worldwide, so imaginably large in size it is hard to comprehend how and where the data is all stored. I believe that it one of the primary issues of big data for the average citizen, we are unsure what data is being stored (assume all of it is) and where the data is being stored and seen and possible used by the 'unknown' government/corporate 'suits'.

This week I specifically looked into how big data is used in a more light hearted manner; social media and television. Big data and personal data abuse as an issue is important, but what I found as an immediate irritation was how social media is becoming part of the television experience. Stephen Harringtons' article 'Tweeting about the Telly' highlights the 'hashtag phenomena' on twitter and how it is becoming a 'virtual lounge room' for viewers. The questions raised by this article include 'collection of data' and using that feedback data to tailor television programs to suit an audience. Personally I am getting sick of the social media invasion on my screen as I'm not part of this growing trend, wherein for instance program twitter comments are shown while the show is on. Honestly I don't care about other peoples reactions and I'd much prefer to see how the show programmers work their magic instead of pandering to what is 'trending'. Reality television should be that, and its weird to now assume that there is a lot of things that happen behind the scenes to make a 'reality' show what it is once it hits screens.

I am excited however for further integration of social media/disussion for live audience type shows where the show itself is somewhat dependent on audience interaction. I reckon that would be great to be part of as many people are unable to attend live recordings of their favorite shows. A number of concerns with this is (especially for Q&A/political audience shows) the censoring and bias (pick and choose questions) that are part of these programs. Its the producers decision how they wish to position their program but I think there is a lot of audience interactions and discussions currently censored that could ironically elevate these shows to new audiences and content explored.

References:

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

Smith, T. (2013). Big Data Ted Talk. http://ed.ted.com/lessons/exploration-on-the-big-data-frontier-tim-smith#review

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Big data and how it's taking over the world

Big data – It’s everywhere and affects every single one of us. Like a great deal of individuals I never really considered the impact of big data, I didn’t even consider the fact that I was a contributor towards it. The fact is every single little thing you do from googling a phrase to buying a pair of shoes off eBay to liking someone’s Instagram photo generates data. “Every medical procedure, credit application, facebook post, movie recommendation, fraudulent act, spammy e-mail, and purchase of any kind-each positive or negative outcome, each successful or failed sales call, each incident, event, and transaction-is encoded as data and warehoused” (Siegel, p.g.3). It was estimated in 2011 that there was approximately 1.8 zettabytes of data created and replicated (The Economist, 2012).

Siegel (2013, 4) states that, “As data piles up we have a genuine gold rush. The data isn’t the gold; data in its raw form is boring crud. The gold is what’s discovered there in.” We can measure, visualize and research so much more than we ever could. Big data is a currency across industry, as well as the sciences and social sciences. Business intelligence and data market feed predictive analytics including online behavioral data (Woodford, 2014). Big data has proposed the opportunity to understand customers and people and push products and ideas at them more effectively. Ebay is a great example of this. Every time you search for an item on eBay is retains that data so that the next time you enter the website it will display a home screen of items it predicts you might be interested in. I often search for brand new clothing under brand names that are normally out of my budget if bought from the actual clothing store. I opened eBay to test this theory and sure enough displayed on the home screen is woman’s clothing from brands I have previously searched.

Big data has the potential to be very useful however it can also be extremely dangerous. With more and more companies, corporations and individuals being able to easily access our personal information and interests privacy is fast becoming a thing of the past. There are 2464 words in the average Internet privacy policy and 1337 words in the declaration of independence (The Economist, 2012). When you research into these privacy settings its alarming how many people have access to your information even if you think you have set your profile or information to private. I believe big data can be very useful in certain circumstances however it worries me how long it will be until privacy is a thing of the past.

  By Courtney Day

  Reference List:

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

The Economist. 2012. The Dark Side of Big Data. YouTube video, posted 26 June. Accessed 11 May, 2014.

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

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The Community is Being Watched

Jack Roberts n8848301

Big data is both a blessing and now a curse on society. It helps us log and exploit the media habits of media consumers to an advertising and marketing perspective. On the other hand, it can make it easier for governments to potentially pervert democracy. Personally my interest lies in the advertising and marketing so I will be somewhat examining the effect it has on the marketing of Television shows for one and examining big data in general. I think it’s important to note that this is an issue that effects everyone using new media, be it twitter to comment on sport or the latest series of The Walking dead or people commenting on the political atmosphere of the time.

Big data has a few things to thank for its existence, mainly the hashtag which allows for organisations to easily view what conversations are being had about a particular topic that uses a hashtag. The hash tag is available on twitter, Facebook and Instagram to name a few. It has grown partially from a need to predict what consumers want and what to offer them, and to predict what circumstances consumers will be in, in the future thus providing an entry for a product into their lives. Prediction is power (Siegel, 2013), a very good motto for a firm to go by when approaching audiences, as prediction greatly reduces the amount of risk an asset may face (Siegel, 2013).

We can therefore use predictive technology and big data to predict when consumers may want something, we can also find out how many individual users are engaged in something and from that using twitter for example we can also map out their connections to other products using technology such as thematic products and using this we can target advertising based on their own personal interests and interests of people with similar followership and interests in products and solutions. This has good and bad points, for example one point that can be seen as possibly bad and potential good, depending on which way you look at it is the fact that retailers such as Target in America can predict if a woman is pregnant using data from specific customers purchasing habits, from there they can target baby related advertising to that customer (Hill, 2012).

Big data has ethical concerns too, namely the example where predictive technology predicted a teenage girl’s pregnancy before her father had found out (Hill, 2012) and there are similar stories popping up all the time. Marketers, advertisers and communication experts alike are all being faced with concerns about what they ethically can and can’t do. Big data helps marketers greatly increase the effectiveness and efficiency of funds allocated to them, it can be used for great good within the marketing and advertising community but could also possibly interfere with other aspects of life such as democracy within our society.

References

Hill, K. (2012, Feb 16). How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did. Retrieved from Forbes: http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

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

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Big Data, Should we be Excited or Afraid?

“Big data is a buzzword, or a catch-phrase, used to describe a massive volume of both structured and unstructured data that is so large it’s difficult to process using traditional database and software techniques (Webopedia, 2014). It’s scary to think that no matter what we do, computers are able to store information about us and use it without our knowledge. Data isn’t the most exciting thing, I know I didn’t even give it a second thought until last weeks lecture. However according to Siegel (2013) data provides endless opportunities for us to learn, computers process this information, which can help us to understand “what drives people and the actions they take – what makes us tick and how the world works”. Social media is one of the most useful tools in gathering big data, “the explosive growth of social media is one of the reasons that 90% of all data in the world has been generated in the last two years alone” (Uganec, 2013). We share endless amounts of personal information on these websites that could easily be used against us. So what exactly does big data mean for the future? Big data can be both scary and exciting.

The scariest idea of big data is predictive analysis “a commonplace and affects everyone, every day. It impacts your experiences in undetectable ways as you drive, shop, study, vote, see the doctor, communicate, watch TV, earn, borrow, or even steal” (Siegel, 2013). Through running this data through sophisticated algorithms organisations are able to use this information to “predict how you will respond to various marketing offers” (Weisbaum, 2014). I often purchase clothes online, just the other day I was looking at a dress on The Iconic online store now it keeps popping up in advertisements all over my browser. I probably wouldn’t of thought twice about the dress but now I want to buy it, because I have constant reminders of how much I like it.

  Through my research for this blog post I came across a YouTube video where photographer Rick Smolan discusses what big data says about you and what this information is used for. He used an example of how the Gates Foundation is working to eradicate Polio in Nigeria, through the use of satellites and GPS they located villages that no body knew existed. Through the use of new media technologies and big data “the Gates Foundation has helped implement a satellite mapping technique, to ensure house-to-house vaccinators use aren’t missing any villages or settlements” (Fox, 2013). How can we not be impressed with what big data can achieve when we hear things like that?

I'd love to hear everyone's opinions on big data, what do you think should we be excited or afraid?

References

Fox, Zoe. 2013. “Bill Gates: Satellites and GPS Are Fighting Polio.” Mashable, April 25. Accessed May 10, 2014. http://mashable.com/2013/04/24/bill-gates-vaccines-interview/

Funk-e Studios. 2013. “What is Big Data and how does it work?” YouTube video, posted November 12, 2013. Accessed May 10, 2014. http://youtu.be/TzxmjbL-i4Y 

Siegel, Eric. 2013. “Introduction: The Prediction Effect.” In Predictive analytics: the power to predict who will click, buy, lie or die, 1 – 16. Hoboken: Wiley Publishing. Accessed https://qutvirtual3.qut.edu.au/qv/olt_material_search_p?p_unit_code=KCB206

THNKR. 2013. “What Big Data Says About You.” YouTube video, posted April 12. Accessed May 10, 2014. http://youtu.be/fc1zBNC9wNY

Uganec, Cameron. 2013. “Social Media, Big Data and Visualization.” Hootsuite n.d. Accessed May 10, 2014. http://blog.hootsuite.com/social-media-big-data/

Webopedia. 2014. “big data.” Accessed May 10, 2014. http://www.webopedia.com/TERM/B/big_data.html

Weisbaum, Herb. 2014. “Big data knows you’re pregnant (and that’s not all).”  CNBC, April 9. Accessed May 10, 2014. http://www.cnbc.com/id/101566276

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BIG DATA

by Latiesha Dunbar

On Monday I presented my five minute review on data. What I found intriguing was how much data is being collected and how it's being collected. As I mentioned in my presentation 

I’m sure all of you have seen a tweet by people who is also watching the show on your television in this section below, regarding a comment about the show. As Woodford stated "social media is increasingly playing an important role in television; playing a key role in the promotion of upcoming shows, being utilized for on-air interactions with the audience across a variety of genre, and also as a tool for measuring the success of a show, at least with the audience segment which social media represents" (Woodford, 2014). 

All of those hundreds of tweets being posted per minute is big data, which is a popular term used to "describe the exponential growth and availability of data, both structured and unstructured" (SAS, 2013). 

Big data may be as important to business and society as the internet has become. It's because continuing data may lead to more accurate analyses. Which leads into a confident decision making however, It's what you do with the data that significantly counts. What we hope is for organisations will be able to take data from any source, relevant data and to find answers to enable cost reductions, time reductions, new product development, and smarter business decision making.

 Everyday, we create 2.5 quintillion bytes of data. Majority of that 90% of the data in the world today, has been created in the last two years alone. This data comes from places you wouldn't even think of from" sensors used to gather climate information, posts to social media sites, digital pictures and videos posted online, transaction records of online purchases, and from cell phone GPS signals to name a few. This data is big data. Big data spans three dimensions Variety, Velocity and Volume" (IBM, 2014).

Knowing all about where the data collection is being collected from, do we necessarily know what’s being collected from our personal pages e.g Facebook account and emails? Sharing is fun and we do it everyday! Whether we are on Facebook uploading photos, liking and commenting on pages. However, some people tend to see big data as a dark side. Because over a million people have access to your information under Facebook’s default privacy settings, 72 percent of privacy polices allow third parties to track internet activity, over a million emails intercepted and stored by the National Security Agency every day. Its all taken into collection and we don’t even know it. 

Personally I don’t mind big data being collected as they are using it in away how can we improve? This audience is reacting positively to this or we are getting no response to that and making it better for the customer or audiences. Its getting all possible information from human response. 

Reference:

What is Big Data? | SAS. 2014. What is Big Data? | SAS.  http://www.sas.com/en_us/insights/big-data/what-is-big-data.html. [Accessed 1 May 2014].

2014. IBM - Bringing big data to the enterprise - What is big data? - Australia. : http://www-01.ibm.com/software/au/data/bigdata/. [Accessed 1 May 2014].

Woodford, Prowd and Bruns - Telemetrics Towards Measuring Social Media Engagement with Television : http://blackboard.qut.edu.au

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Facebook thinks I want a orbera intra gastric balloon.. but why?

In this weeks instalment of ‘the internet, self and beyond’ I learned that computers know more about what I want than I do, or at least they attempt to. They attempt to predict what I want before I actually know I want something. Then there’s the people sitting in dimly lit rooms (I’m assuming they’re dimly lit because hollywood has conditioned me to believe scary things happen in dark rooms) evaluating twitter’s ‘excitement index’ to determine which football game would’ve been more exciting to watch.

Week 9’s lecture got me thinking - what if my life was analysed by those people in ambient rooms? My choices on my interests - what I will buy, who I’ll vote for, what I’ll tweet at, predicted based on my previous activity. Oh wait. That actually happens already. Siegel puts it as ‘Predictive Analytics” - “technology that learns from experience to predict the future behaviour of individuals.” (Siegel, 2013) These ads on my face book newsfeed, tailored to what I may have searched for previously -  

- What are you implying, Mark Zuckerburg?

And that is why Siegel also states that, “The future is unknown, and uncertainty is the only thing about which we're certain.” Which is why, whilst I may have googled ‘Juice Cleanse’ and ‘Paleo Diet’ earlier today, that certainly doesn’t mean I’m interested in an ‘orbera intra gastric balloon.’ 

Darryl Woodford explained in his lecture that every user of twitter, facebook, tumblr etc. is aware that whatever they post goes into the public sphere. But a question of ethics - is it fair that a company, free of charge or any obligation to let us know, takes all this information on what we’re doing - and using it for their own personal & profitable gain? 

  The dark side of big data posted these two statistics, also stating that it takes an average of 10 minutes to read an internet privacy policy. “Every business sector now collects data of one form or another, and the future marketplace will have even more computing power at their fingertips to mine customer behaviour” - Martin 2014. And all this information, one way or another is being stored in the hopes that one day they will be able to use it to analyse how to grow their business, or hone into a specific target market. 

And all this actually got me thinking on Week 4’s topic of ‘cloud labor’, with the slide show describing the negatives of cloud labor as - ‘exploitation, no pay and surveillance’ to do small tasks a big company, such as recaptcha’s gig of having us decipher words. And isn’t this idea of companies spying on our every click the same think, except the ‘labor’ is us just being ourselves? Is this exploitation? 

Taking our data in order to predict what we might click on in the future to me is two sides of a coin, big businesses gain vital knowledge such as - “Vegetarians miss fewer flights.” - (Siegel 2013), or what I may actually like to watch, purchase, vote for, go to. - they advertise it to me and through their prediction I come across things I wanted that I didn’t realise I actually wanted.

Martin, Ellen Rooney. 2014. “EMCVoice: The Ethics Of Big Data” Forbes Magazine, March 27. Accessed May 8, 2014. http://www.forbes.com/sites/emc/2014/03/27/the-ethics-of-big-data/

Siegel, Eric. 2013. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Hoboken: Wiley Publishing.

The Dark Side of Big Data. 2012. Youtube video, posted June 26th. Accessed May 10th 2014. https://www.youtube.com/watch?v=raJOkguPrH4

Woodford, Darryl. 2014. “KCB206 Internet, self and beyond: Week 9 lecture notes. Accessed May 6, 2014. http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

Sauter, Theresa. 2014. “KCB206 Internet, Self any Beyond: Week 4 Lecture Slides” 

Week 4 - New Media Enterprises.pptx (4.326 MB)

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The Notorious B.I.G Data (that's a horrible reference)

Nothing pains me more than the thought of compiling audience and market research then shifting through it to find the information you need. I’m talking small scale data here, like 100 surveys for an assignment. The strenuous task of sorting through data that goes into scales such as “meso” and “macro”, I’m not quite sure how much that is, I don’t think I want to know. Big Data is worth big dollars and is now crucial part of advertising and marketing, with Netflix offering $1 million to scientists who can design a program to recommend tv shows to it’s users based on what they have watched (Seigal 2013).  If a company is willing to offer a large sum of money for their Big Data to be sorted for advertisement, Big Data must be important.

  Every tweet, status, instagram and post you make has the potential to be worth something to someone. Companies are now trying to predict whether movies and/or tv shows will be successful to who will an election to who your soul mate on that dating site is.  Barack Obamas 2012 re-election campaign found out what way voters would prefer to be contacted by whether it would be fliers, tv advertisement or door knockers, this way of campaigning was deemed successful given his win (Siegal 2013), maybe they also just happened to like his campaign promises and political views more than the other guy, we will never know.

Will you trust e-harmony to find you the love of your life based on their predictions?

  “Accurate prediction is generally not possible. The weather is predicted with only about 50 percent accuracy, and it doesn't get easier predicting the behavior of humans, be they patients, customers, or criminals” (Seigal 2013). Given the chance of accuracy, should TV shows really be catered around what we choose to tweet?  I personally think TV shows and movies shouldn’t be based off data collected from tweets. You can’t please everyone, for example when a character dies, will a predictable TV show still be as enjoyable? As for products that companies are producing and selling, big data can be resourceful for consumer feedback and help improve faults in any products for the better (I suppose you still wouldn’t be able to please everyone just like with TV show’s) and help give a company an advantage over their competitor.

  In new media big data is used to suggest advertisement to you, like those recommended youtube videos (I cry when my friend’s see my recommended list filled with guilty pleasure songs) to Facebook ad’s to who to follow, with advertisement and recommendations being tailored from predictive analytics it allows for a higher chance of reaching your target audience.

  Overall Big Data is big business and is changing the content that is being recommended to us, what advertisement we see and what we may view on television. As companies find ways to process big data they will be able to know more easily what their target audience and market want more easily.  

  References

Siegel, Eric. 2013. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Hoboken: Wiley Publishing.

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Lets Begin to Think Like Algorithms

  Up until this week, the concept of big data was unfamiliar to me. I understood that there was this global access to information about human response and innovation, but I was not aware of how this information was captured. The 5-minute reviews and the reading from Siegel (2013) were what stemmed my interest on the concept of confidentiality and big data. Throughout this whole unit, anonymity has been a key contributor to the evolution of new media. In simplest terms, big data can be defined as being the collection of sets of data that are large and complex (Madrigal, 2014). The process then works towards analysing these data sets to form relationships. In order to drive better decisions, Siegel (2013) suggests that data markets, including social media, feed predictive analytics so that technology learns from experience to predict the future behaviour of individuals online and in real time.

I wanted to go to the central point where big data is being collected on my actions, that being social media. More often than not, social media companies constantly collect data on their users to provide customized experiences and target their advertisements. While some users are in favour of this, some fear that this persistent tracking of their social relationships is an invasion of privacy. Those of us who don’t want to be a part of this process are encouraged to think like algorithms in the bid to prevent being tracked by one.

Some of the world’s largest social networks are storing huge amounts of data that could essentially disclose crucial information about consumers connecting and sharing in these outlets. But, are these social networks doing a better job of showing users what they want to see, rather than content and ads that they would rather not waste their time on? For those users who would want their personal bubble of privacy maintained, not all is lost.

In an attempt to blind big data analyst’s, users are engaging in a set of practices that effectively alter their visibility to machine algorithms. These are essentially practices that allow users to communicate, without being computered. And all you have to do as a user is think like an algorithm.

Users of social media will realise that there is the option to tag someone in a post, however, to think like an algorithm, you would endeavour to implement “subtweeting” into your social media scheme. Subtweeting is the practice of talking about someone without referencing them explicitly. Tagging people on Facebook is a very popular practice of which machines can detect the person’s identity, similarly when it comes to tagging photos. I’m sure seeing that Facebook recognise the people in the photos you upload is a little daunting to say the least. 

Madrigal (2014) suggests that users can also escape the algorithmic gaze by screenshotting text instead of linking to a person directly. This will allow for conversations that are silent or invisible to the machine, but work perfectly for humans. Madrigal believes that to a machine, “a conversation in screenshots seems like people talking about a photography”. Subtweeting and screenshotting are all about creating invisibility to the big data collectors.

These extreme benefits of big data are tempered by concerns over privacy and data protection. So as users, it's on our part to be smart, and think like an algorithm in order too, as Darryl Woodford (2014) suggested, understand the way big data works in this age of network intelligence and collaboration.

Reference List:

Madrigal, A. (2014). Behind the Machine’s Back: How Social Media Users Avoid Getting Turned Into Big Data. Available: http://www.theatlantic.com/technology/archive/2014/04/behind-the-machines-back-how-social-media-users-avoid-getting-turned-into-big-data/360416/

Siegel. E. (2013). Introduction – The Prediction Effect. In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc. (Available on CMD)

Woodford, D, Katie Prowd and Axel Bruns. (2014). Telemetircs: Towards Measuring Social Media Engagement with Television

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Big Data and Predictive Analytics

How many of you before purchasing a product made a quick search through social media to view other people’s feedback and opinions about the product? What about before starting a TV show or watching a movie? It’s definitely something I do often and rely on greatly when trying to find or share information.

To begin with Big data according to Webopedia (2013) is “a buzzword, or catch-phrase, used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques.”

TV shows like Big Brother and X-factor rely heavily on collecting data from social media platforms. The promotion of tweets and Facebook use, the encouragement to discuss on social media and allowing people to vote, like and share- are all forms of free promotion for the company (Woodford, 2014). The more people share it, the more it will become popular, which in returns allows producers to collect a more accurate lot of data and opinions on their show, for further improvement.

Twitter for example is one of the most popular social media platforms that are used to collect big data because of its ease. The use of hashtags are a way of coordinating discussion around a topic and is relatively easy to capture and analyse (Woodford, 2014).

Business intelligence & ‘data markets’ (including social media and online behavioural data) feed Predictive Analytics (Woodford, 2014). Siegel (2013) discusses the dynamics of Predictive Analytics, and how it is based on big data. Predictive analytics is “technology that learns from experience (data) to predict the future behaviour of individuals in order to drive better decisions.” (Siegel, 2013)

 Walmart is famous for using analytics to maximise the profits of their retailer operations, by knowing their customers buying habits and what they want (Woodford, 2014).

However, the accuracy and efficiency of prediction is questioned, since there is uncertainty in the unknown future (Siegel, 2013). “We can't confidently predict that any one individual customer will respond. Rather, the value is derived from identifying a group of people who-in aggregate- will tend to behave in a certain way… Predicting better than pure guesswork, even if not accurately, delivers real value.”

REFERENCES:

Siegel, Eric. 2013. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.Hoboken: Wiley Publishing.

Webopedia. (n.d.). Big Data. Retrieved from http://www.webopedia.com/TERM/B/big_data.html

Woodford, Daryl. 2014. “KCB206: Internet, Self and Beyond – Week 9 Lecture Notes.” Accessed May 10, 2014.https://lecturecapture.qut.edu.au/ess/lti/v1/launch/BLACKBOARD/90e0f566-3969-40ff-8600-e7cac2c2e219

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The Scary World We Live In

looming in the background of all conversation about New media, there is always the question about the absence of privacy. I have never paid much heed to this growing concerns, as I have always addressed it in the context of my own personal information. Considering my lack of criminal intent, I never placed that much worry on people using my personal information as a weapon against me. I did not fear the internet suggested purchases, more so I relished in a technological experience catered for enjoyment. However, it was not until I attended and read this week's content about big data and telemetric that I truly started to fear to technology. By mainly confining in Eric Siegel's article, this perspective emerged by separating humanity away from prediction software. The readings made me marvel at technological prowess, although my fear is not directly aimed at what prediction analysis can achieve rather our inability to stop it.

All creatures on earth that have an innate sense of survival were bequeath by natural selection to have a key ability maintaining their proliferation (Biology 18,2010). Humanity though, is an obscurity as we were not gifted with a physical trait like heightened vision or a second stomach. Rather we were solely given the gift of spectacular intelligence. Our assets for survival is our ability to understand our environment and make decisions out of logic and unlike animals not out of necessity.  In a sense, my fear for what Eric Siegel describes as the clairvoyant computer, is that we are creating machines with the ability that made us the dominant species on the globe. I am not being so childish to fear some kind of robot apocalypse. Prediction is power. Big business secures a killer competitive stronghold predicting the future destiny and value of individual assets. The fear is that we will eventually create digital tools that are superior to humanity, as it accurately maps all human patterns based on  how we act in our environment. The fear is that "machine learning -computers automatically developing new knowledge and capabilities by furiously feeding on modern society's greatest and most potent unnatural resource: data" (Siegel, 2013). The shocking statistic that every day we create 2.5 quintillion bytes of data is even more alarming considering that 90% of all data was created in the past three years (SINTEF, 2013). As each generation become more familiar and trusting of technology, organisations will generally have a much better understanding of profiling people. As this week's guest lecture Darrly Woodford identified, television companies already monitor what's trending through social media. Business intelligence & ‘data markets’ (including social media & online behavioral data) feed Predictive Analytics so that technology learns from experience (data) to predict the future

behaviour of individuals in order to drive better decisions(Siegel, 2013). This type of monitoring is not passive but active. As we become a culture more confident in digitally tracking our life the clairvoyant computer is will always be humanities new shadow. I question have we really been empowered by social media or the complete opposite.

  Woodford, Darryl. 2014. KCB206 Internet, Self and Beyond: Week 9 Lecture Notes.http://blackboard.qut.edu.au (accessed May 9, 2014)

Siegel, Eric. 2013. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.Hoboken: Wiley Publishing. accessed May 9, 2014)

SINTEF, 2013. ' Big Data, for better or worse: 90% of world's data generated over last two years accessed May 10, 2014

Evolution
 Lecture
2 2010. "Natural Selection (Darwin-Wallace)"accessed May 10, 2014)http://ncse.com/files/pub/evolution/Evolution--Futuyma--chap11.pdf

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TV and New Media

With 645,750,000 active twitter users as of January this year, there is no wonder so many companies are trying to take advantage of this media outlet (Statistic Brain 2014). Scholars have established the vital role that this type of social media plays in relation to television and the promotion of particular programs (Harrington 2013; Woodford, Prowd and Bruns).

From previous blogposts, I have established individuals want for collaboration and interaction with others on social media. This strive for interactivity is one of the reasons why it can be seen that social media enhances the television experience (Harrington 2013). The new medium being used demonstrates a culmination of the old and the new. Television is the traditional way for individuals to access entertainment and information, social media and the internet is the new and contemporary for doing so. By encouraging the use of social media to support television viewing, the television networks are trying to bring this traditional way of communication into the current age to enhance its relevance and audience engagement. In the reading Harrington stated that 60% of people now use social media whilst watching television, the ‘virtual lounge room’ (2013). A virtual lounge room is “an online space where an audience can commune and centrally share the television experience” (Harrington 2013). This online space allows viewers to share their opinions of a program with others whilst viewing.  One way that television networks use social media is through twitter publics. Twitter publics are when a discussion around a topic or television program is coordinated using hashtags. The use of hashtags makes it easier for the network to analyse the popularity, engagement and ultimately the success of the show online (Woodford 2014).

The Voice is an example of a television program that has embraced the ever-increasing attachment to social media. The show is an international concept, which was brought to Australia in 2012. It has been described by some as a “social TV experience” (Messent 2012) with multiple platforms of media being embraced. In promotion of the show and audience connectivity, the program communicates via social media sites such as Twitter, Vine and Facebook (Broderick 2014; The Voice Australia 2014). One particular viewer praised the show saying that they are “integrating social media conversations to influence the live content as well as extending the show beyond merely the television screen” (Messent 2012). The first season of the Australian show was very successful with a peak audience of four million, which as the network Shine states is “the largest reality format launch in Australian television history” (Shine Group 2012). In addition to this, the show had tremendous social media success with 1.7 million video steams in the first 72 hours of the season premiere, more than 50,000 on the #TheVoiceAU hashtag in just three days and 10,000 shares of performances on facebook (Britt 2012).

In conclusion, The Voice is one of many programs that now use social media to enhance the television experience. Social media such as twitter can be a tool to coordinate conversation as well as produce data around the success of a program. With large numbers of viewers and a huge level of engagement online, it is clear that programs such as The Voice have become successful through the inclusion and adoption of social media to further their shows reach.

  Reference List

Britt, Mark. 2012. “The Voice breaks records on-air, and online”. MI9, April 18. Accessed May 9, 2014. http://mi9.com.au/article.aspx?id=8453251

Broderick, Tony [@brod]. 2014. “Join the conversation around TV’s new blockbuster shows”. Twitter Blog, April 30. Accessed May 9, 2014. https://blog.twitter.com/2014/join-the-conversation-around-tvs-new-blockbuster-shows

Good, Robin. 2010. “Social Media Meets Online Television: Social TV Brings Television 2.0 To Your TV Set”. Accessed May 9, 2014. http://www.masternewmedia.org/social-media-meets-online-television-social-tv-brings-television-20-to-your-tv-set/

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. 

Messent, Rodd. 2012. “The Voice – Australia’s best example yet of social TV”. Accessed May 9, 2014. http://mumbrella.com.au/the-voice-australias-best-example-yet-of-social-tv-92339

Popportunity. 2012. “The Voice Australia: The Live Final!” Accessed May 9, 2014. http://thepopportunity.com/category/the-voice-australia/

Shine Group. 2012. “The Voice”. Accessed May 9, 2014. http://www.shineaustralia.com.au/productions/voice

Statistic Brain. 2014. “Twitter Statistics”. Accessed May 9, 2014. http://www.statisticbrain.com/twitter-statistics/

The Voice Australia [@TheVoiceAU]. 2014. Twitter user. Accessed May 9, 2014. https://twitter.com/TheVoiceAU

The Voice Australia. 2014. “The Voice AU”. Facebook Page. Accessed May 9, 2014. https://www.facebook.com/TheVoiceAu

Twitter. 2014. “#TheVoiceAU”. Accessed May 9, 2014. https://twitter.com/search?f=realtime&q=%23TheVoiceAU&src=hash

Woodford, Darryl. 2014. KCB206 Internet, Self and Beyond: Week 9 Lecture Notes. http://blackboard.qut.edu.au (accessed May 9, 2014)

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

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My Phone Knows Me Better Than I Know Myself (or the Rise of the Machines)

Perhaps the truly astounding thing here is that it is not some guy behind the machine collating the data, analysing it and then making educated guesses. Anyone with a reasonable degree of common sense would be able to figure out where I worked if they had access to my calendar and GPS tracking data. No. This is the computer. Machine learning (Siegel 2013). There is not some employee at Google saying “we could make James’ life easier by uploading our educated conclusions to his phone so he does not have to worry about when he needs to catch the bus to get to work on time”. It is my phone. My phone is doing exactly what Siegel (2013) said the machines would do: “automatically developing new knowledge and capabilities by furiously feeding on ... data”.

Do not get me wrong. I think predictive analysis (Siegel 2013) is pretty great. I like that Netflix has its recommendation system that suggests I watch Batman Beyond because I really enjoyed Justice League Unlimited and Young Justice. I completely see the benefits and I understand why Siegel (2013) would compare this new age of data collection to a gold rush. Instead of pointless junk mail and intrusive telemarketer surveys, companies are able to mine the data as purchases occur or clicks are made and instantly customise what they present to us with things we would actually use. Like how Tesco was able to personalise the coupons it sent to its customers (Siegel 2013), I noticed how Coles was collecting data off my Flybuys and giving me discounts on the coffee I buy. I can see why researchers would be so excited to get their hands on the data these companies collect – to try to figure out what all this information says about society at large.

But that does not mean it is not a little bit creepy that my phone has just now (and I do mean at the moment of having typed this) figured out where my girlfriend lives and how to get there by public transport because it also knows that I cannot drive.

  The machines are learning.

References

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

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Week 9 | The Macro, Meso and Micro

Axel Bruns and Hallvard Moe this week discuss the communicative faculties of Twitter and the different modes upon which these faculties exist (Bruns and Moe 2013). Bruns and Moe discuss three key layers of communication that exist in Twitter: “the micro level of interpersonal communication, the meso level of follower-followee networks, and the macro level of hashtag-based exchanges,” (Bruns and Moe 2013).  These three levels of communication can widely affect the way in which a user intends to communicate a message. The same tweet has the potential for a completely different intention dependent upon the mode or layer of communication they are utilizing (based on the Bruns and Moe models). For example, hypothetically a user could tweet a message about something highly personal, such as becoming a father for the first time – where tweeting it in the Macro level (using hashtags) could represent a public outcry for the world to see, whereas if the user tweeted this on a closer level of communication such as Meso or Micro, the tweet would become more personal and private and would be more about informing followers and friends (in the case of the Meso level).

These three levels of communication clearly represent different intentions for discussion or social expression. “Including a hash tag in one’s tweets signals a wish to take part in a wider communicative process, potentially with anyone interested in the same topic,” (Bruns and Moe 2013). The differences between communication levels can be vast. Macro levels of communication in Twitter are more about wider messages, about discussing issues, topics, jokes and so on with the world. A quick example of this would be the thousands of tweets during and in the aftermath of the Tsunami and Earthquake that hit Japan in early March 2011. The world tuned into Twitter, as messages to loved ones, friends and family were sent throughout the site during this catastrophic natural disaster. On a smaller scale are the Meso and Micro layers of communication on Twitter, of followers and followees and replies within Twitter (Kelly 2011). Another quick example, this time of the Meso and Micro layers, is of star NBA player Kevin Durant who tweeted during the NBA lockout about his desire to play a casual game of flag football. After many retweets (within the Meso layer), a student from Oklahoma State replied (within the Micro layer) to Durant’s tweet, inviting him to join his game of college football that night. Surprisingly, the professional basketball player joined the flag football game, scoring four touchdowns. These two examples of the three different layers represent the different intentions and results they can have on society and on personal life. Kevin Durant’s Meso and Micro layered communications were on the more personal, low-scale level and so resulted in real-life personal outcomes exclusive to him. Contrary to that is the example of the Macro level, which affected not just one or two people within the Twitter circle but thousands, if not millions. These layers exhibit the different uses and techniques that social media and specifically Twitter can have on society and a user’s personal life.

Paige Talbot | n8579857

References:

Bruns, A. and H. Moe (2013). Structural Layers of Communication on Twitter; The Perils and Pleasures of Tweeting with Fans; Tweeting About the Telly: Live TV, Audiences, and Social Media. Twitter and Society. K. Weller. New York, Peter Lang: 15-28, 221-236, 237-248.

Kelly, S. M. (2011). "Twitter's Top 10 Remarkable Tweet Stories of the Year." Retrieved 11-May, 2014, from http://mashable.com/2011/12/01/twitters-top-10-stories/ - _.

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The Clairvoyant Computer

Michaela Connors

“What often happens to you that cannot be witnessed, and that you can’t even be sure has happened afterward – but can be predicted in advance?” – Predictive Analysis (Siegel, 15:2013).

Big data can be sourced and collected from anywhere such as a medical procedure, credit application, Facebook post, movie recommendation, or fraudulent act (Siegel, 3:2013). It is then encoded and warehoused. It is estimated that stored data is increasingly growing by 2.5 quintillion bytes per day (IBM, n.d.). This resource is being used as a method of prediction, creating the ability to analyse an individual’s behavior and how the world works.

To learn more about the evolution of big data, watch the above video.

Prediction can be particularly difficult, as there is uncertainty in the unknown future (Siegel, 9:2013). Although, the accuracy of prediction does not have to be perfect in order to obtain value. There are many organisations that use guesswork in order to make operational decisions; this opens room for improvement in efficiency and excessive waste (Siegel, 10:2013).

  This notion is being revolutionised by Assistant Professor Kang Zhao who is leading a team to develop the first algorithm, changing the way predictive analysis works on online dating sites. Sites such as RSVP and eHarmony are part of the online dating business worth approximately $3 billion a year. Traditionally sites match users based on information entered into a profile such as age, likes, dislikes and hobbies (Emerging Technology from the arXiv 2013). The algorithm Zhao and his team have developed accesses users contact history, under the assumption that this data is based on what an individual is looking for in a partner (DiSalvo 2013).

  Image sourced from: http://www.digitaltrends.com/home/dating-apps-at-sxsw-2013/

The data was provided from a popular commercial online dating service, with over forty nine thousand contacts. Analysis into the data found that only twenty five percent where reciprocated. The new algorithm uses similar logic to Netflix, recommending movies by analysing the users viewing history. The new model combines two components, recommended contacts based on who users have previously contacted, and attractiveness looking into how many actually returned contact (DiSalvo 2013). Zhao (2013) states, “Your actions reflect your taste and attractiveness in a way that could be more accurate than what you include in your profile.” By developing a new algorithm that better predicts a more accurate online dating match, reciprocation has increased by over fifty percent for users (DiSalvo 2013).

  The data we provide, allows predictive analysis that is used in many online activities the typical mundane partakes in everyday life. This can create an ethical mine field, with no set or predetermined responsibilities or laws protecting online users. For more information, Chantelle Groth further derives into the exploitation of privacy in her post "Big Data and Predictive Technology - A Huge Advancement or an Ethical Minefield". As Siegel (2013) states, “Compelled to grow and propelled to the mainstream, predictive technology is commonplace and affects everyone”.

  References

  Brooks, Hannah, Sebastian Gutierrez. 2013. “How Machine Learning Can Transform Online Dating: Kang Zhao Interview”. Data Science Weekly, n.d. Available at: http://www.datascienceweekly.org/blog/7-how-machine-learning-can-transform-online-dating-kang-zhao-interview

  DiSalvo, David. 2013. “Why The Future Of Online Dating Relies On Ignoring You”. Forbes, July 12. Available at: http://www.forbes.com/sites/daviddisalvo/2013/12/07/why-the-future-of-online-dating-relies-on-ignoring-you/

  Emerging Technology from the arXiv. 2013. The Online Dating Engine tat Assesses Your Taste In the Opposite Sex (And Whether They Find You Attractive)”. Technology Review, November 18. Available at: http://www.technologyreview.com/view/521826/the-online-dating-engine-that-assesses-your-taste-in-the-opposite-sex-and-whether-they/

  IBM, n.d. “IBM – Bringing Big Data to the Enterprise – What is Big Data?”. Accessed May 10, 2014. http://www-01.ibm.com/software/au/data/bigdata/

Siegel, Eric. 2013. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Hoboken: Wiley Publishing.

TED-Ed. 2013. YouTube video, posted May 3. Accessed May 10, 2014. https://www.youtube.com/watch?v=j-0cUmUyb-Y 

Woodford, Daryl. 2014. “KCB206: Internet, Self and Beyond – Week 9 Lecture Notes.” Accessed May 10, 2014. https://lecturecapture.qut.edu.au/ess/lti/v1/launch/BLACKBOARD/90e0f566-3969-40ff-8600-e7cac2c2e219