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The revolution of new media and 'us.'

Lexy Haggard

In our fast-pace technological society, the demand to engage in new media is a budding social pressure, one that most find themselves conceding to. Social network sites have gained remarkable traction in recent times as a platform for both young people and adults to interact socially and professionally in a way like never before (Boyd, Danah. 2011). This global immersion in new media has dramatically altered our day-to-day lives particularly the way in which we interact with one another. Deuze (2011) offers the modern perspective that “the appropriations of media penetrate all aspects of contemporary life,” a life he describes to be lived “in, rather than with media.” This concept is perfectly demonstrated in the facebook ‘ecards’ which highlight the noticeably relatable ways in which people worldwide have transformed their lives to keep up to date with new technologies and incorporate varied forms of social media to their daily routine.

  These ‘ecards’ ultimately bring to light the way in which almost all forms of new media are readily accessible from a variety of our everyday devices including smart-phones, laptops and apples range of products making that instagram ‘gym selfie’ (taken before you get all hot and sweaty of course) a definite must! This excessive involvement with social media and people’s concern surrounding the way we are perceived on these platforms poses the question, why is it that we do what we do?

Well, according to Theresa Sauter (2013) “day-to-day processes of self-formation are becoming more and more implicated with new digital tools” suggesting the need for acceptance and likeability as something instantaneously receivable via SNSs. Furthermore, Sauter suggests that the status update is infact a tool for users to express themselves/their identity to others on a larger scale than possible in reality with the added comfort of sitting behind a computer screen lacking face-to-face confrontation. This notion is accurately reflected in a study conducted by The New York Times Customer Insight Group (2011) reveals that 68% of the 2500 surveyed users of online sharing sites, share content on the internet to give others a better sense of who they are and what they care about. With SNSs options to ‘like’ and ‘comment’ on others posts it is understandable why people turn to these platforms in search for positive attention and that much needed self-esteem boost! This theory is likewise discussed by Deuze (2011) as he distinguishes that “media become the playground for a search for meaning and belonging.”

According to Donath and Boyd (2004) social networks have a variety of valuable functions providing direct access to information about jobs, other people, and the world at large. “The types of social networks that develop in different communities have a profound effect on the way people work, the opportunities they have, and the structure of their daily life.”

So, while new media continue to develop at an unprecedented rate, “…it is too soon to tell what the final consequences will be, but it seems unlikely that they will ever be universal or stable” (Baym 2010).

References:

Baym, Nancy. 2010. “Ch 1: New Forms of Personal Connection.” In Personal Connections in the Digital Age, 1 - 21. Cambridge MA: Polity Press.

Boyd, Danah, (2011). Chapter 2 : Social Network Sites as Networked Publics – Affordances, Dynamics, and Implications. In Papacharissi, Zizi, A networked self – identity, community, and culture on social network sites, (pp.39 - 58). New York: Routledge.

Deuze, Mark. 2011 “Media Life.” Media, Culture & Society 33 (1): 137-148. (Available on CMD)

Donath, Judith and Boyd, Danah (2004). Public displays of connection., BT Technology Journal 22 (4) pp.71-82.

Sauter, Theresa (2013). ' What’s on your Mind ? ' Self-writing on Facebook as a tool for self-formation., New Media & Society pp.1-17.

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Week 4: Creating New Media Content - Carissa Dalton

In recent years society has been exposed to a digital transformation. As technology continues to advance, so do the ways in which media interacts with consumers. New technologies enhance our capabilities as operators in a modern world. Flew (2014) raises the question of why some media are considered to be new? The newness of media is certainly subjective, with some linking new media to that of variants on long established media products.  However, It must be acknowledged that the changes in media production, distribution and consumption over the last two decades capture changes that go beyond the actual devices themselves.  Furthermore, old media –print, broadcasting, film, still images – sets a foundation for new media and its place in society (Flew, 2104).  

The rise of hybrid economies and the increasing nature of community spaces such as YouTube exemplify how crowd accelerated innovation is becoming more and more pervasive. Crowd accelerated innovation proves to be one of the most effective forms of communication amongst consumers, suggesting that modern media audiences play a crucial role in creating, distributing and consuming media content. As discussed in Rosen (2006), Dan Gillmor understands that the “former audience” refers to the owners and operators of tools that were once exclusively used by media people to capture and hold their attention. For example, Rosen gives the example of how shooting, editing and distributing video once belonged to media bodies but now these elements are in the control of the user. This understanding links strongly to the view that web 2.0 has allowed for the redistribution of media power. This decentralisation means that new media audiences are far more empowered than ever before, moving from one-way, top down to two-way bottom-up communication (Green and Jenkins 2011, 109).  A once favorably centralized media scheme connected people up to the centers of power but not across to others. Now a horizontal flow, user-to-user, is just as significant as the vertical one (Rosen, 2006).

Let’s play is a series of screen shots or a recorded video that documents a play-through of a video game and usually includes commentary by the gamer. The rise of this user-generated phenomenon is owed to YouTube, which is one of the most active fields of online video sharing and content creation. By gamers sharing their own personal experiences to already established games, they are able to apply their own creative abilities. The growing concept that is Let’s play changes the way users experience gaming.  Furthermore, Let’s play links strongly to the notion of hybrid economies as discussed in Lessig (2008) in that it builds upon both the sharing and commercial economies and has the potential to dominate the architecture for commerce on the web.

The increasingly pervasive nature of user-generated media content, particularly the Let’s play phenomenon, is directly linked to the creation of new media content, which effectively empowers modern media audiences. 

References

Flew, Terry. 2014. “Ch 1: Introduction to New Media.” In New Media. 4th ed, 1-17 Melbourne: Oxford University Press

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

Lessig, Lawrence. 2008. “Chapter 7: Hybrid Economies” In Remix: Making Art and Culture Thrive in the Hybrid Economy, 177-224. New York, NY: Penguin

Rosen, Jay. 2006. “The People Formerly Known as the Audience.” PressThink: Ghost of Democracy in the Media Machine, June 27. Accessed March 21, 2014. http”//archive.pressthink.org/2006/06/27/ppl_frmr.html 

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New media, Big Data and Telemetrics (WK9 content)

Prior to exploring week nine’s topic I asked myself the question; ‘what even is big data?’ Well, Siegel (2013) suggests that “...data embodies a priceless collection of experience from which to learn” which evidences this type of new media is a lot more relevant than I had initially expected.

When properly analysed, data has the ability to unveil people’s motivations for the decisions they make, highly beneficial knowledge for organizations around the globe. Using the metaphor of a gold rush, Seigel (2013) continues with the notion that through this massive influx of data we are able to discover gold - the facts behind the data. He explains that it is from this data that predictions are made possible.

“Predictions drive how organizations treat and serve an individual, across the operations that define a functional society” (Seigel 2013).

Television programs are one of many organizations that implement this concept of analysing data in order to further their success. Through social media, television audiences may experience new channels of “mass conversation” (Spurgeon in Harrington 2013). Harrington (2013) discusses the manner in which “the television industry has responded to these developments be seeking out new ways of engaging in, and promoting, this conversation.” This concept of convergence between new media and these organizations has ultimately enabled the opening of doors between television programs and a seemingly limitless supply of technological resources. Twitter’s hashtags in particular, act as a means of collating data enabling the television industry to experience tremendous growth.

The power of #hashtags

Since the conceptualization of audiences as ‘active’ in this new technological society, “television has been well understood as a medium that readily catalyses audience discussion, interaction, fandom and other social activity” (Harrington 2013, 244).  In his lecture, Darryl Woodford (2014) discussed hashtags as a way of coordinating discussion around a topic in a relatively easy way to capture and analyse. It is discussed that twitter opens the lines of communication between users in real time through a collective conversation about their chosen television programs describing the platform as a “second screen” audiences can experience in their “virtual loungeroom” (Harrington 2013, 240-241).

“Television can be enhanced when experienced alongside others” (Harrington 2013).

A recent study suggested over 60% of people now use social media while watching television (Ericsson in Harrington 2013, 240). The popularity of this platform has been identified by television networks/programs that recognise this growing trend as an opportunity for success attracting audiences through hashtags and incorporating tweets into television (Harrington 2013, 242). Television program ‘The Voice Australia’ frequently utilises twitter to attract popularity to the show with live tweets constantly posted to audiences at home while further displaying their tweets on the bottom of our television screens tracked by the hashtag #TheVoiceAU which is frequently trending on Twitter. Harrington (2013) identifies this type of response to social media by networks as a “return channel” which both ignites discussion on twitter and attracts more people to the conversation.

See the voice’s twitter here: https://twitter.com/TheVoiceAU

I feel that Jones (Harrington 2013) really specifies the power of new media for television as he suggests; “Television networks can no longer assume that the production of quality content will be a distinguishing or determining factor in what programming audiences choose…they must deliberately craft intensive relationships with viewers, and formulate connections that will encourage routine and repeated viewing.”

Harrington (2013, 244) further discusses that in order to understand the meaning-making activities audiences participate in, as well as the way in which television is experienced; observations by television stations/programs are essential. These observations are conducted via platforms like twitter, which present a phenomenal prospect; the ability to collect and analyse a great quantity of data at minimal cost or effort instantaneously. This provides insight into the thoughts, feelings, emotions and opinions of a specific targeted audience which is then able to be analysed by television programs and stations to make appropriate and relevant decisions (Harrington 2013, 247).

REFERENCES

Harrington, Stephen. 2013. “Ch 18 Tweeting about the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society. 237-248. Accessed May 11, 2014.

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

Woodford, Darryl. 2014. “KCB206 Internet, Self and Beyond: Week 9 lecture notes.” Accessed May 8, 2014. http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

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Big Data and the ethical concerns it brings - Shaun Lewis

Since the invention of the internet and the potential it has reached the last decade it has completely changed the way we look at the world. We are being overloaded with information and data everywhere we look, mostly on social media. Many researchers think that the new currency in the world is big data and that many innovations will come in due time thanks to this knowledge. This huge amount of data that is being created at ridiculous rate is allowing us to process and track information that would have been ludicrous not too long ago. Although with all this new information at the ends of our fingertips, it has raised a lot of ethical concerns in terms of privacy.

  The hash-tags on twitter are at the top of this pyramid of easily identifiable ways of tracking what people are talking about, all you have to do is look at what’s ‘trending’ to get a basic idea of what people are consuming / thinking about at this very hour.  The Hashtag has such a big use “because they are one of the most visible ‘phenomena’ on twitter and as such are much easier to access for research.” (Bruns et al, 2013) The hashtag information that is there for the taking produces wide and varied amounts of data in response to any situation whether it being a computer game, live event or movie.

From what was said above marketing is obviously benefitting massively from all this online date. Companies are now just coming out with a unique hashtag and requesting you to post it if you like their brand or want to win free stuff.  Facebook and all sorts of websites now are also making companies “pay per click for the advertisements they display, therefore they predict which ad you’re more likely to click on.” (Siegel, 2013). Now on Facebook, Twitter and YouTube now the only advertisements that show up are products I’m generally interested in and would actually click on if it was a sale or something of the sort.

Unfortunately big data and privacy do not work hand-in-hand and do not work together at all. Huge amounts of personal data are being collected by many statistic agencies and social networking companies to determine your demographic information and internet activity such as liking, commenting and sharing.

Research is not always a terrible thing for society; it essentially shows us that our every move on the internet is being watched. The ethical morals that surround big data are always going to be ever –changing and not at all a topic we can collectively agree upon,   but that doesn’t mean it’s going to stop happening.

References

Bruns, Axel. 2013. “Follower Accession: How Australian Politicians Gained their Twitter Followers.” Mapping Online Publics Blog, July 8. Accessed February 4, 2014.

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

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Blog post week 10: Big Data, Predictive analytics and the Hash Tag

Every time we sit down at a computer and open an Internet browser, we are sending a torrent of information or “data” out into the world. Everything from social media posts online shopping patters and transaction records can be collected, stored and used to help companies market their products more effectively. This vast well of information is referred to as “Big Data”, and for those who can harness this resource, there are significant competitive advantages to be gained. But just what is “Big Data?”

Big Data refers to the recent and “exponential growth and availability of data, both structured and unstructured” (Davenport & Dyche, 2013).  This information is used by businesses to create more effective and accurate marketing techniques, by analyzing and predicting patters of consumer behavior and spending.  This is achieved through the process of “machines learning from data” (Siegel, 2013). A variety of companies are using this method of predictive analysis to help improve their products; like Netflix recommendation system for Movies and TV shows, or advertising companies using your data to predict which products you’re most likely to be interested in, and tailoring their ads accordingly. Eric Siegel (2013) refers to this as “Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions”.

Although these predictions are not always 100% accurate, they do help in identifying consumers who are more likely to respond to an ad, or have use for certain products. This helps to increase the amount of relevant advertising a consumer will receive, and reduce the amount of unwanted “junk mail” they receive.

“In this way the business, already playing a sort of numbers game by conducting mass marketing in the first place, tips the balance delicately yet significantly in its favor and does so without highly accurate predictions” (Siegel, 2013) 

One of the most valuable sources of data for these companies is social media, and one of the most effective tools in organizing this torrent of information is through use of the “hashtag”. Since their first appearance in 2007, hashtags have been a central part of social media, Twitter in particular. They are used to help connect an individual’s content and thoughts with other people's related content in a simple and quick way. By doing this, it has helped social media to organize and structure content, and “help social media users participate in wider community conversations” (Cazier, 2013).

Recently, other social media sites like Facebook and Google+ have begun to adopt the hashtag. While on one hand this can been seen as an attempt to harness the social advantages of hash tags, companies are likely to use this as another method of “big data” analysis to increase their advertising and marketing potential. This sentiment is reflected by Clay Cazier (2013), who writes “Hashtags are, by their very nature, harnessing the potential of people’s thoughts and interests and turning them into mineable information”. 

  References:

Davenport, T. H. & Dyche, J. 2013. “Big Data: What it is and why it matters”. Accessed May 8, 2014 http://www.sas.com/en_us/insights/big-data/what-is-big-data.html

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

Crazier, C. 2013. “Hash-tags as Big Data”. PmDigital, July 10. Accessed May 11, 2014 http://www.pmdigital.com/blog/2013/07/hashtags-as-big-data/

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Big Data and How it Creates Big Money- Niamh Burke

Every online purchase, hashtag used, and like of a page contributes to the idea of ‘big data’. Grace (2012) describes big data as “Global access to all possible information about human response and human innovation.” It allows companies to analyse and respond to data collected through social media platforms, especially Facebook and Instagram. Siegel (2013) explains that by gathering big data it “embodies a priceless collection of experience of which to learn”. If companies are to learn and increase their profit from big data, what are the effective ways in which to do so?

  A primary example of where big data is gathered from is Instragram. This social media platform allows users to post photos and videos accompanied by a caption. Instagram is the new ‘hip’ type of social media, which is a gold mine for companies. Popularity on Instagram is measured by how many people are ‘following’ your account as well as how many likes you get per photo. An effective way of how using big data has increased awareness and profit for companies is sponsoring a person who has a large amount of followers.

  A recent company that has harnessed this tool to create a whirlwind of talk about their company is Frank. Frank is an Australian company based in Melbourne that makes an exfoliating coffee scrub for your body. Now this may seem like quite a boring product that would struggle to make it a popular, Instagram success. However by targeting popular Instagram-ers to try their product this generated awareness from thousands of other followers. The popular way to post about Frank was to take a ‘selfie’, covered in the exfoliator, with the product positioned in the lower half of the photo.

  After viewing the Frank website I came across a blog post which was titled ‘How to take the perfect frank selfie.’ It explained that to perfect the selfie the hashtags #thefrankeffect and #letsbefrank were vital (Frank, 2014). I searched these hashtags on Instagram and #thefrankeffect generated 19,923 posts (see picture below) and #letsbefrank had 14,663 posts (Instagram, 2014).

(Instagram, 2014)

By using big data to understand the most effective way to promote their product on Instagram they have become increasingly successful from it. I have seen this product promoted all over my personal Instagram news feed. For $14.95 girls from all over Australia have purchased this product to not only use it but  more importantly post it on Instagram. Even my sister mentioned she has ordered a bag of Frank because it has been promoted so intensely over Instagram, so why not see if it really works? I however have been quite sceptical of how well this product works as I feel many people who post about it on Instagram are just trying to follow the ‘trend’ without actually caring about how well it exfoliates your skin.

  To conclude, big data is a powerful tool. It allows companies to understand their target market to a fuller extent and combine this data with marketing geniuses and voilà, a business increases profits and becomes more successful.

    Reference List

Frank. 2014. “How to take the perfect selfie”. Accessed May 11, 2014. http://au.frankbody.com/blogs/news/10565689-how-to-take-the-perfect-frank-selfie#.U29lKi-ID3o

Grace A. (The Economist). 2012. “What is Big Data?” YouTube Video, posted June 26. Accessed May 11, 2014 https://www.youtube.com/watch?v=ahZGEusG13A

  Instagram. 2014. “#THEFRANKEFFECT.” Viewed May 11, 2014.

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

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Big Data: Taking the Good with the Bad - Simon Nugent Week 9 Readings

“Data is the new Oil”, so said the European Consumer Commissioner Meglena Kuneva, and she had a point too. Today’s society with its penchant for all things connected online, allows for a most valuable resource to exist electronically. Data is and will continue to be one of the most important and valuable resources available to big business for the foreseeable future.

But why data? Facebook posts, silly quizzes about which Jonas Brother you are and spam emails can’t be all that useful can they? Well, it’s important to remember all of the data we enter online. Google searches, credit applications and medical information all uploaded, published and clicked on are aspects of your online footprint that involve data. But that’s not all. You see, data isn’t valuable in and of itself. However, when data is processed, it unlocks a deep well of information.  As Siegel (2013) so aptly put it:

  “It uncovers what drives people and the actions they take-what makes us tick and how the world works. With the new knowledge gained, prediction is possible. “

  Yes, that’s right, with the use of data processing, companies can really make predictions on, who you are as a person, what you might be interested in and what you might be doing on a day-to-day basis (Siegel, 2013). For the companies this means they have more daily opportunities to sell you stuff. They can tell what you’d probably buy and what you’d probably be doing online at any certain time and can tailor their advertising right to you.  Though not always to a completely accurate degree (Siegel, 2013).

  This can be very good for you. On one hand it means that it’s likely that ads you see are only going to be things that you want to buy, and similarly, you will be recommended movies that you actually want to see and music that you want to listen to. Your online experience can be tailored to you and centered on you. It means more meaningful communication between the consumer and the company and therefore less junk that you need to sort through (Siegel, 2013).

  However Big Data has it’s own dark sides too (Woodford, 2014). Some would see this new world of Data collection as a mass invasion of privacy. In many cases, third parties have access to personal data about you and the government is privy to much of your information too (The Economist, 2012). In this case it can basically be said that parts of who you are being sold. So is data collection really as good as we hope it is or do the negatives outweigh the positives?

  Whatever you happen to believe about data collection, it is clear that it is beginning to define the way organizations gain capitol. Data is becoming a more and more important resource in the world of New Media, and the questions is how much of it are you willing to give up?

  References:

  The Economist. (2012). The dark side of big data. [Online Video]. 26 June. Available from:https://www.youtube.com/watch?v=raJOkguPrH4. [Accessed: 11 May 2014].

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

  Darryl Woodford (2014). New Media, Big Data and Telemetrics, lecture notes distributed in the topic KCB206 Internet, Self and Beyond. QUT, Kelvin Grove on 1 May.

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Big Data and Telemetrics (Week 9) - Vanessa Wu

Data is emerging as the world’s newest resource for competitive advantage among nations, organizations and business. It is estimated that every day we create 2.5 quintillion bytes of data from a variety of sources. (Frank, 2012) These are from the computer notes to posts on social media sites and even transaction records. These collections of large and complex data sets are difficult to process using the on hand database management tools are known as big data, which is a popular term that describes the exponential growth, availability, and use of information, both structured and unstructured. (Kudyba, 2014, 72) So how are these massive data related to our everyday lives?

In today’s world we are surrounded by predictions. For example, during political elections, the main focus of the media and the public is not on the differences between the candidates’ positions, but rather on the “horse race” aspect of the competition. Predictions are also made about the stock market, box-office of movies, or even a football match. Moreover, nowadays, with predictive analytics, big data has been successfully used to make predictions in the marketing, insurance, retail, and many other sectors. Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. It forecasts what might happen in the future with an acceptable level or reliability.

Big data-derived predictive analytics are now popping up all around us in the consumer and social world we interact in. The Executive Vice President of strategy and analytics for Target Data, a marketing firm that combines big data and analytics to help businesses target consumers during the major life event of moving, Scott Bailey stated that “We have built a model that can predict with 75% accuracy the likelihood that a home will sell in the next 30,60 or 90 days”. (Kanani, 2013) Cash registers are now available that predict which discounts will be redeemed to make smarter decisions as to what coupons will be given at check-out to drive sales. Websites are now predicting which advertisement people will click in order to instantly chooses which one to show, driving millions in new-found revenue. Netflix uses this approach to predict which movies you will like. Online dating sites predict who will be your best mate. Everywhere we turn, the power of big data-derived predictions is becoming evident.

A general problem is that online data sets are big. There is no guarantee that they are representing the overall population. Data from social media often tends to over-represent an urban, educated and privileged population. And even if you could predict elections or the stock market, your accurate predictions would change the object of your study in such a way to render your predictions meaningless.  

  Reference:

Carlos Castillo. 2014. “Predicting the future with big data”. Accessed on 9 May 2014. http://www.aljazeera.com/indepth/opinion/2013/10/predicting-future-with-big-dat-2013103143325386234.html

Christopher Frank. 2012. “Improving Decision Making in the World of Big Data”. Accessed on 9 May 2014. http://www.forbes.com/sites/christopherfrank/2012/03/25/improving-decision-making-in-the-world-of-big-data/

Rahim Kanani. 2013. “Target Practice: The Power of Predictive Analytic”. Accessed on 9 May 2014. http://www.forbes.com/sites/rahimkanani/2013/07/29/target-practice-the-power-of-predictive-analytics/ 

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

Stephan Kudybe. 2014. Big Data, Mining, and Analytics: Components of Strategic Decision Making. n.p.: Auerbach Publications. Accessed on 9May 2014. http://books.google.com.hk/books?id=nuoxAwAAQBAJ&printsec=frontcover&dq=Big+Data,+Mining,+and+Analytics:+Components+of+Strategic+Decision+Making&hl=zh-TW&sa=X&ei=jh1uU_j6BcKgkAXIl4GQCA&ved=0CD0Q6AEwAA#v=onepage&q=Big%20Data%2C%20Mining%2C%20and%20Analytics%3A%20Components%20of%20Strategic%20Decision%20Making&f=false

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#bigdatatelemetrics

Carly Shearman – Week 10

I was told by a little bird that for my final blog post I should talk about Twitter. We, members of the Twittersphere, produce a great mass of data. In fact, we produce so much data that it is called ‘big data’. We are drowning our advertising companies, researchers and other big brother-ish characters in so much data that it is too much data. ‘Big data’ is kind of like content analysis; it produces a great mass of data. We are left wondering though, what does it all mean?

#hashtag research helps us to make sense of what is going on. Woodford (2014) explains that the small hashtag symbol allows us to:

Coordinate conversation around a particular subject (e.g. #bringbackourgirls)

Relatively easily capture and analyse (just click on the hashtag and you become part of a new world specifically designed around one topic)

A major issue with hashtagging is that they only work when you use them (Woodford 2014). Therefore, if I am discussing My Kitchen Rules, people can only be part of a conversation or analyse me IF I use the hashtag. By using a hashtag, we are kind of placing a tracking device on whatever we post.

If we essentially put tracking devices on our content then don’t we want to watch what people could track? My question to you is how much information do you put up on the public domain? A little? A lot?  You would be surprised to know that almost everything you do is traceable. That includes online purchases you make and private messages on Facebook (Gray 2014). How crazy is that?

This week’s lectures and readings were helpful but an article I read really made me understand the world of new media, big data and telemetrics. The article was about Janet Vertesi, a pregnant woman determined to “keep her pregnancy undetected by cookies, bots and data collectors,” (Gray 2014). Sounds like a simple task, right? Wrong! A normal person’s data is worth around 10 cents HOWEVER a pregnant woman’s data is worth around $1.50 (Gray 2014). Why? Because they are an advertisers dream! Baby clothes, baby furniture, prams, bottles, diapers, maternity wear; the baby business thrives on soon-to-be-mums wanting the best for their newborn.

Twittersphere is not all about being tracked and how kind of stalkerish that is but it is about connectivity. According to Harrington (2013, 239) “television is a highly social medium,” and “can be enhanced when experienced alongside others.” Twitter and hashtagging creates a sense of community amongst fans. Social media eradicates barriers of time and space. The art of hashtagging allows people to connect with those outside their ‘friends’ or ‘followers’ and allows them to discuss their opinions about a particular TV program or storyline (Harrington 2013, 240).

Social media is a “second screen” whilst watching television with many people now using social media while watching television (Ericsson 2012). Social media, and more so Twitter, allows us to connect with people outside our living room. Twitter also ensures that marketing companies have the highest chance of success by targeting and profiling people due to what we discuss, share and post.

For my final blog post, I again present you with the confusing facts. Is Twitter good or bad? Are we okay with being tracked if it means we get to have a social forum? Social media and media communications is not so black and white. These weekly reflections have really taught me that. But hey, I guess at the end of the day it is up to us as individuals to decide what we do and do not find acceptable and whether we do or do not want to participate in these new medias.

#thanksforreading

       Reference List

Ericsson. 2012. “TV and Video: An analysis of evolving consumer habits. August 2012” Accessed May 8, 2014. http://www.ericsson.com/res/docs/2012/consumerlab/tv_video_consumerlab_report.pdf

Gray, Sarah. 2014. “One woman’s attempt to hide her pregnancy from big data – it’s more difficult than you’d expect.” Salon, April 29. Accessed May 8, 2014. http://www.salon.com/2014/04/28/one_womans_attempt_to_hide_her_pregnancy_from_big_data

Harrington, Stephen. 2013. “Chapter 18: Tweeting About the Telly: Live TV, Audiences and Social Media.” In Twitter and Society, edited by Axel Bruns, Jean Burgess, Merja Mahrt and Katrin Weller, 237-248. New York: Peter Lang.

Woodford, Darryl. 2014. “KCB206 Internet, Self and Beyond: Week 9 lecture notes.” Accessed May 8, 2014. http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

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Big Data and Predictive Analytics Week 10 Blog Tianxiang Yu (Anthony) N8030715

Now the world has become into a digital era of information explosion, data is generated around us all the time. With the widespread popularity of social networks, each internet user has become a major part of data collection. Any operation by users on the internet, could be used to understand their character, habits, and even values. Thus, the increasing amount of big data affects and changes people’s personal life and work in some aspects.

  Facebook's director of engineering Jay Parikh explains the significance of big data: “Big data really is about having insights and making an impact on your business. If you aren’t taking advantage of the data you’re collecting, then you just have a pile of data, you don’t have big data." (Cohen 2012)

  “Big data” has been existed in the industries of science, military, communications, finance and others for a long time. But the internet is only rapidly developed in recent years, and the new media in film and TV industries is now just showing up its advantage. In the film and TV industries, nothing is certain. A film and TV production may have found all the great directors, actors and screenplay of popular themes, but the results is still unsatisfactory. However, prediction is power (Siegel 2013). According to Siegel (2013), “Predictive analytics (PA)-Technology that learns from experience (data) to predict the future behaviour of individuals in order to drive better decisions.”

  PA leads within the growing trend to make decisions more “data driven”, with empirical evidence. PA is the means to drive per-person decisions empirically, as guided by data. Predictions drive how organizations treat and serve an individual, across the operations that define a functional society. And, PA is a completely different from forecasting (Siegel 2013).

  American TV series "House of Cards" is a successful example of "custom" TV show based on data analysis.

It's presented and brocasted by Netflix. Every day, Netflix user traffic averagely contains 30 million users, 4 million comments, 3 million searching requests. From the audience insight, targeting, every step is guided by accurate and detailed data analysis, which is decision produced by user demand (Armina 2013).

YouTube Video: The Risk Behind Netflix's Original Programming Leap

  It seems that Netflix knows what audience like to see. From their database, they have been known audience like director David Fincher’s film, Kevin Spacey’s performance, British version of "House of Cards" (running 1990) is popular.

Eventually, what to film, who is gonna direct, who is gonna act, how to broadcast, all the decisions are made by millions of audience’s personal preferences (Carr 2013). Netflix's former VP of communications Steve Swasey said “We don’t have to spend millions to get people to tune into this, through our algorithms we can determine who would be interested in the show”(Ryan 2011).

  From a marketing point of view, excavate and analyse habits and preferences of users from complex data, find out the products and services which fit the "taste" of users more, and combine with user’s needs to adjust and optimize the product and services specifically, that is the value of big data.

References

Armina, Ligaya. 2013. “Netflix ready for onslaught: CEO; Competitors developing own services”. National Post, May 15. 

  Carr, David. 2013. “Giving Viewers What They Want”. New York Times, February 24. 

  Cohen David. 2012. “Gabriel & Co. is looking for a Social Media Coordinator.  Facebook VP Of Infrastructure Engineering Jay Parikh Talks Big Data, Project Prism”. AllFacebook, August 23.                                

Keating, Gina. 2014. “Myths and realities Netflix finds higher demand for old movies and TV shows”. South Florida Sun – Sentinel, Feb 22. 

  Ryan, Lawler. 2011. “How Netflix Will Use Big Data to Push House of Cards.” Gigaom, March 18. 

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

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Big Data creates "Big Bucks" - Louise Stokes

Each time a new media user likes, shares, hashtags or online purchases, the concept of big data is displayed. What is big data really though? “Big data is a way of understanding the world, we can measure so much more than we ever could, and now we’re going to be able to visualise so much more than we ever could. It’s going to provide a whole new framework for understanding human civilisation” - Jason Silva, (2012, The Economist). Big data gives large corporations the chance to examine and act accordingly to data collected through social media platforms. Siegel (2013) explains that by gathering big data it “embodies a priceless collection of experience of which to learn”. In the event that organisations are to take in and raise their benefit from huge data, what are the successful ways in which to do so?

An example of where big data is collected from is Instagram. It is a mobile social media, photo-sharing platform that allows users to follow and like/comment on brands and people they are interested in. According to Smith (2014), there are 200 million monthly, active users on Instagram. “Instagram has quickly gone from a trendy iOS-app, to a massive social network with Android and web presence. Instagram hasn’t shown any sign of slowing down, and with Facebook’s pocketbook behind it now, its growth will surely continue,” (Smith, 2014).

A local brand that has increased its marketing exposure by using Instagram effectively is SaboSkirt. Two Brisbane girls originally started fashion blogging in early 2010 and since then has created an online store and now has one million Instagram followers from all over the world. I believe without Instagram they wouldn’t have been able to experience growth in the way they have.

(Instagram Profile @saboskirt)

By utilising big data through Instagram, Saboskirt has been able to experience global success. Instagram has allowed customers to interact directly with the brand and given them the platform for such close engagement.

Companies can examine their audiences in greater depth and thus give them and continue to deliver to their needs as a group. This new understanding can give the company new ways to promote to their followers and continue to grow them, creating a hugely popular concept.

In summary, big data gives a shifting control to brands. It allows companies to better promote to its audiences and therefore grow its business. Big data is an ever increasing popular tool, that when efficiently dissected, can affect monetarily and fascally, as well as inside social domains.

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

Silva, J. (The Economist). 2012. “What is Big Data?”. Youtube Video, posted June 26. Accessed May 10, 2014 https://www.youtube.com/watch?v=ahZGEusG13A

Smith, C. (Digital Maketing Ramblings). 2014, “(April 2014) By The Numbers: 70 Interesting Instagram Statistics, posted March 6, 2014. Accessed May 9, 2014 http://expandedramblings.com/index.php/important-instagram-stats/#.U3gSdRCLNoc

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How big data is used to predict music's next "big thing". Christine E. Hansen

As pointed out in my previous posts, new media have enabled a vast number of new opportunities to connect, share and communicate – in addition; it has also enabled organisations to collect data. Through our participation in several new media channels, companies more often collect data about consumer behaviour.  Siegel (2013, 3) explains that “data embodies a priceless collection of experience of which to learn”.  If we allow organisations to collect data about our behaviour, it will, according to Siegel (2013), enable them to make predictions that may benefit the organisation.

When we share content on Facebook or Twitter, stream music, watch videos on YouTube, purchase products on amazon or search Google for our next holiday; online organisations store this information, data, and use it to “uncover what drives people and the actions they take, what makes us tick and how the world works” (Siegel 2013, 4).

Kadhim Shubber (2014) reports in The Guardian that social media platforms and streaming sites have enabled record labels and other music industry stakeholders to acquire access to immense quantities of data on listening habits and contemporary trends.

Not long ago, our listening habits were relatively private, and we could listen to our favourite music without Spotify or Last.fm storing the information or sharing it with the rest of the world. Before organisations gathered data; before new media, record companies had an overview of where their CD’s were popular, and which radio channel played their songs, however, that information only painted an incomplete picture (Shubber, 2014). Yet, Shubber (2014) states that the new explosion of data from several sources such as torrenting, music streaming sites and social media platforms has given the music industry a great opportunity to appreciate their fans and find upcoming artists, like never before. Simultaneously, as the Internet is stripping the power away from record labels, it is also empowering them to foretell future hits.

 Likewise, other branches of the music industry such as the mobile app “Shazam”, which enables users to recognise music and TV around them, is using stored data to predict artists that will most likely receive a growing attention in the nearest future (Datoo, 2013). The app combines critics’ evaluations together with the number of people that have utilised Shazam to search for a song, in order to comprehend which artists that are currently creating a high level of interest. By doing this, Shazam is able to make use of consumer behaviour to better evaluate the artists that have already begun to raise interests and that are on the way to attain traction.

With the new knowledge gained by data collection, Siegel (2013) contends that prediction is possible. Furthermore, by connecting the latter example of utilising data to the aforementioned statements by Siegel, the data collection is most likely benefiting organisations in several ways, but primarily, it allows them to predict new trends and how consumers may behave in the future. Conversely, one could beg the question if the collection of data is as benefiting to the consumers, or if it belongs to the discussion regarding exploitation of users.

References

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

Datoo, Siraj. 2013. "How Shazam uses big data to predict music's next big artists". The Guardian, December 10. Accessed May 9, 2014. http://www.theguardian.com/technology/datablog/2013/dec/10/shazam-big-data-prediction-breakthrough-music-artists#start-of-comments

Shubber, Khadim. 2014. "Music analytics is helping the music industry see into the future". The Guardian, April 9. Accessed May 9, 2014. http://www.theguardian.com/technology/2014/apr/09/music-analytics-is-helping-the-music-industry-see-into-the-future

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Big Data and Moral Ambiguity

When we access the internet, we are basically drowning in data - with social media, digital images and multiple other sources.  In fact nearly 12 terabytes of data are created by tweets alone each day. The rate in which data is being created is accelerating exponentially. In fact, 90% of data in the world today was created within the last couple of years (Gobble, 2013). This is all essentially part of big data. Many believers think that within in big data lies great innovations. However , it also raises many ethical questions in terms of privacy.

Unfortunately big data and privacy are often not two concepts that work well together. Extremely large amounts of personal data such as demographic information, internet activity, electricity usage and social interactions – are being collected by many nation statistics agencies, organizations and social networking companies. However with the loss of privacy, also comes great potential. This data can be used to facilitate advances in science; public policy and help students develop their data analysis skills. (Machanavajjhala & Rietwe, 2012, 20)

We have become aware of the potential that big data holds - it’s being used by many people and companies to create and drive business models, or to make predictions on the future outcomes on projects.

This way of conducting and predicting success can apply to any industry. One industry that uses it for its advantage is the film industry. They can use the hits on Wikipedia pages of future films, to make a prediction of the film’s success when it is released.  Big data can also be used to potentially stop large financial losses within companies. Many large commercial data services are known to use their data towards the use of fraud detection.  By doing this they can avert immense financial losses (Lesk, 2013, 87).

Even hash-tags on twitter are often used for market research.  Bruns et al (2013) suggest that this is because they are one of the most visible ‘phenomena’ on twitter and as such are much easier to access for research. The research that is conducted with hash-tags can be wide and varied. It includes audience response to film and television shows - as well as getting some sort of understanding to their popularity. It also is used to look at the reaction times of the general public to breaking news and social issues.

While, research is rarely a bad thing - it essentially shows us that our every move on the internet is being watched. The moral ambiguity surrounding big data is an interesting one, many worthwhile innovations and research can come from it, but many people see it as a breach of user privacy.  Although many social media sights tell you that your data may be used in its privacy policy (which people rarely read) – it still comes as a surprise to many.

I will leave this post with a video, by the youtube channel Thinkr with Rick Smolan discussing some of the dangers of Big Data in the online world.

References

Bruns, A. et al, (2013). Chapter 2 : Structural Layers of Communication on Twitter pp. 15-28; Chapter 17 : The Perils and Pleasures of Tweeting with Fans pp. 221-236; Chapter 18 : Tweeting About the Telly : Live TV, Audiences, and Social Media pp. 237-248. In Weller, Katrin et al (eds), Twitter and society, (p.-). New York: Peter Lang.

Gobble, Marry-Anne 'Big Data: The Next Big Thing in Innovation" Journal of Research Technology Managemetn 56(1) 64-66 http://search.proquest.com.ezp01.library.qut.edu.au/docview/1315657208 Accessed 10th May 2014

Lesk, Michael, 2013 "Big Data, Big Money, Big Brother," Security and Privacy 11(4) 85-89  http://ieeexplore.ieee.org.ezp01.library.qut.edu.au/stamp/stamp.jsp?tp=&arnumber=6573299 Accessed 10th May 2014

Machanavajjhala Ashwin & Rietwe, Jerome  2012 “Big Privacy: Protecting Confidentiality in Big Data,” XRDS Crossroads  19(1) DOI: 10.1145/2331042.2331051 Accessed 9th May 2014

Thinkr, 2013 "The Dangers of Big Data," Youtube video posted April 16. Accessed 11th May 2014

Image sourced from http://blog.joelrubinson.net/wp-content/uploads/2014/04/big-data-cloud.jpg

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Big Data & Target Marketing (Week 10 – John Geraghty)

The dawn of the digital era has entirely revolutionsed the way humans process, understand and interact with information. As suggested in the video, libraries and books are no longer our sole reference for analysing the human experience. Big data is an extremely diverse currency that has emerged from Web 2.0 that allows us to perform tasks that once upon a time, would be deemed impossible. The almost unfathomable scale of the data available gives us global access to all possible information about human response and human innovation – and while this has raised various ethical concerns regarding privacy, it has allowed business (particularly in the field of marketing) to flourish.

  In an article written for the NY times, Charles Duhigg (2012) makes a commentary on the marketing methods of department store, ‘Target’ in the United States. Through an intensive process of target marketing and data analysis, Target revealed to an American father that his teenage daughter was pregnant before he knew at all. The store’s marketing team managed to find this out through closely tracking their consumers spending habits. This is achieved through the use of ID numbers for each shopper, allowing the marketing analytics department to closely examine specific customers. The team then sent advertising material to the address of the girl, promoting infant related products (Duhigg, 2012).

  (Sourced from www.polyvore.com)

This raises various ethical issues for consumers, especially the Father of the pregnant teenager. Should large corporations such as Target have the ability to hone in on people in this way? The methods used to analyse consumer behaviour in the 21st century allow companies to understand so much more about our lifestyle, even to the point of predicting pregnancies and then trying to win us over with specifically angled promotion. The use of the internet now also plays an extremely important role in business target marketing.

  In the current media environment, an online presence is almost essential for any modern business to thrive. Coupled with this, a strong understanding of the analytics associated with online consumer interaction is required to gain a comprehensive understanding of their motivations, wants and needs. Hashtags (#) play a crucial role in this process and allow marketers to easily coordinate discussion around certain topics with their consumers (Woodford, 2014).  I only needed to browse my Facebook feed for ten seconds before finding a promotional video from ‘V Energy Drink Australia’ promoting their new energy drink. An interactive social media campaign encourages consumers to upload videos of them pranking their friends, tied together with the hashtag, ‘#kaboomamate’. This type of promotion allows companies to see the ways in which their consumers interact with the product and also has the consumers advertising the product itself through both the video and the hashtag posted from their individual social media (Venergydrinkaus, 2014).

 View: https://www.youtube.com/watch?v=bTSkz9RWRhs 

This once again poses certain ethical dilemmas in massive companies knowing so much about our motivations. It seems almost inevitable that corporations will continue to manipulate the technologies available to them. It is probable that moving into the future target marketing will become even more specifically angled to consumers of various backgrounds, trying always to win us over by tailoring to our differing needs.

  Reference List

  Duhigg, Charles. 2012. “How companies learn your secrets.” New York Times, February 16. Accessed May 9, 2014. http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=1&_r=3&hp&adxnnl=1&adxnnlx=1329521286-bAJ8Lu/xck%20DQwMUzVaodQ&

    Venergydrinkaus. 2014. “Adrian Van Oyen introduces #kaboomamate.” YouTube video, posted May 8. Accessed May 9, 2014. https://www.youtube.com/watch?v=bTSkz9RWRhs

    Woodford, Darryl. 2014. “KCB206 internet, self and beyond: Week 9 lecture notes.” Accessed May 9, 2014. http://blackboard.qut.edu.au/webapps/portal/frameset.jsp?tab_tab_group_id=_4_1&url=%2Fwebapps%2Fblackboard%2Fcontent%2FlistContent.jsp%3Fcourse_id%3D_108110_1%26content_id%3D_5232451_1

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Big Data (Blog Post 7: Yani Webber)

Big Data

What is it? Why is it important? This final blog will focus on Big Data and the relationship with Social Media.

Big Data essentially refers to a term that describes the “exponential growth and availability of data, both structured and unstructured” (Davenport & Dyche 2013). Forbes contributor Lisa Arthur explains further by stating “Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis” (Arthur 2013). Big Data has become extremely important to businesses and society, just as the internet has, because the more data available, the more accurate analyses, which in turn, leads to more effective decision making and greater efficiencies (Davenport & Dyche 2013).

“Big data is a way of understanding the world…we can measure so much more than we ever could, and now we’re going to be able to visualise so much more than we ever could. It’s going to provide a whole new framework for understanding human civilisation” - Jason Silva, 2012, The Economist.

In Analysing Big data from a new media perspective, the most crucial element is perhaps the hash-tag and hash-tag research; hash tags are used as a way of coordinating discussion around a topic and are relatively easy to capture and analyse (Woodford 2014). As hash-tags allow for users to search tags to engage in wider participation and conversations, they are the core to Twitter’s success. As other social media platforms, such as Facebook, have incorporated hash-tags, the initial thought was for social purposes, however underneath this, companies are adopting hash-tags for the focus on Big Data. “Hash-tags are, by their very nature, harnessing the potential of people’s thoughts and interests and turning them into mineable information” (Crazier 2013).

This element in itself is reinventing social media – taking a social communication platform and squeezing out vital information it holds for learning and predictive marketing. “The world’s largest social networks are storing massive amounts of never-before-analysed data that could reveal crucial information about consumers… Potentially, this data could also make social networks like Facebook, do a better job of showing users what they want to see, rather than content and ads they’d rather not waste time on” (Smith 2014). Social media plays a huge role in Big Data and analytics of human behaviour, as data is produced at an inconceivable speed and in a variety of formats, including pictures and videos, Big Data allows for organisations to manage social conversations across social media in real-time. “Big Data is usually associated to having volume, velocity, variety, variability and complexity”, these features are also embedded in social media analytics. Social media holds the world’s largest source of unexplored consumer data (Bowden 2014).

Yani Webber

References

Silva, J. (The Economist). 2012. “What is Big Data?”. Youtube Video, posted June 26. Accessed May 10, 2014 https://www.youtube.com/watch?v=ahZGEusG13A

Arthur, L. 2013. “What is Big Data?”. Forbes, August 15. Accessed May 10, 2014 http://www.forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/

Davenport, T. H. & Dyche, J. 2013. “Big Data: What it is and why it matters”. Accessed May 10, 2014 http://www.sas.com/en_us/insights/big-data/what-is-big-data.html

Woodford, D. 2014. “New Media, Big Data & Telemetrics”.  Accessed May 10, 2014 http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

Smith, C. 2014. “Reinventing Social Media: Deep Learning, Predictive Marketing, and Image Recognition Will Change Everything”. Business Insider, March 21. Accessed May 10, 2014 http://www.businessinsider.com.au/social-medias-big-data-future-2014-3

Bowden, J. 2014. “Reasons to Explore Big Data with Social Media Analytics”. Social Media Today, January 15. Accessed May 10, 2014 http://socialmediatoday.com/jayson-bowden/2066591/reasons-explore-big-data-social-media-analytics

Crazier, C. 2013. “Hash-tags as Big Data”. PmDigital, July 10. Accessed May 10, 2014 http://www.pmdigital.com/blog/2013/07/hashtags-as-big-data/

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New Media- Big Data. Amy McCarthy Week 10

It is not hard to see that new media is a powerful tool. It is a part of our lives now and fits in seamlessly, none more so than the internet. I myself spend most of my day somewhat connected to the internet, whether if be on Facebook, Instagram or checking emails. But it's what goes on behind the scenes that is interesting- everything I do can turn in to data that will benefit someone. What is more important though, is what is done with all that data. Coming to conclusions and making predictions is what can make a difference. Predictive data can affect your day in every way possible, from the way you drive, shop, study, vote, watch TV and communicate (Siegel, 2013). A lot of this is to do with marketing, and how consumers are targeting differently according to predictions. Marketing is benefitting hugely from collecting online data. Companies like Netflix show recommendations by making use of data collected from consumers. This way they can suggest what a consumer should purchase next (Shaw, 2014). This form of target marketing is extremely useful and can essentially turn a much larger profit. Similarly companies predict 'mouse clicks' of online advertising. Websites generally pay per click for the advertisements they display, therefore they predict which ad you're more likely to click on (Siegel, 2013). Whenever I'm on Facebook, Google or YouTube the only advertisements that seem to show up are for companies I am actually interested in. It's like they know me really well. You would think with all of this knowledge that companies would take advantage of it. But from my experience this isn't really the case. Companies like General Pants Co, Universal etc have all implemented 'data bases' that send you promotional emails. The idea of this is that every time you make a purchase online or in-store the data base would track your purchases- learn your favourite brands or styles then send you emails accordingly. Whether or not this has actually worked is unsure, but if the program was implemented correctly it could be a huge profit generator. On the other hand, insurance companies have the capabilities to predict if someone is likely to crash a car or injure themselves (Siegel, 2013). An insurance company called Allstate predicts bodily injury liability from car crashes based on the characteristics of the insured car which saves approximately $40 million per year. Obviously there are limits to the accuracy of these predictions, Jay Leno once asked "How come you never see a headline like 'Psychic Wins Lottery'?" (Siegel, 2013). Which is right- "prediction is very difficult, especially if it's about the future" (Bohr, unknown) because the future IS unknown (Siegel, 2013). REFERENCE LIST: -Shaw, J. (2014, March-April). Why “Big Data ” is a Big Deal. Harvard Magazine. Retrieved from http://harvardmagazine.com/2014/03/why-big-data-is-a-big-deal -Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc. -The Economist. (2012, June 26). What is Big Data? [Video file]. Retrieved from https://www.youtube.com/watch?v=ahZGEusG13A

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Week 10: New Media, Big Data and Telemetrics – Carissa Dalton

“While the audience may have fragmented, the mass audience still exists for those events that bind us together in space and time. Exploiting the continuing power of this immediacy will be the future of commercial television.” (Herd in Harrington, 2013).

Over the past decade new media has undoubtedly changed the media industry, particularly the television industry. The concept of technological convergence is central to the ways in which audiences engage with television content. Technological convergence has afforded consumers access to a digital library of television content that is accessible at any time across a wide range of catch up services.

Harrington addresses the idea that, in changing the way content is distributed, these services have radically changed how, where, and through what technologies television can be experienced, leading some to suggest that we have entered into a "post-broadcast" (Turner & Tay, 2009), or"post-network" (Lotz, 2009) era.

Conversely, it is argued that television “is neither beating nor losing to new media in some sort of cosmic clash of technology”, and in fact, the old and the new media are continually developing a mutually beneficial relationship (Gray and Lotz, 2012). So, it is justified to assess how and where the forces of convergence enhance television-viewing patterns and how the concept of “mass conversation” provided by social media impacts upon their collective use.

One of the most commonly used social media platforms that enhance the idea of “mass conversation” is Twitter. Harrington asserts that the platform affords users to connect with other viewers in real time to engage in a live, almost unmediated discussion. This form of dual engagement demonstrates that, contrary to the stereotypical belief, television is a highly social medium that brings people together around a shared point of interest (Lemish, 1982; Morley, 1986).

The Carrie Diaries

In 2013, the second season finale of The Carrie Diaries went to air with no confirmation as to whether a third season of the Sex & the City prequel would reach the production stage. Viewers were clearly motivated to push for the production of a third season and took to social media to ensure that they had their voices heard.  A petition was created on change.org, which allowed for fans to register their interest in a third season. Those who signed the petition were strongly encouraged to watch the season finale live and tweet at the CW Network accompanied by the hashtags, #TheCarrieDiariesSeason3, #WeWantTCDSeason3, #RenewTheCarrieDiaries and #SaveTheCarrieDiaries. While, The Carrie Diaries was cancelled on the 8th of May, the petition was then opened up to include potential buyers such as ABC Family, MTV and Netflix.

By the way that the television industry is continually developing in relation with social media, is it impractical for networks to assume that the production of quality content will be a determining factor in what programming audiences choose. It is quintessential for networks to establish and maintain relationships with viewers, and communicate connections that encourage routine and loyalty of viewers (Jones, 2012).

References:

Harrington, Stephen. 2013. “Tweeting About the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society, edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt and Cornelius Puschmann, 237-248. New York: Peter Lang.

Gray, J., & Lotz, A. D. (2012). Television studies. Cambridge, UK: Polity Press.

Jones, J. P. (2012). The 'new' news as no 'news': U.S. cable news channels as branded political entertainment television. Media International Australia, (144), 146-155.

Lemish, D. (1982). The rules of viewing television in public places. Journal of Broadcasting, 26(4). 757-781.

Lotz, A. D. (Ed.). (2009). Beyond prime time: Television programming in the post-network era. London, UK: Routledge.

Morley, D. (1986). Family television: Cultural power and domestic leisure. London, UK: Comedia.

Turner, G., & Tay, J. (Eds.). (2009). Television studies after TV: Understanding television in the post-broadcast era. London, UK: Routledge.