QUT KCB206 2014 Tutorial group 9

New Media Entertainment

Elana Shefrin’s article on participatory fandom and congruencies between the internet and media entertainment culture contrasted George Lucas’ Star Wars and Peter Jacksons Lord of the Rings online participation and showed how Peter Jackson was more successful for his fan interaction and consideration, stating,‘As Jackson’s and New Line Cinema’s strategies have shown, the active fan audience offers cultural producers the opportunity to forge strategic alliances that can democratically influence the power hierarchies in the artistic field and the larger field of class relations’. This study from 2004 was significant in showing how audience interaction on new platforms has become a significant factor within entertainment industries, encouraging a participatory culture (Shefrin 2004). This convergence of new medias is described as ‘a flow of content across multiple media platforms’, media audiences nowadays play a crucial role in creating and distributing content, and convergence therefore has to be examined in terms of social, as well as technological changes within the society.

An emerging trend that has arisen is with Crossmedia and Transmedia campaigns, which utilise numerous platforms to enable a campaign. Crossmedia is a mediaproperty, service, story or experience distributed across media platforms using a variety of media forms – the same content pushed across different platforms in specialised forms – typically involving audience interactivity. Transmedia is the technique of telling a single story or story experience across multiple platforms and formats using current digital technologies. Generally, it is used as an entry point into a story world and typically uses different dimensions, for instance narrative spaces, number and relative timing of platforms and audience interaction (Voigts 2013).

One of the most successful transmedia campaigns in recent years was for the Prometheus film, a prequel to the successful Alien series. The campaign started with a viral video called ‘TED talk 2023’ where the owner of Weyland’s corporation presented an idea about the androids during the TED (Technology, Entertainment and Design) conference. ‘TED talk 2013′ also led to www.Weylandindustries.comwebsite where a visitor was able to read more information on the company and become one of its investors. At the annual comic book, science fiction and motion picture convention called WonderCon Weyland employees handed out their business cards. After a call to the listed phone number, fans received texts with a second viral video about the newest company’s android called David 8. This video and some commercial advertisements on other products were also shown on TV and at www.Mashable.com which was dedicated to report news on digital innovations. When fans unlocked all information about the David 8 and other company’s products the Weyland company’s mission called ‘Prometheus’ was finally revealed. Moreover, ‘Prometheus’ offered vacancies for which applicants could be assessed by successfully completing an online game. Some information on this job opportunity was announced on a website for professionals calledLinkedIn. The third viral video presented Dr. Shaw’s ‘Project Genesis’ where she asked for a help researching it. Fans had to search for artefacts that would help discovering the Engineer’s planet by unlocking hints. The film release revealed a lot more plot although not the complete story and there are more films in the series to come (Changers 2012).

The campaign utilised Twitter, Facebook, LinkedIn, text messaging, read ‘Mashable’ and watch commercials commercials to engage audiences, resulting in a spread of audiences to 15-45 year old males and females, rather than a typical young male audience (Clarke 2012).

Burgess, Jean and Joshua Green. 2009. “You Tube and the Mainstream Media.” In YouTube: Online Video and Participatory Culture edited by Jean Burgess, Joshua Green and Henry Jenkins, 15-37. Cambridge, MA: Polity Press

Data. It seems like a boring subject and as mentioned by Eric Siegel in one of this weeks readings: “people couldn’t be less interested in it”. Although, he goes onto explain that data is what enables the collection of invaluable experiences from which we can develop and learn. In recent times the way in which data is collected has completely changed.

The rise of new media has shown that the television has become an important data-gathering tool. Harrington explains, “television has been well understood as a medium that readily catalyses audience discussion, interaction, fandom, and other social activity” (2013). This is especially true when looking at how modern audiences use Twitter to increase discussion and interaction with television programs. It is not uncommon to be watching a show and for the hosts to say something similar to “join the discussion on our Twitter page and hashtag us to have your say”. Perhaps statements such as these aren’t made so that viewers can interact, but so that television shows can gather data about its audience. Harrington is able to link these ideas: 

“Twitter therefore represents a phenomenal opportunity. It provides us with exactly such a means of inconspicuously observing the activities of television audiences. We are left with "material traces that are left of the practice of sense- making" (McKee, 2003, p. 15), which are created 'organically', as the research participants are generally unaware of their own participation in a research study” (Harrington, 2013).

I personally, am convinced that majority of the general public is not aware of the motives of television shows and their use of Twitter. New media platforms such as Twitter have allowed programs to inconspicuously collect data about their target publics. Whether this is profoundly bad or good is extremely unclear as like many new media concepts there are positives and negatives. While data is unknowingly gathered about everyday people such as you and I, we are ultimately the ones who decide whether or not to participate. We also decide whether or not to have public profiles on new media platforms such as Twitter. 

The convergence of Twitter and the television allows networks and programs to gain access to cheap market research. New media has allowed this type of data gathering to become widely used in today’s world. Harrington explains that we must “get to a position where this is a firmly entrenched and widely accepted (and respected) form of audience research”. Making audiences aware and familiar with this process will allow this type of data gathering to develop. Hopefully, in turn this will create a mutually beneficial relationship between television programs and its audience.

  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 

When thinking about big data, the bridge between IT and business (McVey, 2013), and how big companies make use of customers’ information to predict and influence their future purchase activities (Siegel, 2013), one example that comes to mind is Target.

Target wanted to establish themselves as the one stop shop for families, but they noticed it was really hard to change people’s habits once they get used to buying cleaning products at target and everything else at a supermarket or grocery shop. So they decided they needed to target consumers at a vulnerable time of their lives, meaning new parents. They realized new parents didn’t have enough time to go to so many different shops, and that if they wanted to acquire long term loyal customers, they’d have to target those new growing families (Duhigg, 2012).

One of their best statisticians, after months of analysing data, came up with a list of products that early expectant mothers would buy. Things like vitamins supplements, cotton balls and body lotion were on the list, and it proved to be over 80% accurate (Duhigg, 2012). Once such products were sold, shoppers information was retrieved either through their credit cards or through having them sign up for a coupon promotional campaign, where Target would mail customers with a list of discounted products to get them to come back (Golgowski, 2012).   

Everything was going well until one day when a middle aged man walked up to a Target store and asked to speak to management. He was rather annoyed about the coupons his fifteen year old daughter had gotten in the mail; Nappies, infant milk products, trolleys, expecting mother fashion items, and other such products were being explicitly advertised to a young girl. The manager apologised, insisted there must have been a mistake and a few days after the incident, called the father again to once more apologise. But this time the father was the one who apologised, saying he had just been informed his teenage daughter was in fact pregnant (Golowski, 2012).

This is one very interesting example of just how powerful big data really is. Activities and behaviours that might seem random can be analysed and used to find out some very personal information. After that incident, Target decided to change their strategy a little, seen that not everyone will be happy to know a company that big has access to such private information not everyone might like to share. What Target chose to do to address this problem is to include some random products such as lawn mowers and building tools in the coupon list, so the nappies and baby bottles will look just as random (Duhigg, 2012).

Big data allows companies to find out a lot more information than we think we put out, and companies have ways of using that information without being too obvious, while providing us with what we want and need before we even know those wants and needs actually exist. I believe the fact that the information is out there is not necessarily bad, but what can be bad is the lack of control we have over our own information and what others may use it for.

  Bibliography:

Duhigg, C. (2012). How Companies Learn our Secrets. Retrieved from: http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0

Golgowski, N. (2012). How Target Knows When its Shoppers are Pregnant – and Figured out a Teen was Before her Father Did. Retrieved from: http://www.dailymail.co.uk/news/article-2102859/How-Target-knows-shoppers-pregnant--figured-teen-father-did.html

McVey, I. (2013). What Is Big Data?. [video] Available at:https://www.youtube.com/watch?v=9cRAdB9eriY 

Think back to your early memories of television. What kind of role did it play in your life? What place did it have within your family and social life? For instance, were there certain shows that everyone would gather round to watch? There may have been shows that you would discuss with friends the day after each episode, to offer opinions, reminisce the fun or excitement, and debate what the next episode might entail. To some extent, we all (hopefully) remember the televisual experience before the arrival of Web 2.0 - a time when the only way you could watch any desired show was to tune in whenever it was scheduled, and if you missed it: too bad. Now consider the way you watch television today.

We, as consumers of televisual content in the modern age, are faced with a myriad of new ways to catch our favourite shows, interact with other fans, and even interact with the shows themselves. One of the most important facts is that we no longer have to submit to the scheduling made by television stations. We can access the same content in an instant from various sources: torrenting sites (illegally), catch-up sites dedicated to those who missed a scheduled show they wished to see (such as ABC iView, Plus7, etc), or buying/renting online (through apps like iTunes). These new affordances have led some to suggest that we now live in the ‘post-broadcast’ or ‘post network’ era. (Bruns 2013, 238)

The flip side to this coin is the change brought about by social media - where viewing of scheduled shows becomes a social activity undertaken by potentially millions of viewers in real time, all connected to each other via the internet. This is facilitated by social media sites such as Twitter, which allows viewers to post comments in certain ‘threads’ relating to a particular show, creating a virtual, communal conversation that occurs during the show. “Each user can engage in a running commentary or conversation on what they are seeing ... as it happens on their television screen.” (Bruns 2013, 240) Where once we had a lounge room with maybe a few family members or friends watching a show together, we now have a ‘virtual lounge room’ with the potential to include the entire world. (Bruns 2013, 241)

Check out this short video, which gives an quick but informative look into the world of ‘Social TV’:

http://www.youtube.com/watch?v=kA--f052lvw

Personally I’ve never really been a part of one of these virtual lounge rooms, mainly because I've just never delved into Twitter, but I think it’s a fantastic way for audiences to connect with one another in a way they never could. I can also really see the appeal in it, because while I don't watch a great deal of television shows, when I do I always enjoy the realisation that many people all across the country are doing exactly the same. So being able to connect with them as the show unfolds seems like a bit of a gift. Bruns (2013) discusses how social media has become a complimentary element to scheduled television - used for voting in competitive shows like The Voice, or for people to have their opinions shown on screen (such as in ABC's Q&A) to name a couple. I agree with him, and don't think that scheduled television is going to be replaced by other methods of consumption anytime soon.

References:

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

Reuters TV. 2012. “Social TV: The future of television?” Youtube video, posted January 3. Accessed May 8, 2014.

In an age where we are more inclined to watch an entire season of our favourite programs in one sitting, and where the race against the clock to be home in time for its airing on television is just a distant memory, it appears that the small screen we’ve grown up in front of is dying a slow, painful death. Business Insider (2013) paints a grim picture- “The TV business is having its worst year ever. Audience ratings have collapsed: Aside from a brief respite during the Olympics, there has been only negative ratings growth on broadcast and cable TV since September 2011.” And it’s no surprise. With Netflix, online streaming, and legal and not so legal downloading prevalent in our lives, television series are becoming more and more accessible on our computers, laptops and smart devices, meaning there’s no reason to tune in at the allotted time on your local television network, all you have to do is log in and an entire library of programs and seasons is quite literally at your fingertips.

However there is a solution. Twitter.

Stephen Harrington (2013) found that it is estimated that 60% television viewers engage with social while simultaneously watching their favourite programs) and the use of social media to enhance the audiences enjoyment of these programs can result in return, and ultimately loyal, viewership.  Harrington goes on in stating that “TV can be enhanced when experienced alongside others” and that ultimately social media can fill this desire, creating conversations and excitement about a program long after the credits have rolled.  Producers have caught on to this idea and now many have programs have a strong twitter presence, creating hashtag handles to make following the interactions easier and allows users the ability to have their opinions contribute to a larger conversation.

A recent example of would have to be the battle of reality tv shows including My Kitchen Rules, The Block and The Biggest Loser in which My Kitchen Rules has undoubtedly been the most successful, in terms of television ratings widely thought to be due to successfully capturing a social media audience, clearly winning the battle for the Twitter audience on premiere night, and maintaining a lead over both The Block and The Biggest Loser since then. (Woodford and Prowd, 2014)

  http://mumbrella.com.au/twitter-graph-202662

 This “capturing” can be attributed to the introduction a live tweet feed during their broadcast, something many reality series are swiftly adopting, selecting tweets relating to the events occurring in the episode in real time. Which not only encourages and invites followers to watch the program and contribute in hopes of seeing their tweet featured but also creates the sensation of group enjoyment mentioned earlier.

Not only do audiences benefit from this fresh form of engagement and interaction. The use hashtags gives networks and data analysts offers sought-after big data and telemetrics (Woodford, 2013), using methods to evaluate performance such as the Twitter Excitement Index, which measure the volatility of Twitter discussion throughout a program and allows comparison between shows and genres, an invaluable tool for countless media industries (Woodford, Prowd, Bruns, 2014).

So TV may not be looking to well but it’s certainly not dead, and social media may be just the life-saving injection it requires.

REFERENCES

Woodford, D, Prowd, K & Bruns, A. n.d.. “Telemetrics: Towards Measuring Social Media Engagement with Television” Accessed May 11, 2014. http://blackboard.qut.edu.au/bbcswebdav/pid-5234702-dt-content-rid-2118244_1/courses/KCB206_14se1/Woodford%2C%20Prowd%20and%20Bruns%20-%20Telemetrics%20Towards%20Measuring%20Social%20Media%20Engagement%20with%20Television.pdf

Woodford, Darryl & Prowd, Katie. 2014. “My Kitchen Rules pair are all the rage on social media – for now.” The Conversation. Accessed May 11, 2014. https://theconversation.com/my-kitchen-rules-pair-are-all-the-rage-on-social-media-for-now-22830

Darryl Woodford.2013. Mapping Online Publics. “Australian Reality TV on Twitter a Two Horse Race”. Accessed 10 May 2014.       

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.

“Social media is increasingly playing an important role in television; playing a key role in the promotion of upcoming shows, being utilised for on-air interactions with the audience across a variety of genres, and also as a tool for measuring the success of a show, at least with the audience segment which social media represents.” (Woodford, Prowd & Axel, n.d.)

The introduction of social media has made its way into many aspects of our lives. We use it as a tool for sharing whether it be for our person lives or business. This form of networking on a global scale allows us to share with others who hold similar interests. An example of this is the excitement and interest of popular television shows. One of the most popular social media networks for this type of sharing is Twitter. Twitter introduced the practice of adding ‘hashtags’ to tweets on their network. “A hashtag groups tweets together on a particular subject. Hashtags are user generated and their popularity has grown organically to become an integral part of Twitter culture” (Cowan, n.d.).

The activity on these social networking sites can be particularly useful to production companies and networks of television shows. This kind of information is what is known as big data. Bertolucci (2013) gives us a practical definition of big data and believes “big data is about building new analytic applications based on new types of data, in order to better serve your customers and drive a better competitive advantage. “Big Data is a currency across industry, as well as the sciences and social sciences” (Woodford, 2014). Big data is helping provide companies with an informative approximation of how their content is being perceived by audiences (Woodford, 2014).

The use of hashtags on twitter is described by Stephen Harrington (2013) as ‘Layers of communication on Twitter’. Harrington further describes this as “the key modes of communication on Twitter are linked to the specific technological affordances of Twitter as a platform, and can be understood as corresponding to micro, meso and macro layers of information exchange and user interaction”. The meso layer determines the exchange of information between follower- followee networks. Micro layer the reply of conversations takes communication on Twitter from the meso to macro layer. The Macro layer is that of hashtagged exchanges, users create a relating hashtag to their tweet, which then links their content and makes it discoverable by other users (Harrington, 2013).

Although this sort of data collection is still only as mentioned above an informative approximation of how content is being perceived by audiences (Woodford, 2014). Though like many innovations of new media with further development it could potentially improve into an essential form of data collection.

References:

Cowan, A. n.d. “Twitter Tip: What is a hashtag (#) on Twitter?” Alicia Cowan Blog. n.d. Accessed May 11, 2014. http://www.aliciacowan.com/social-media-and-digital-marketing/twitter/twitter-hashtags

Harrington, S. 2013. “Ch 18 Tweeting about the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt & Cornelius Puschmann, 237-248. New York, NY: Peter Lang.

Subramanyan, V. 2013. “Twitter buys social TV Tracker Trendrr”. Image. Accessed May 11, 2014. http://www.business2community.com/trends-news/twitter-buys-social-tv-tracker-trendrr-0599596#!Mc5Nw

Woodford, D. 2014. “KCB206 Internet, Self & Beyond: Week 9 New Media, Big Data & Telemetrics.” Accessed May 11, 2014. http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

The emergence of big data and how its used has greatly changed business, in terms of analysing markets and prediction. It has been found that many people have many different ideas about how big data is defined, but basically it rounds down to the idea that it is enormous amounts of data that we use to draw conclusions about many different things like consumer behaviour and target segments. Ian McVey suggests that big data is the bridge between IT and business basically stating that businesses use this information and turn it into knowledge about how we consume (McVey, I. 2013). Ultimately everything we do online, shopping, tweeting, messaging, reading news article, using apps and watching TV all contribute to big data. Big data is seen as a currency to corporations, to gain a greater insight to people than has ever been possible. To put into perspective how much data exists nowadays; McVey says that in the past two years more data has been created than in the whole of human history (McVey, I. 2013).

In order to gain an understanding of what consumers want, retailers use big data to see what sort of products and feedback are posting online and integrate this into their pricing strategies, products and marketing. One of the best examples of this is retail giant Macy’s. Macy’s with the help of Statistical Analysis Systems (SAS) enhanced their customer insight to effectively measure and understand the impact of its online marketing initiatives on online sales in order and create  better online shopping experience.  They used big data to find how effective  their email campaigns were, finding how  often an email would trigger a consumer to click through to the site and purchase something.  This lead to Macy’s discovering what type of emails were effective for each customer, leading them to be able to create individual emails to suit each customers needs. Spokesperson from Macy’s stated that their customers share a lot of information with them, therefore finding effective improvements to their campaigns was easy. The use of big data in this case led to Macy’s reducing the emails they sent out by 20% but increased the success of their emails by 101% (Sas.com, 2014). This is an example of what Eric Siegel discusses in his book, The Prediction Effect, when he discusses the idea of companies literally predict how to best influence you to buy things (Siegel, E. 2013) .

  Something that I found interesting is the way we use big data in everyday life. For example, Google use big data in a number of ways to give us an insight into what is trending and even predict when we are due to get a cold. Google uses aggravated search data to estimate when there will be a flu outbreak in your area, basically every time someone searches for health information about the flu, they are contributing to Google’s graph tool.

  Everyday we contribute to big data, most of us, without even realising it. Big data is one of the fastest growing markets and its services are considered to be an effective multibillion-dollar opportunity, with the ability to predict trends and consumers actions before we even think about consuming.

  REFERENCES –

Google.org, (2014). Google Flu Trends | Australia. [online] Available at: http://www.google.org/flutrends/au/#AU [Accessed 11 May. 2014].

McVey, I. (2013). What Is Big Data?. [video] Available at: https://www.youtube.com/watch?v=9cRAdB9eriY [Accessed 11 May. 2014].

Sas.com, (2014). Macy's Knows What's In Store For Customers | SAS. [online] Available at: http://www.sas.com/en_us/customers/macys.html [Accessed 11 May. 2014].

“Ethics is the branch of philosophy dealing with values relating to human conduct, with respect to the rightness and wrongness of certain actions and to the goodness and badness of the motives and ends of such actions” (Dictionary.com, 2014).

  In the last two decades the world has witnessed a rapid transformation of traditional media into new media that incorporates digital, computerized, and networked information and communication technologies.

  This shift has raised concerns and discussions around the positive and negative implications of the new media, and other issues such as: control of information, volume and speed of communication, the public sphere, and the global influence of media corporations (Eid. Ward, 2009).

  Social networking sites, video sharing sites, wikis, blogs, and a whole bunch of others, have evolved as a result of Web 2.0 concepts and new media technologies (Eid, 2009). Millions of people around the globe, through social networking sites being accessed on laptops, computers, tablets and smart phones, are recently building online local, regional and global communities to communicate their shared interests and activities and to also broadcast information and interact through a variety of web-based tools.

  The way people use new media and social networking sites like MySpace, Facebook, YouTube, LinkedIn and Twitter do however present implications for our society, our culture and our politics which has led many investigations into a variety of issues and many related issues such as: social identity, privacy, distance learning, social capital, socio-psychological effects of the web, misuse of cyberspace, diaspora, social status, and access to information (Ward, 2009).

  There has already been numerous incidents of misconduct on new media tools that have led to negative and harmful situations such as: political deception of voters, suicide, slander and breach of privacy, cyber-crimes, cyber-bullying and the list goes on.

  Not only does the issue of ethics affect every day users, it also affects people’s careers such as journalists in particular who rely heavily on sharing and communicating information through the Internet and new media tools.

  The growth of new media means that journalism ethics now has to undergo a difficult transition from a traditional, professional ethics to a mixed media ethics. Both professional and citizen journalists now struggle to re-invent media ethics, and there has been debate on how to adapt existing norms such as accuracy, verification and objectivity to the new media environment (Salcito, 2009).

  “Ethics and social responsibility are key fundamentals to the effective performance of new media” (Eid. Ward, 2009) and a great example of new media ethics is the case of our very own Julian Assange and his new media website, Wikileaks. WikiLeaks has been criticized before for revealing sensitive information.

  For example, the organization published thousands of reports about the Iraq War and even though much of the information was already known in broad terms, many of the specifics were not. WikiLeaks also released classified U.S. military footage from an attack in July 2007 in Baghdad by a U.S. helicopter that killed a dozen people including two journalists. For these leaks and others, many have praised the organization. However the questions of ethics especially in the case of Wikileaks is always a tricky topic as some people may think its ethically wrong to release private information but other may think that ethically it is their right to know about the private information so therefore some of what WikiLeaks does is ethical, and some is not.

  According to Pavlik (2012), “Ultimately, democracy and society depend on an ethical media system.” In the digital age, an increasing portion of the community is actively engaged in media systems and sites as a consumer or producer, whether its via news, social media or entertainment (Pavlik, 2012).  In conclusion, I believe each person’s actions, or inactions on new media are part of a global network and can contribute to making it an ethical digital media system.

  Reference List

Dictionary.com. 2014. “Ethics”. Accessed May 4, 2014. http://dictionary.reference.com/browse/ethics.

Eid, Mahmoud. Ward, Stephen. “Editorial: Ethics, New Media, and Social Networks.” Retrieved from http://www.gmj.uottawa.ca/0901/v2i1_eid%20and%20ward.pdf.

Pavlik, John. “Concept: Ethics in the Digital Age.” Retrieved from http://www.mediaethicsmagazine.com/index.php/browse-back-issues/144-fall-2012/3998645-concept-media-ethics-in-the-digital-age. 

Originally in the marketing industry, companies would develop products and services with the intent to market to every possible consumer. Through research however, these companies discovered that pursuing only segments of the entire consumer spectrum, found to be far more cost efficient and cost effective for them (Riley, 2012). Now, this process of understanding the consumer has become much easier with the development of machine learning, or more widely understood as predictive technology. Machine learning is a computer's way of automatically developing new knowledge by number-crunching and analysing endless amounts of raw data to build upon insights with predictive capabilities.

In the mediascape, machine learning has been a valuable tool in the prediction of blockbusters for Hollywood's film industry. Google has suggested that, by crunching aggregate search queries and other datasets, movie studios can predict how well their films will perform ahead of time (Kolakowski, 2013).  In other words, box-office performances are usually determined by the amount of movie-related searches that occur online prior to screening. The figure below shows the relationship on a scatter plot graph. This algorithm usually has an accuracy of around 70%, but throw in the number of clicks per paid movie advertisement along with other factors such as the status of the franchise, and the accuracy of such predictions can increase to over 90%. Not only short term, but long-term predictions can also be calculated through critic review scores and theatre seat counts from previous weeks.

Although predictive technology is incredibly useful in aiding media companies with specific data to hone in on the intricacies of their current, and may I add, possible consumers, I can't help but to see other areas outside the media sector affected by this clever engineering. The justice system is an area that has computers programmed to understand and interpret human behaviour faster than we can ourselves. I don't believe it's too far fetched to say that computers will eventually be able to incriminate humans. Currently, polygraphs and microexpression readers are used, but they yield only semi-accurate results, and are inadmissible as evidence in court. However, technology is becoming more intelligent and producing more thourough data, such as equipment that tells if you are cheating on taxes, or computers that scan your brain and tell you what kind of object you're thinking about (Siegel, 2013). This said, it is only a matter of time before data will be accurate enough to count as evidence, and it will be our word against an objective, emotionless machine that supposedly knows us better than we do. Scary.

_________________________

Resources:

Jim Riley. 2012. Why Segment a Market?. [ONLINE] Available at: http://www.tutor2u.net/business/marketing/segmentation_why.asp. [Accessed 10 May 14].

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. 

As the internet evolves, so does the way we interact and consume various forms of media. In today’s society, we are more inclined to participate in a television show forum through the use of Twitter and hashtags. This change has not been unnoticed with major television show producers taking huge advantages of the successful engagement. This phenomenon is knows at the ‘Second Screen’ and is seen to be revitalizing the way television is consumed in the new digital world (Segura, 2014). This phenomenon has only really come to the forefront of media technologies after the rise of smart phones with internet capability. This is because of the ease of publishing through these devices.   

Many people often underestimate the power of Twitter. It could be said that Twitter can be seen as the new television remote with a study by Neilson revealing a correlation between and increase in tweets and a growth of audiences for that particular television show (Segura, 2014). Director of Media Partnerships of Twitter, Danny Keen, says that television is alive and thriving because of Twitter. This allows many programs, as we will see below, to harness the popularity of social media for further engage their success.

One show that has been particularly successful on Twitter is American ABC Family’s Pretty Little Liars. Many episodes of Pretty Little Liars has been extremely successful with the Twittersphere with the show holding top prize for the most-tweeted season premiere on television (Deadline, 2014). This reached almost 1.3 million tweets by over 700,00 contributors. However, by using the hashtag #WorldWarA they were able to further build anticipation and excitement for the upcoming finale (Myers, 2013).

Further, they implemented a different hashtag to be used during the show airing. This hashtag, #PLLReaction, gave fans a call to action to immediately post their reaction face to the twist in the story line (Myers, 2013). This prompted a huge rise in the amount of contribution the hashtag was receiving and also allowed the hashtag to ‘trend’ on Twitter.

(main character, Lucy Hale, posting her #PLLReaction)

However, Pretty Little Liars is only one example of a successful social media campaign and is only the base of what can be seen in the future. We can see that Twitter and television play an important role in each other’s existence. The use of the two platforms conjunctively can completely change the way television and twitter is consumed.

Deadline. (2014). “Pretty Little Liars Winter Debut Most Tweeted Season Premiere in History” Accessed 11 May 2014. http://www.deadline.com/2014/01/pretty-little-liars-winter-debut-most-tweeted-season-premiere-in-tv-history/

Liz Myers. (2013. “Pretty Little Tweeters; How the finale beat the records” Accessed 11 May 2014. https://blog.twitter.com/2013/pretty-little-tweeters-how-the-finale-beat-the-records

Week 9: New Media, Big Data and Telemetrics – How can television shows harness the power of new media?

A couple of weeks ago I made a blog post about how fandoms pressured entertainment studios into resurrecting characters by using social media. It was power that audiences never had before: the power to give the entertainment studios instant and detailed feedback and opinions. Fast-forward to now, and entertainment studios are using social media to market their products and test audiences for interest in new products. They’ve realised how good of a tool that new media can be for their own products.

Television networks can instantly get feedback on shows and encourage new people to watch their channels much more easily thanks to new media. To harken back to the fandom blog post, just look at Agents of SHIELD. It’s frequently posting teasers, posters, trailers, and news through its Facebook, Instagram, and Twitter pages and getting likes and comments from across those networks. The amount of likes and comments become feedback. If Agents of SHIELD or any of its marketing taglines suddenly starts trending as a hashtag, it’s because the show is generating interest. Of course, because Twitter hashtags are regional and Agents of SHIELD is so centred on American audiences, we can’t really witness that here in Australia.

But I digress; when Agents of SHIELD first kicked off late last year, it had a lot of disappointed fans taking to the internet to voice their opinions on the show. It was dull. It lacked superheroes. It was cheesy and low-budget. When fans spent so many years getting used to high-budget action-packed Marvel movies focusing on superheroes, they expected more of that in a television setting with Agents of SHIELD. As the fans took to Twitter, the television executives started taking notice. To protect their Nielsen ratings scores (because that is what TV investors and network executives still prefer over new media feedback), Agents of SHIELD frequently took breaks between episodes – the Christmas break and the Winter Olympic break were all used to stop traditional ratings scores from plummeting and getting the show cancelled. Fans voiced their displeasure about this too, of course. The television network executives came back after those breaks with an almost-new version of the show: plenty of action, plenty of superpowers, and much higher production values combined with a lack of breaks meant that viewers are much happier with the show now and ratings have stopped declining.

This change came about through feedback that couldn’t have been gathered through Nielsen ratings or old media – new (social) media gave viewers avenues to actually talk about what they’re thinking. This level of interaction from Facebook statuses, Twitter posts, and internet trends helps to sustain a show’s popularity and help make the show become better, as proved through Marvel’s Agents of SHIELD.

References: 1. Introducing “Telemetrics” | Darryl Woodford. 2014.[ONLINE] Available at: http://www.dpwoodford.net/introducing-telemetrics/. [Accessed 11 May 2014].

2. Australian Reality TV on Twitter: A Two Horse Race | Mapping Online Publics. 2014. [ONLINE] Available at: http://mappingonlinepublics.net/2013/08/13/australian-reality-tv-on-twitter-a-two-horse-race/. [Accessed 11 May 2014].

Man generated 5 exabytes of data up until 2003 (Intel 2014). In 2012, data grew 500x more data than all data ever generated:  predicted to grow 3 times bigger by 2015 (Intel 2014). The internet has facilitated measurable and organised approaches to harvesting this data. Big data (as it’s called) is bought, sold and traded as the currency of modern business (D Woodford 2014). As such, we must readdress notions of fact and fiction, as more businesses creep into our daily lives. How big data is utilized to generate knowledge? What industries are capitalizing over others?

If you know what to look for, you can make the most of big data (G Bell 2014). In this respect, data is in the eye of the beholder, not the machine that processes it. Part of this process means understanding the core components of facts, analytics, visualisation and algorithms (D Woodford 2014). Facts are complicated, and always partial from the start (G Bell 2014). In fact, when I signed up to Myspace, I was technically too young to use the site. My friends and I claimed to be obscure ages such as 105, which was the case with 15% of users (J Bullas 2014).Similarly, power distances become important in how we determine fact from fiction. The context and relationship with another (person, organisation etc.) encourages us to think differently about the data we provide.For example,I'm probably going to be honest when talking about new work policy opinions to other staff, probably not when talking to the manager.According to Mark Vincent (from Shelston intellectual property) Data comes with responsibilities and challenges, which calls to mind the position of the Government on issues such as privacy and misleading information (2014).

Visualization provides new frameworks for presenting facts, while analytics means discovering and communicating meaningful patterns (D Woodford 2014). As facts move along the chain, we start to inject our own opinions, interpretations and ideologies. This means data continues to change, especially in the algorithm phase (G Bell 2014).  In the digital age, in such a cluttered environment, which facts are the most relevant to study? Social media is a clever tool for tracking customer buying, viewing and consumption habits to determine a brands impact. Hashtags can work as markers of a topic, issue or event, allowing data to be organised in online communities (Bruns & Moe 2013). The point is here, that algorithms are about making comfortable, preferable truths that we can handle and resonate with. When you type a search into google, it automatically populates the rest of the answer (Siegel 2013) just as netflicks recommends movies based on your personal data (G Bell 2014).

(screenshot: google, 2014)

Humans hate change and the feeling of cognitive dissonance. These recommendation algorithms are successful because they capitalize on the familiar (Intel 2014). The content television ecosystem is well aware of this. As such, there are more housewives than ever, with reality TV booming. Game of thrones producers were clever, they saw opportunity for narrative based drama, and it became the most popular show released in 2013 (IMBd 2014).

(game of thrones, 2014)

Only 10-15 percent of organisations fully capitalize on big data (Gartner group 2012), and will outperform competitors by 20% (Gartner group 2012). So, there is potential to gain insight and competitive edge from big data. Innovative approaches are required to sift through mountains of data to uncover the golden nugget of knowledge and insight. The challenge is to use algorithms so they deliver familiar and novel, exciting experiences. Ultimately, technology doesn’t make facts; it can’t control emotions, as technological determinism would beg to differ. Humans are the ones in control, and we are only limited by our own intellects and imaginations.

  Bell, Genevieve. 2014. “The secret life of big data” Inside HPC accessed 07/05/14 http://insidehpc.com/2014/02/23/video-intels-genevieve-bell-keynotes-sc13/

Bullas, J 2014. “22 social media facts and statistics you should know” Jeffbullas.com Accessed 29/04/14 http://www.jeffbullas.com/2014/01/17/20-social-media-facts-and-statistics-you-should-know-in-2014/

Gartner Group, 2012. “The importance of big data: a definition” Gartner Accessed 02/05/14 https://www.gartner.com/doc/2057415/importance-big-data-definition

 IMBd, 2014. “Most popular TV series” IMBd accessed 29/04/14 http://www.imdb.com/search/title?title_type=tv_series

Intel, 2014. “How Big Data works” Intel IT centre accessed 07/05/14 http://www.intel.com/content/www/us/en/big-data/unstructured-data-analytics-paper.html

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

Vincent, M. 2014. “Australia: the application of traditional legal rights in a big data world” Mondaq Accessed 07/05/14 http://www.mondaq.com/australia/x/290666/Copyright/The+application+of+traditional+legal+rights+in+a+big+data+world

(Lewis and Partners, 2012)

“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 genres, and also as a tool for measuring the success of a show, at least with the audience segment which social media represents.” (Woodford, et al 2014, 1) While many users may not know it, they are actually creating data when they tweet about their favourite TV shows using relevant terms or hashtags. The utilization of this “big data” is termed by Woodford as being television metrics or “telemetrics”, “the application of Sabermetric principles to television ratings, and more immediately the use of social media to promote, and enable interaction with, television networks and franchises.” (Woodford, 2014).

(Teen Wolf, 2014)

Big data can be utilized by market researchers regarding TV engagement. For example, television shows have flocked to Twitter as a way to engage with their audiences and promote their respective programs. Teen Wolf engages its audience through various online platforms. It has its own Tumblr page through which they upload and reblog content as well as interacting with fans; the Twitter page does the same things but with the organizational element of hashtags. At the moment they are using #Season4Alphas as a hashtag on their official Twitter page for users to nominate their friends to be “Alpha of the Week” (MTV Teen Wolf, 2014).

(New Girl Facebook, 2014)

New Girl utilized the same strategy with Facebook – a “fan of the week” spotlight was enlisted with the possibility of people who mentioned the show on their Facebook could be featured on national TV (Darling, 2012).

In the case of TV programs, official hashtags cause “more posts to aggregate” (Darling, 2012) and therefore collate data useful for market researchers and the networks to inform themselves on trends and opinions of the respective show.

(Proulx, 2012 - Yellow is #Glee; red is #saturdaynightGLEEver)

The TV show Glee which usually has an official hashtag of #Glee spurs around 75,000 tweets per episode but still alters Twitter hashtags trends; this is evident through new hashtag #saturdaynightGLEEver which aired during an April 2012 episode and was employed by Twitter users over 8,500 times (Darling, 2012).

Harrington (2013) states that "TV can be enhanced when experienced alongside others” and describes social media as acting as a virtual lounge room for audiences to share their experiences of episodes. Hashtags enable users to find and contribute to conversations they are interested in and link their contributions to a wider discussion. It is arguable that networks and data analysts benefit more from the use of hashtags as it provides them with marketable big data and information on the viewing habits and opinions of unsuspecting audiences – it essentially harnesses “Twitter as a means to inconspicuously observe the activities of TV audiences” (Harrington, 2013). However, audiences do benefit from the collation of hashtagged conversations as it makes those respective conversations easier to find and contribute to.

Data gathering has been around for some time now. As our social and private worlds become effervescently integrated through online social mediums our irks, quirks and personal views of the world become public knowledge.  This public knowledge, left at the ready hands of big brands, corporations and governing bodies, allows what is known as ‘Big Data’ to serve as a form of currency, allowing the determination and prediction of trends, social behaviours and biased ‘caretaking’ laws to be set in place.

One of the concurrent collators of big data lives in the 140 character world of Twitter. Known for its use of creating ad hoc publics by grouping hashtags, Twitter easily and accessibly coordinates the collective discussions based on trending topics created through the micro/meso/macro social layers (as discussed in last week’s blog post). Although it is arguably only one form of online social media, it is more frequently than not used as a linking tool for other Medias such as YouTube, aptly serving as a short and sweet connective player highlighting trends.

The use of big data serving in the efforts of helping politicians gain sway as elections come and go is no different. Unsurprisingly as the media revolution took off persons with political prowess determined to grasp the support of civilians took to implementing social media accounts to not only connect more personally with their audiences, but to find insight into what the people truly want. As an example a large amount of analysts believe that Barrack Obama’s political win was largely due to the online strategy that he had implemented. Australian politics however, seems to run by what’s trending with the links to persons in political power and the amount of their Twitter followers becoming all too eminent,  disclosing that we are only really interested in what is happening with certain politics when they are the ones in charge of ruling our country, or, are involved with and entertaining scandal.

Here’s where telemetrics comes into play, in particular looking at entertainment for entertainment’s sake. Overseeing the received information garnered through online mediums through Twitter hashtags, Facebook posts and reposts, entertainment industries are able to measure what is known as the ‘Excitement Index’  (Burke) of a televised show.  Of course the diverse formats make truly accurate readings nearly impossible, with the variations of television shows that ask viewers to Tweet their thoughts as the show airs e.g. Big Brother as opposed to shows that provide prompts to tweet thoughts at certain segment points such as The Project. The excitement index however is able to give an oversight as to what shows are popular, what topics gain momentum and allow insight for future predictions of televised success.

Even with the attributions of excitement index calculations in place to monitor data for programs, Australia still falls behind the technological revolution, often favouring Facebook over Twitter posts.  Where in the US 95% of interactive engagement comes from the Twitter sphere (Bluefin), Australia adopts a more even playing field with just over 53% of the population favouring Facebook over Twitter (Woodford). This makes the collection of data more obscure as different posting mediums procure different posting habits that might not allow for the collection of an ad hoc public through the use of hashtagging.

  REFERENCES:

Alex Bruns. 2013. Mapping Online Publics. “How Australian Politics Gained their Twitter Followers”.  Accessed 11 May 2014.

http://mappingonlinepublics.net/2013/07/08/follower-accession-how-australian-politicians-gained-their-twitter-followers/

  Darryl Woodford.2013. Mapping Online Publics. “Australian Reality TV on Twitter a Two Horse Race”. Accessed 10 May 2014.

http://mappingonlinepublics.net/2013/08/13/australian-reality-tv-on-twitter-a-two-horse-race/

  Marketing Charts. 2013. “Twitter Overtakes Facebook as Teens’ Most Important Social Network”.  Accessed 10 May 2014.

http://www.marketingcharts.com/wp/online/twitter-overtakes-facebook-as-teens-most-important-social-network-37352/

  Tumasjan, Andranik and Timm O. Sprenger, Philipp G. Sandner,  Isabell M. Welpe. 2014. Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. Accessed May 11 2014.

Week 9 Review

  This week’s readings collectively discussed telemetrics and the idea of “big data”. Whilst this topic is known to be a little dry, I think Stephen Harrington’s work about the way Twitter is shaping the television industry from the book “Tweeting in Society” was quite insightful. Stephen talks about how television is such an ingrained part of our culture nowadays, but rather than dying out or becoming “displaced” at the inclusion of newer medias such as the internet a type of ‘symbiotic relationship’ has been formed.

  It’s referred to as a ‘virtual lounge room’. The combination of tweeting about a television show in real time as it happens (Harrington, 2013). Being social creatures when something important happens in our lives, we flock to social media to ‘share’ the news with our ‘friends’. Like when we finally found out Emily’s shooter in Revenge. When Tim beat Jade in Big Brother. When Sherlock died and everyone had a conspiracy theory as to how he survived and you desperately had to share yours with everyone because if you’re right it could potentially be the most satisfying event to ever happen in your life… you get the idea. A perfect example about how these kinds of situations come about is the televised movie “Sharknado”. Two hours prior to Sharknado, almost no one had even heard of the program, never lone knew that it was premiering. Through the power of Twitter the program gained traction and suddenly the movie had over a million views and more than 400,000 tweets that night (Rubin Report, 2013) (Jones, 2013).

  Now while we may have been using our ‘virtual lounge rooms’ for quite some time now there has never before been a way for marketers to use this generated information for informative purposes. Introducing the hashtag. The hashtag allows companies and television networks to see whether people are watching their program and what sort of conversational noise they are generating. Is it a discussion about the characters, or did the editors of the program miss something subtle that happened in the background of an episode. Television stations are now able to conduct simple quantitative market research that was never possible before (Harrington, 2013).

    Harrington talks quite extensively about this topic in the reading commenting that when a focus group or interview is conducted on people’s television habits they subconsciously act unnaturally. “In order to achieve the ‘ethnographic’ goal and understand the ‘everyday’ affective practices through which [television] is experienced” then we need systems that allow us to understand the meaning-making processes that the audiences engage in, even while they are not necessarily conscious of their being ‘observed’ by a researcher” (Harrington, 2013). A basic example of this comes from the Marketing Research Company Nielsen who last year reported that people were watching on average 4.5 hours of TV a week, compared to the measly 2 that other competing companies had reported (Whestphal, 2014). However Nielsen was in fact correct having set up timers and hidden cameras to accurately record their findings, while opposing companies simply asked consumers in a questionnaire.

  The use of the hashtag on Twitter eliminates this inconsistency, now there’s no stopping businesses completing accurate secondary marketing and audience research. And once they’ve analysed the Twitter findings, if they still wish to conduct more research at least they have a solid foundation from which to ask knowledgeable questions that they know are relevant.

  Finally, Harrington acknowledges that indeed there is still a long way to go in the acceptance of Twitter collected trends as accurate audience research material but once that convergence happens I believe then we may truly benefit from our virtual lounge room.

    Reference List

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.

Rubin Report. 2013. “Twitter Buzz Boosts TV Ratings.” Youtube video posted August 8. Accessed May 6, 2014. https://www.youtube.com/watch?v=IxTdaK1r8Bc.

Jones, Allie. 2013. “Not Many People Watched Sharknado after All”. The Wire, July 12. Accessed May 5, 2014. http://www.thewire.com/entertainment/2013/07/sharknado-ratings-are-and-all-twitter-just-smidge-television/67115/

TV and Twitter 

http://www.opusfidelis.com/uploads/images/Twitter-TV-Ad-Dashboard.png

Thanks to technical convergence we now have access to a variety of different media content on a range of different technological devices. The way we now view television is a perfect example of this convergence. There was once a time, not so long ago, when you had to wait for the day and time a show that you wanted to watch was on television to view it. Those days are now over. Aside from online illegal viewing, many television channels are now providing their viewers with online ‘catch up’ versions of their programs (Harrington, 2013). Viewers can now watch a show when they want to and on what device they choose, as long as they have an internet connection. Television channels have almost been forced into ‘going online’ in order to keep up with this convergence and to keep satisfying viewer needs, but also to try and partly counter the illegal online options available. “In changing the way content is distributed, these services have therefore 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” (Harrington, 2013). 

It is debatable whether technical convergence will replace normal television viewing, but at the moment we are seeing ways in which it is enhancing it. Through social media and in particular Twitter, television audiences can now experience mass conversation (Harrington, 2013). Twitter and Television have made a special connection (Twitter, 2011). “Broadcasters are eager to provide new ways to drive viewer engagement. Twitter provides a forum for real-time context and commentary that turns watchers into participants” (Twitter, 2011). 

One of Twitter’s biggest television integrations was during the live coverage of the royal wedding. By using various hashtags such as #RoyalSuccess and #RoyalMess viewers interacted with ABC news to express their thoughts about the event unfolding. Viewers watching the wedding live on television “posted millions of Tweets, peaking at 16,000 Tweets per minute between 5 and 6 a.m. EST” (Twitter, 2011). 

The broadcast of the royal wedding is just one example of how Twitter can enhance audience engagement (Twitter, 2011). Shows such as My Kitchen Rules have also adopted live Twitter feeds on screen, with great success. A study has shown that 60% of people use social media while watching television, so there is no wonder why engagement can be so high (Harrington, 2013). But not only can Twitter increase engagement and the number of related Tweets, evidence has already shown that Twitter integration also increases the number of real-time viewers (Harrington, 2013). It will be interesting to see if Twitter and other social media platforms could save traditional, real-time television viewing in the future. 

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.