QUT KCB206 2014 Tutorial group 5

The popularized of the new media have led to the result of the decline of traditional media usage, especially for the entertainment industry since that people are now able to sit back and enjoy variety types of entertainment events such as watching movies, listening songs, contact with friends and even reading newspapers online. New media is big and it is big enough to gather different networking platforms or tools such as TV, mobiles, games and printed materials like magazines onto ONE platform – Internet.

  Since that the new media have gathered all different type of communication tools and platforms, people are now able to access similar contents on different platforms in specialised form. (Week5 lecture,2014) This is called crossmedia. Crossmedia have provided a great opportunity for audiences to access the online materials such as webpages, TV and our own laptops. With the rapidly growth of technology development, there are more people are able to afford their own laptops and smart phones.  With the increased platforms, entertainment industry such as TV broadcast stations could publish their programs on a lot more platforms in different formats. Which increased the interactions between the entertainment industry and their audiences and this phenomenon has led to another issue – fan cultures.

Fandom could be considered as a group of fans that form social networks with one another based on their common interest in reading and watching particular texts those are related to their idols, and the fans in turn write or otherwise produce materials for that text. (Georgia Institute of Technology, 2013) New media and fandom have a close bond between them. Fandom includes the re-creation of particular idols from their fans. The group may publish the extant media object on the Internet and share with the group that have common interest – the fan community. (Booth, 2010) According to Paul Booth, “Digital Fandom provides a balance to this “productive” dichotomy metaphor of Internet communication by revealing non-market economic antecedents to fan’s use of media. (Booth, 2010)  New media platform such as Instagram allows the fan community to publish their recreation works by creating an account for their community. New media entertainment provides new opportunities for self-formation. (Week 5 lecture)

Besides, YouTube is another social media platform to the fan communities. Since that YouTube is a free-to-all amateur website, fan communities are allowed to publish the materials that related to their idols without paying any extra. According to Week 5 lecture, the New media entertainment involves fans and audiences in a new way. YouTube has made the communication more easy and visual by only focusing on uploading videos. Idols are all visualised on YouTube and the fan communities are able to “see” their idols in a more “realistic” way. For example, the fan community of DBSK from Korea was once wanted to gather all the potential fans on the internet, they have created a video about how much effort DBSK have put into their career and how the fans are touched by their performances in order to make the fans become emotional and the desire of buying their CD and product will increase.

The new media is powerful since that it can influence people easily. Entertainment could use new media as a tool to work as a part of their marketing strategies. Users should be conscious about the information that they are receiving online and understand the meaning behind those information because after all we are the one who are using the new media, but now new media using us.

  Reference List

Saughter, Theresa. 2014. “KCB206 Internet Self and Beyond: Week 5 Lecture Recording.” Accessed April 3, 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_4831513_1%26mode%3Dreset 

In recent years it has been hotly discussed whether internet and social media are diminishing industries such as the television and music industries.  With most media and communication related topics, you usually only hear more so about the negative things that are going on rather than the positive.  This directly applies to the relationship between television and social media.  In the news and in scholarly articles we see and read bad things happening to television due to social media.  However, this week I learned that social media is significantly helping the television industry in that big data produced by social networking can greatly benefit those in the television industry.

 As we know, social media has allowed users to tweet and post status’ which allows them to express how they feel.  For producers of television shows this has majorly effected how they work and essentially lifted the veil that existed between them and their mass audiences.  Since platforms such as Facebook and especially Twitter are new channels of conversation, that have facilitated and extended conversation about television shows (Harrington, 238).  It gives users the opportunity to connect with other viewers in real time, and engaging in a live, effectively unmediated, communal discussions of television programs.  While they are watching a certain television show and engaging in online conversation with others, users are including hash tags in their conversations.  

These are vital for producers of the show as these hashtags make it relatively easy for them to capture and analyse that is surrounding there show.  They will be able to easily and readily find out if people like or dislike what is going on in there show.  No longer do producers have to rely on ratings or viewing numbers to determine the success of their show.  Social media has quite dramatically changed this landscape, overcoming pre-existing limitations, and now enable producers to listen to the audience’s conversation in real time. So this online conversation is not only beneficial for the viewer of the program but also for the creator.

 All these tweets, status updates and hashtags are an accumulation of data.  When collecting and analysing this data  producers can look at things such as the number of tweets per minute to determine what is and isn’t working within the show.  If they can effectively analyse the data and produce findings from it, they will reap a large amount of benefits.  Take for example the US version of the television program ‘X Factor’.  After each week’s episode the show’s producer and judge Simon Cowell takes time out to go onto Twitter and read a bulk of the tweets surrounding the show, whether they be good or bad (Stetler, 2011).  Than by the next week he would have made specific changes to the show based on what was trending on Twitter.  Cowell states that due to this “it’s like having millions of producers working next to you” (Stetler, 2011).  One example is when people were tweeting about how convenient it would be if they could vote for contestants on the show via Twitter.  A few weeks later it was made possible to vote for a contestant via a Twitter message.

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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.https://qutvirtual3.qut.edu.au/qv/olt_material_search_p?p_unit_code=KCB206

Stetler, Brian. 2011. "Twitter and TV get close to help each other grow". New York Times, October 25. Accessed May 11, 2014. http://www.nytimes.com/2011/10/26/business/media/twitter-and-tv-get-close-to-help-each-other-grow.html?_r=0

Everytime we select a link on Google, search for a YouTube video or purchase a song on iTunes, we are creating pieces of data. Big Data, the topic for Week ten’s readings, makes sense of all of this information. The mass information to which we are now able to obtain allows for a completely new outlook on how we understand the world (The Economist 2012).

 Social media is one of the most insightful and diverse areas in which information can be gathered. One of the most used and prominent examples of this is Twitter. Tweets, hashtags and replies are all stored and evaluated allowing for companies and organisations to understand their target audiences as well as their specific wants and needs Woodford 2014). When these companies understand their audience better, they can advertise, sell and utilize information more effectively (The Economist 2012). When someone incorporates a hashtag into their tweet, it links itself to any other tweet with that hashtag. This allows for groups of tweets to be formed, categorising areas of interest and thusly showing the popularity of topics.

 An area to which big data is highly significant is within the television industry. Although episodes of popular TV shows are given ratings of viewers, often it does not directly correlate to the audiences perspective (Woodford N.D.). Thusly tweets allow for producers of these shows to understand when audiences were more engaged and more interested. However, this information cannot be approached by simply counting out the number of tweets per minute and comparing to other TV shows. Instead, big data allows for levels of excitement to be understood. The Excitement Index (EI) can be found by firstly, dividing each tweet count by the average creating a ratio between the number of tweets within that minute and the overall average (Woodford N.D.). The sum of these two numbers, then divided by the length time of the show gives the average change per minute.  By then multiplying by five, the Twitter Excitement Index is found. The higher the number, the more engaging and exciting the show is. This gives media companies the information they need on how and why audiences react to specific episodes and thusly, allow for change and improvements to be made.

EI of American Horror Story. 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

However, with all of this ease and simplicity in extracting information, it does have a downfall. While nearly every move we make online is stored and evaluated, it does beg the question of whether ones personal privacy is being overlooked (Woodford 2014). The fact that most people have little idea to the extent to which their online behavior is being tracked allows for some concern. As our personal information is being turned into numbers and statistics for big companies to understand our habits and ultimately increase profit, is this, in a sense, a form of media labour?

  References

Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming) “Telemetrics: Towards Measuring Social Media Engagement with Television.” Accessed 9 May, 201. 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. 2014. “New Media, Big Data and Telemetrics” KCB206 Week Ten Lecture. Accessed 9 May, 2014.  http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

Big data, why does it creep people out and how do we fix it? Big data refers to the collection and analysis of large amounts of data to better predict trends in our society, the needs and wants of customers and so on, as well as referring to the utilization of that information to become as efficient as possible. With Eric Siegel (2013, p.1-16) pointing out how the prediction effect can influence our lives by allowing for the collection of big data, we can see how organizations collecting data on such a large scale can be beneficial to us. Permitting companies to advertise and cater to us based on what we like alongside our past behaviour is great, albeit a little creepy. This leads to the major concern many seem to have with the collection of big data, and that’s privacy.

Numerous people aren’t comfortable with information being collected on them without their consent, if the outburst surrounding the NSA is anything to go by, (Greenwald, 2013). Some people didn’t care, others defended the NSA and attacked Snowden for revealing confidential information to the public, but the majority felt betrayed.  I can understand the need for a spy agency to remain incognito, but when a story like this comes out, trust becomes a serious issue. If the NSA was transparent about the data they collected from the beginning, would this be such an issue? Considering the scale at which this spying took place, not just on citizens of the USA but the citizens, officials and governments of other countries as well, I’d say it probably would… but what about the other companies and organizations that collect data on a large scale, be it to advertise to the appropriate people for their products, or react to trends in an area. If all these data collectors were upfront about the data they were collecting, would people view big data collection with such animosity?

It’s indisputable that Google gathers information about its users, but it’s not something they’ve really tried to hide. You can view data reports on internet traffic, content removal requests as well as security and privacy reports they’ve compiled through their Transparency Report feature, (Google, 2014). Google even goes to the extent of notifying you if someone requests Google to disclose information that’s stored or associated with your account unless they’re legally prohibited from doing so. The legal process section also details what needs to take place before law enforcement agencies can force Google to disclose user information. While this Transparency Report by no means gives access to all of the data collected by Google, many companies wouldn’t disclose this information to its users, so it’s definitely a step in the right direction. People for the most part trust Google to do right by us with our data, and part of that trust boils down to its transparency. If companies and corporations become more transparent and honest about their data collection, they too could work towards the level of trust we have for Google.

References

Google. 2014, “Transparency Report” Google. [ONLINE] Available at: http://www.google.com/transparencyreport/?hl=en_US [Accessed 02 May 2014]

Greenwald, G. MacAskill, E. Poitras, L. 2013, “Edward Snowden: the whistleblower behind the NSA surveillance revelations” The Guardian. Available at: http://www.theguardian.com/world/2013/jun/09/edward-snowden-nsa-whistleblower-surveillance [Accessed 03 May 2014]

The Economist. 2012, “What is Big Data?” YouTube. [ONLINE] Available at: https://www.youtube.com/watch?v=ahZGEusG13A [Accessed 01 May 2014]

In this network and intelligence age of society, data analytics is an important factor that leads to success for a company or organisation. Useful information could be gathered from a big among of data and turn the information into action and wisdoms. (Benchley, 2013)

  Big data is a popular term that used to describe the exponential growth and availability of data, which includes both structured and dynamically structured. For example, social media and text message conversations. The idea of big data could be explain and show by the following example. Back in 2012 there are around 2.8 zeta bytes and according to the prediction, in 2020 the total among of data store is expected to be 50 times larger than today. (SAS, 2013) Back in 2012 America was the largest single contributor to global data. According to the prediction from ‘International institution for analytics’, the emerging markets are showing the largest increases in data growth. Moreover, the global data access has become more evenly distributed. This phenomenon could be caused by the popularized of the use of new technologies and new media. People may gain easy access to all possible information.

In week9 lecture, the importance of big data to telemetric was mentioned. “Receiving a message from a highly followed individual is a status symbol in itself.” In this network and intelligence age, big data is a new way of understanding the industry since that companies are able to visualize things that they ever could, such as customers’ responses towards to product. (Davenport & Dyché, 2013)

The example that I have picked is a Korean drama called “My love from the star”. The Korean broadcast station SBS have achieved a huge success with this drama in 2013. While the drama was broadcasting, SBS have decided to conduct surveys on social media platform such as the company’s official website and forums to collect opinions from the highly followed individuals of the drama. SBS has gathered the data, visualised and analysed the data and turned the data into action. Which they change the story line to fulfil the desire of the audiences. This is a great example of big data worked as a business intelligence and proven the importance of the data market could be.

 On the other hand, according to SAS’s report in 2013, the data and information those are available on Internet, nearly half of them are not under production or private. Internet users are able to access a large among of data without getting the authors’ permissions. Derivative work is a common phenomenon that people can recreate a certain topic by adding new elements based on the original work. However, derivative work may still considered as a piracy act under the copyright laws since the author of the original work did not approve, or did not know about the recreation.

 We now have easy access to the big data on the Internet. On the other hand we are also giving out a lot of information when we are using the network. Internet users should have caution towards the privacy of their work and information since that the others may access the unprotected data, as easy as we do everyday.

        Reference

  Woodford, Daryl. 2014. Week 9: NEW MEDIA, BIG DATA & TELEMETRICS, KCB206: Internet Self and Beyond. Retrieved from:http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdfhttp://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf. Accessed: 10 May 2014

  The Economist. 2012. “What is Big Data?” YouTube video, posted June 26. Accessed May 6, 2014. http://www.youtube.com/watch?v=ahZGEusG13A

Since the introduction of online video viewing the dynamic of television has been forced to change. With the Internet taking over the media realm, TV has had to integrate and adapt to remain relevant. Steven Harrington (2013, p. 238) suggests that the Internet and television can work to enhance each other. Despite some sources describing YouTube as a threat for television, many shows use it to their advantage. For example, Jimmy Fallon uses the social media platform to further his comedy talk show but posting popular skits for fans to re-watch as well as by posting YouTube exclusive skits.

  Harrington also points out the extra channel that the Internet provides to offer new ways to promote and generate conversation. Television game shows often use websites to add an extra element to the contest for viewers to participate in. My Kitchen Rules for example, is a cooking competition show which posts recipes onto their website for fans to look up and cook themselves. This extra element of participation helps viewers to feel more connected to the show.

  Twitter being one of the most popular social media platforms, it only makes sense that it would be utilised and incorporated into television programs. Referring back to the My Kitchen Rules example, the game show includes a live stream of tweets by fans using the #mkr hash tag. This gives fans to have the opportunity for their thoughts and commentary to be displayed during the airing of the show, adding an extra element of involvement.

  The incorporation of Internet with TV essential creates a platform where viewers, no matter their location, can share an experience together (Harrington, 2013, p. 241). This is often referred to as the ‘virtual lounge room’ (Harrington, 2013, p. 241). Giving the space for people to share opinions and connect over an online platform is both convenient and gives and added edge to what use to be single medium entertainment.

  Eric Siegel (2013, p. 2) describes Internet as a focal point of our day to day lives. Almost everything we do revolve around it. We self diagnose through Google, buy tickets to events online, communicate with friends over social media; it only makes sense that our television entertainment moved online as well (Siegel, 2013, p. 2). Without a doubt he ability to stream television shows online at any point in time and even on the go with smart phones, the TV industry has had to work out a way to converge with new media. Overall the new methods and systems that the industry is using to incorporate and embrace the use of the Internet in their work are successful. The use of social media sites such as twitter and YouTube help to enhance and give a sense of exclusivity to the programs.

  References:

Siegel, E. ( 2013). Predictive analytics :the power to predict who will click, buy, lie, or die. Hoboken : Wiley .

This techno-social society where we are linked by social media has created a new means of gathering information. Data produced from social media can be priceless to companies who know how to analyze it. There are some who question the ethics of big data, however, blame can’t be restricted to the corporations. Social media users can’t be blind to the fact that what information they provide on the site and what they do on it was intended for sharing, hence ‘social’ media. Data collected via people using twitter is used to benefit a company, yes, but it also promotes that sense of community and sharing that everyone goes to social media for.

Twitter especially is an important part of providing big data. The data gathered on twitter is hugely useful for any company. ‘It’s basic marketing; know your audience’ (Walden, 2013). Prior to new media, the price of conducting research to gather information could be quite excessive, and like all research, subjective. So it’s in a company’s best interest to go to a source where there is already data for the taking. In the case of television, twitter has given us ‘the capacity to efficiently collect enormous amounts of data, at relatively minimal cost and effort, about how people react to TV in real time’ (Harrington, 2013). Because of these benefits, we can now see traditional media outlets adapting to keep up with the ever changing times, encouraging the use of audience contribution via twitter. ‘Hashtags are a very good way of picking up data from the API… and can get a large sum of data which can then be analyzed’ (Woodford, 2014)

When I watch ‘The Voice’ I am overwhelmed with promos for their website and to join the conversation through twitter. While I personally won’t be joining in, I can see other people who are watching the show and their thoughts as a text box pops up in the corner of the screen with viewers twitter comments who tweeted to the #TheVoiceAU. Not only does this provide the program with information to profile their audience, but it’s also a free and effective way to advertise. This ‘cycle for free promotion’ (Woodford, 2014) has a ripple effect where if just one person tweets about ‘The Voice’ their followers will see it and may respond and so forth. And I don’t necessarily think this is a bad or immoral thing. ‘Anything that is public is fair game’ (Woodford, 2014)

When people sign up for twitter, Facebook or any other kind of social media, the sites privacy policy and terms and conditions and what they can do with your information is stated. So if users aren’t aware of what’s being done with their information and don’t like it, well they were warned. And if they were aware of how their information get’s used but still accepted the terms, they knew what they were getting into. To put it very simply, if you want to be completely private, just don’t use twitter or any social medium for that matter. And as for companies getting free advertising, no body forces people to tweet/like or comment something, it’s all done at their own will, because they chose to do it.

References:

Walden, S. (2013). Marketers, Do You Really Know Your Customers? Retrieved from http://mashable.com/2013/11/25/visitor-profiles-metrics/

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. https://qutvirtual3.qut.edu.au/qv/olt_material_search_p?p_unit_code=KCB206

The word ‘prediction’ carries with it many connotations, fortunetellers, fate and future. Eric Siegel in chapter one of his book, ‘Introduction: The Prediction Effect’ talks about the power that prediction has in big business today. He talks about how computerized prediction has untold power as it analysis big, seemingly incomprehensible, data. “ The truth is that data embodies a priceless collection of experience from which to learn. Every medical procedure, credit application, Facebook post, movie recommendation, fraudulent act, spammy email, and purchase of any kind – each positive or negative outcome, each successful or failed sales call, each incident, event and transaction – is encoded as data and warehoused.” (Siegel, 2013) Through analysis of this immense data computers are able to learn “automatically developing new knowledge and capabilities by furiously feeding on modern society’s greatest and most potent unnatural resource, data”.(Siegel, 2013) I can’t help but think about the terminator movies and Skynet. The machines are learning!

 The thought that everything I have ever done on the Internet has been recorded somewhere and that someone can access this is a scary one. Even more worrying is that the data I am creating when surfing the net is being used to market to me.

 Siegel makes its clear that it isn’t as easy as it sounds to use data to make predictions. “as data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein. The process of machines learning from data unleashes the power of this exploding resource. It uncovers what drives people and the actions they take – what makes us tick and how the world works. With new knowledge gained, prediction is possible.” (Siegel, 2013) With this new ability to predict you might ask what exactly it is that the machines are trying to predict, “ Every important thing to a person is valuable to predict, namely: consume think, work, quit, vote, love, procreate, divorce, mess up, lie, cheat, steal, kill, and die”. (Siegel, 2013)

 However Siegel does say that accurate prediction isn’t easy, in that the future can never be predicted with 100% accuracy. He states the Prediction Effect is valuable because “ predicting better than pure guesswork, even if not accurately, delivers real value”. (Siegel, 2013)

 Predictions have untold power as predictions govern the way every organisation interacts with its consumers.  There are five effect of prediction introduced in the this reading and initially it focuses on Predictive Analysis (PA) . “PA leads within the growing trend to make decisions more “data driven “, relying less on ones “gut” and more on hard, imperial evidence”,  (PA) is “technology that learns for experience (data) to predict the future behavior of individuals in order to drive better decisions”. (Siegel, 2013)

This new ability for businesses to access, understand and utilise large amounts of data doesn’t sit well with me and if anything drives me to be unpredictable in my actions and spending’s. Overall this new ability will prove useful to businesses but could be dangerous to those who are easily influenced.

Bibliography

This week we were introduced to the notion of “big data”, a concept that classifies our online participation as a new kind of knowledge just waiting to be organized and interpreted. In this weeks lecture Darryl Woodford discussed new media and telemetrics, defining big data as “a currency across industry, business intelligence and data markets” (Woodford, 2014). Societies prolific use of social media has prompted television and media bodies to utilize this medium as a tool for measuring and interacting with audiences (Ibid, 2014).

  “Big data is used to describe the exponential growth and availability of data, both structured and unstructured” (SAS, 2014). As early as 2001 analyst Doug Lanely articulated the potential big data poses to those organizations that accurately analyse it. Sites like Twitter and facebook hold “data that embodies a priceless collection of experience from which we can learn” (Siegel, 2010). That’s right, no matter how benign or mundane the tweeter post, that public declaration on your newsfeed is encoded as data and warehoused (Siegel, 2010). As discussed, Twitter is at the forefront of big data analysis, and as Axel Bruns discussed, “Twitter as a platform, and can be understood as corresponding to micro, meso, and macro layers of information exchange and user interaction”.

    Twitter trends are increasingly conceptualised through user “hash-tagging”. Every time a user “hash-tags” they are essentially marking a tweet as being relevant to that specific topic, effectively making it more discoverable to other users and researchers (Bruns, 2013).

    Woodford’s article, “Telemetrics: Towards Measuring Social Media Engagement with Television”, provided an insight into the development of sophisticated and nuanced metrics to understand how Twitter uses are reacting to the content of particular broadcasts (Woodford, 2014). The article discussed two modes of evaluating the performance of television shows through twitter. The twitter excitement index, performs show-to-show comparisons of audience engagement as it measures the volatility of Twitter discussion throughout the show. This method thus allows comparisons across genres and between networks and countries (Woodford, 2014).  The second form of twitter analysis is undertaken through a seaonsonal model evaluating episodes independent of their context (ibid, 2014).

Google’s’ recent investment of $130 million to finance the start of Flatiron Health Inc. represents the financial worth big data possess. (Hay, 2014) Flatiron health aggregates cancer-patient data from a wide variety of sources, and with Google’s investment it can now allow doctors to acquire patient records to help make more informed treatment decisions. (Ibid, 2014). 

    We live in a hyper-personalized world, and the analysis of big data by organisations permits them to personalize content dependant on the needs of the consumer. The leveraging of big data tools by organisations is just another instance in new medias continual evolution. As this process benefits both the consumer and the organisation I can only advocate its use. 

      Woodford, Daryl. “KCB206 Internet, Self and Beyond: Week 9 lecture notes.” Accessed May 7, 2014. http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

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

    Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming). 
     “Telemetrics: Towards Measuring Social Media Engagement with 
     Television”. Accessed May 8, 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%20Measu
     ring%20Social%20Media%20Engagement%20with%20Television
     .pdf

Bruns, Axel. 2013. “Follower Accession: How Australian Politicians Gained their Twitter Followers.” Mapping Online Publics Blog, July 8. Accessed May 9, 2014. http://mappingonlinepublics.net/2013/07/08/follower-accession-how-australian-politicians-gained-their-twitter-followers/

    Hay, Timothy, 2014 “Google Ventures Leads $130M Round For Big Data Medical Software Company Flatiron Health” Wall Street journal, may 7th, Accessed may 9th, 2014. http://blogs.wsj.com/venturecapital/2014/05/07/google-ventures-leads-130m-round-for-big-data-medical-software-company-flatiron-health/

This week topic is about the perils and potentials of the emergence and use of “big data”. The lecture had outlined some novel approaches to collecting and analysing data on television shows through the concept of predictive analysis.

A collection of data sets which are large and complex and become difficult to process by readily available database management tools or traditional data processing applications will be defined as big data. In recent years, social media became one of the most effective methods to collect data and marketing services. Research has been conducted to create metrics for social media (Woodford, 2014). Data is collected by different companies which aimed at analyzing the interaction between the industry and consumer. According to Seigel (2013), data is not the gold. In its raw form is boring crud in it. The gold is what can be found therein. With the process of machines learning from data, the power of its resource will drive people and the actions they take. After the new knowledge gained from the process, prediction is possible. This is becoming more important in the entertainment industry in recent years. Tweets, Facebook status and every user of social media are actually informative about other platforms. When you post a tweet, hashtag with a television show or like a fan page of a movie, all these kind of content can be collected and analysed to forecast about consumer behavior and assist in marketing. To measure the engagement of audience with a television show, the best way is to considering the maximum tweets per minute (Woodford, 2014). Take “Hawaii Five-O” from CBS as an example, it is the first drama to allow viewers to choose the ending of an episode in TV history.

Audiences can choose to vote on CBS.com or Twitter during broadcast and the vote will be tallied immediately. The most popular ending will suit each broadcast. Which means audiences can actually have the chance to tell the producer what they think. The producers of the show can therefore used the big data from the audiences and develop the show. For me, I always “like” the official fan page of my favorite television shows in Facebook, I always vote for my favorite artist when I am watching “The Voice” and I always retweet when someone post about my favorite drama. There can all be used as part of the big data without I am noticing it. With the emergence of different social media platforms, it is becoming more usual for audience like me express opinions in Twitter and Facebook. These kinds of information are all over the social media platforms; therefore, I think it is crucial for media producers to make good use of it. Once they have collected and analysed it, it will become the valuable information that ready to use. Although variance can be seen in ratings are mirrored in tweets and unique users, the variation between the Twitter audiences is a bit extreme (Woodford, 2014). Therefore, companies cannot solely rely on the data from social media platforms. All in all, understanding audiences is very important for companies and content produces.

References:

Siegel, Eric. 2013. “Introduction: The Prediction Effect.” In Predictive analytics: the power to predict who will click, buy, lie or die, 1-16.Hoboken, NJ: Wiley. Accessed: 11 May 2014

What is the Internet to average people like you and me? A constant connection to people and information that could originate from the opposite side of the globe; or in my case, my number one tool in procrastination. But the Internet to data analysts and API groups is an apothecary of data waiting to be dissected. In the week 9 lecture we explored the idea of Big Data and the role it plays in all industries today.

Don Tapscott (2012), the co-author of Macrowikinomics, wisely described the age we live in as “not an information age” but an “age of intelligence and one of collaboration. This is very much the truth. We [human beings] are hungry for new knowledge and information, and new methods of receiving what’s new. As new technologies have become the primary source of information gathering, every action conducted online generates new information and data for others to receive. Big data is a means of leveraging technological advancements in order to make sense of all the information generated online.

The main example of data sourcing used during the lecture given by Darryl was Twitter, where it was defined as a proxy to interpret information regarding other platforms (Woodford, 2014). While Application Program Interfaces (APIs) are generally the chief method of specifying data found online, Twitter is an easier alternative to data gathering and interpretation. On Twitter, hashtags (#) are frequently used to contribute and coordinate a discussion on a public network. Bruns and Moe (2013) introduce 3 key layers of communication on Twitter, and define the use of hashtags as the macro layer. By tracking the use of hashtags, analysts are able to use this data to source what is most popular and when and why and all other bits of information that can be extracted from trending topics. Commonly TV shows will advertise for viewers to post on twitter and use a specific hashtag in relation to the show, and will allow some to be selected and aired on live television. This also allows for twitter users to engage in live discussions about what they are watching with people they mightn’t have ever met before. In a sense, Twitter serves as a virtual lounge room (Harrington, 2013). What tweeters might not realise is that by using the show’s hashtag they are providing free advertising for the show, and also opening your 140-character-long thoughts to the general public. Now I don’t know about everyone else, but the idea of having strangers view things I share makes me feel equivalent to having them look through my bedroom window: very very uncomfortable. Big data collection is another aspect of new media technologies that challenges traditional notions of privacy, which was a topic we explored way back in week 2. Despite this slightly uninviting factor of big data, it is truly a very useful way to discover what the public wants. By tracking the hashtags used by viewers, (using my TV-viewing-tweeters example again) the head honchos of a TV show are able to monitor how many people actively engage online and what the viewers may want to see or not see. In this sense Twitter provides the platform for TV programs’ analysts to extract data that can benefit the show’s production.

  References:

-       Bruns, Axel & Moe, Hallvard. 2013. “Chapter 2: Structural Layers of Communication on Twitter” in Twitter and Society, edited by Axel Bruns, 15-28. New York: Peter Lang.

-       Harrington, Stephen. 2013. “Tweeting about the Telly” in Twitter and Society, edited by Axel Bruns, 237-247. New York: Peter Lang.

-       Woodford, Darryl. 2014. “New Media, Big Data & Telemetrics” Accessed May 6, 2014. https://lecturecapture.qut.edu.au/ess/echo/presentation/11dcf106-e7c9-44eb-a753-74dfff689043?ec=true

New Media, Bid Data and Telemetrics

With the popularity of social media, people have taken their lives to the internet and corporations governments and organisations are profiting as a result of big data being used as foresight to see in to the future and the ability to map complex mass behavioural patterns. Big data is defined as a currency across the industry, including the sciences and social sciences according to Woodford (2014,4). As you engage on the internet and use sites like google, Facebook, Twitter ect. data is stored, individually this data isn’t that useful but when plug into a larger group of information it can form patterns that can be used to predict events or bring awareness before it becomes apparent to the masses. Google can calculate and measure the rate of people getting sick in particular global areas and by what cause as people Google search for information on their sickness (http://www.iom-world.org/sicknessabsence/summeas.htm). This means that if there was an outbreak of a particularly harmful virus Google could warn authorities to close travel methods e.g. airports to contain the virus to a location.

Although big data can be used to benefit societies it can also be used by corporations to greatly profit from these mass established patterns. For example large corporations that dealt with smart phones like Apple/Samsung could pull data from social media like Twitters hash tag system as people talk about future model releases #needsabiggerscreen. Buying patterns could influence the way they market their products. Siegel (Siegel,2013) raises a good point, how far will this big data get into the mind of the consumer  and questions the accurateness/effectiveness. Unnoticed variables could strew data and be effective in making decisions.

How ethical is big data collection? Most people are unaware their internet use is being collected and being used by governments and corporations. Could this big data collection be used to monitor people and take advantage of their perceived privacy? Edward Snowden revealed the NSA was storing bulk private information being used to “protect” people and they claim it has prevented 40+ terrorist attacks from happening since its introduction after 911 in 2001. If they had made this publically clear to all people would this have been such a big deal?

References

Bruns, Axel, and Hallvard Moe. 2013. “Chapter 2: 2 Structural Layers  of Communication on Twitter”. In Twitter and Society. New York:  Peter Lang.

Siegal, Eric. 2013.  “Introduction :The Prediction Effect” in Predictive analytics :the power to predict who will click, buy, lie, or die. 1-16. Hoboken, NJ: Wiley. Accessed May 9, 2014.http://blackboard.qut.edu.au/webapps/portal/frameset.jsp?tab_tab_group_id=_4_1&url=%2Fwebapps%2Fqut-cmdbb_bb60%2Fdisplay.jsp%3Fcourse_id%3D_108110_1%26content_id%3D_4848770_1

New media and in particular social media, has made it easier to collect Big Data. Most of us that are actively on social media are part of a resource that can be used for big data. Data is collected when we log in, post, share, like and comment. We are constantly putting data in the public domain (Woodford, 2014). Big Data is a term used to describe the exponential growth and availability of data.

Big Data has become an important part or large businesses and corporations. Data is collected and processed to find trends in consumer behaviour and gives these businesses a better collective understanding of their customers (Chung, 2012). In recent times it has had a huge impact on the entertainment industry. Think about how many times you have used your Twitter or Facebook account for some sort of link to a TV program or Movie. Are you the kind of person to post about My Kitchen Rules? Or do you Hashtag your favourite Big Brother contestants? This kind of content you share can be collected and analysed. This data may then be used to make predictions about consumers and assist in marketing (Woodford, 2014).

In more recent times, the large increase of popularity in video games has caused gaming developers to greatly increase their use of big data. Because video games happen in a virtual world, it's possible to measure just about every aspect of the game. It's also like being able to observe a sports match or a battle, but being able to attach stats and figures to every player, every weapon / bullet, every surface of the environment, and gather all that data in real time. The Big Data revolution has made this possible, and video game companies routinely gather 50 terabytes of data per day to improve their games, operations and revenue (Smith, 2013). Games themselves become mirrors of our own playing preference, which brings us back to play more when we are completely enjoying a game.

Image Source: http://media.pcgamer.com/files/2013/03/League-of-Legends.jpg

One game in particular has seen a huge impact from the use of Big Data. This game? League of Legends or LOL as you may know it. Altogether, LOL players average around a billion cumulative hours of play a month and game developer Riot Games keeps track of everything, as LOL has become one of the most competitive games in the world. David Smith, a data scientist at Revolution Analytics says “Because it’s all happening inside a computer, Riot Games can attach a telemetry sensor to every player, every joint, every part of the field, and gather all that data” (Johnson, 2013).

Almost all players will reach a point where they will plateau without self-reflection, analysis, and practice. Any player who understands the basics can learn from statistics. It will be easier for them to identify their weaknesses and focus on improving. But stats can only get you so far, with high level competitors using ‘gut feeling’ and recognising patterns (Johnson, 2013). The same could apply to other businesses. How far will this data get you into to the mind of the consumer? Will it be effective all the time? And will the data be accurate? (Siegel, 2013)

References:

Chung, Andrew. 2012. What is Big Data? The Economist. Retrieved from: https://www.youtube.com/watch?v=ahZGEusG13A. Accessed: 11 May 2014

Johnson, Jason. 2013. How Big Data Hurts and Helps League of Legends Players. Kill Screen. Retrieved From: http://killscreendaily.com/articles/articles/how-big-data-helps-and-hurts-league-legends-players/. Accessed: 11 May 2014

Siegel, Eric. 2013. “Introduction: The Prediction Effect.” In Predictive analytics: the power to predict who will click, buy, lie or die, 1-16.Hoboken, NJ: Wiley. Accessed: 11 May 2014

Smith, David. 2013. The impact of Big Data on video gamers. Revolutions. Retrieved From: http://blog.revolutionanalytics.com/2013/12/the-impact-of-big-data-on-video-gamers.html. Accessed: 11 May 2014

This week in Internet, self and beyond we looked at the concept of Big Data and telemetric. To begin, we need to have a grasp on exactly what big data is. Defined succinctly in the video below, big data is a basically the process of how we are using the data that is being produced constantly in our technological society and transforming it into change, ideas and understanding on consumption behaviours.  

http://youtu.be/ahZGEusG13A

Seigel (2013) makes the point in Predictive Analytics that data in itself is not useful, the way we analyse it is where the real progress stems from."As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein...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 (Siegel, 2013)" 

This data may be used to predict trends in consumer behaviours. The increasing convergence of various online handles across different sites allows users to easily follow a certain content creator across all platforms. This allows the owners of this content to track and analyse the behaviours with thanks to hashtags and other functions within the sites (Woodford, 2013). This then becomes increasingly important within the entertainment industry, as it is becoming more and more common for an audience to become active through the use of twitter (Harrington, 2013). 

For me, the most obvious example of how big data interacts with audience is QandA, as the twitter components run a lot of the topics. This feature of the show, the data retrieved from it, may be analysed to gather information of the active audience.  Big Data is undoubtably useful for companies and content produces to gain an understanding about their audience. The subtle use of big data for a audience can be the impact of a more tailored and directed entertainment and even furthered into other industries. Through big data, new media has opened the doors for new and exciting ways for content to created and audiences to be understood.

Harrington, S. (2013). Ch 18 Tweeting about the Telly: Live TV, Audiences, and Social Media. In K. Weller, A. Bruns, J. Burgess, M. Mahrt & C. Puschmann, Twitter and Society (1st ed., pp. 237-248). New York, NY: Peter Lang.

Siegel, E. (2013). Predictive analytics (1st ed., p. 4). Hoboken, N.J.: Wiley.

Woodford, D. (2014). NEW MEDIA, BIG DATA & TELEMETRICS. Presentation, QUT.

Ever since new media has been developing in the society, the term “big data” has been getting popular. According to Woodford (2014, 4), big data is a currency that is across the industry, including the sciences and social sciences. It is also about diverges and a large-scale data set (Ogilvie 2012). Big data is extremely useful in prediction analysis in social media. In the contemporary society, businesses always use a variety of social media tools to gain public awareness. In order to analyse the results, the companies have been gathering big data about social media (Woodford, 2014, 1), as well as visualising the big data into graphs.

Analysing Twitter usage is one of the trends for companies to gain a sound understanding of audience engagement, as well as measuring success. In Twitter, hashtags are one of the measurements in the research for analysing the data. Hashtags are used as a way of organising discussion about an issue, which is relatively easy to capture and measure in research. For example, if we set Big Brother (the television show) as the topic, we can search “#bigbrother” and find out how many tweets use the hashtag #bigbrother, in order to collect big data for further analysis. After collecting the data, visualisation is the next step for the researchers to analyse the results. Data visualisation is communicating the information based on big data through graphic presentation (Ogilvie 2012). Below is an example of visualised data about the tweets that tracking the #bb15 hashtag (refers to the 15th Big Brother series) and other hashtags associated with Big Brother.

Image source from: http://mappingonlinepublics.net/dev/wp-content/uploads/2013/07/minutebyminute-corrected.png

The visualised data (Woodford 2013) shows the peak time of most people discussing Big Brother, as well as labelling live telecasts of Big Brother. After visualising the data, the researcher can analyse the phenomenon, as well as measuring the success of each Big Brother show. In addition to the visualised data, the researcher can also compare different sets of related data for the results. The graph (Snurb 2013) below shows the situation that can trigger an increased amount of Twitter followers of politicians.

Image source from: http://mappingonlinepublics.net/dev/wp-content/uploads/2013/06/Combined-overall-follower-growth-first-500001.png

Furthermore, 2-dimensional graphs can be used to visualise the big data, as you can see below. In the graph, the closer the terms are, the higher the association between the terms (Woodford 2013).

Image source from: http://mappingonlinepublics.net/dev/wp-content/uploads/2013/07/2dmap.png

From all of the graphs shown above, we can see that the numbers related to new media can be collected as big data, in order to analyse the results. In addition, big data can be visualised into graphs, which makes it easier to read the data. Big data has become more common in contemporary society and important in the new media research.

------------Reference-----------

Bruns Axel. 2013. “FOLLOWER ASSOCIATION: HOW AUSTRALIAN POLITICIANS GAINED THEIR TWITTER FOLLOWERS.” Accessed 10 May 2014. http://mappingonlinepublics.net/2013/07/08/follower-accession-how-australian-politicians-gained-their-twitter-followers/

Ogilvie, Rupert. (2012). Big data visualisation. Computer Reseller News. Accessed 10 April 2014. http://search.proquest.com/docview/1020411547?accountid=13380

Woodford, Darryl. 2013. “Big Brother 15, WEST COAST VIEWERS & GUILT BY ASSOCIATION.” Accessed 10 May 2014. http://mappingonlinepublics.net/2013/07/29/big-brother-15-west-coast-viewers-guilt-by-association/

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

Have you ever wanted to be a part of the reality television show, Big Brother? Do you think you could have your every move monitored and analysed to the nth degree? Well, that is something you find yourself comfortable with, it may be closer to home than you first imagined. Unbeknownst to many of use, whenever we are logging onto Facebook, sending a tweet or starting a conversation on social media with our friends we are contributing to big data. This week, Daryl Woodford (2014) gave a lecture on new media, big data and telemetrics and the way in which the work and how they impact our lives and help organisations. Particularly, big data has become an integral part to the entertainment industry and Twitter especially has become a forerunner for the use of measuring metric engagement of consumers towards a product or event. (Woodford, 2014). As data piles up, we have ourselves a genuine gold rush of information in which uncovers the driving factors for people’s actions. (Siegel, 2013, 4). However, where all data collection is concerned, there are also ethical issues to consider, as essentially corporations are using this information to gain a better insight into consumer behaviours and to then market to us other products. (Woodford, 2014).

  Big data, although defined many different ways may be summed up as the way in which data formed online, collected, analysed and presented as global information for use. (The Economist, 2012). This data may then be used for predictions and marketing, whatever use, it empowers an entirely new form of competitive armament to organisations. (Siegel, 2013, 2). As mentioned, Twitter is a site most easily used for tracking this particular information, and is often used as a proxy for other platforms. For example, ‘Youtubers’ or other personalities and produsers (Bruns, 2008) will use the same title through their primary channels and Twitter, so that majority of their subscribers will follow them on both sites. (Woodford, 2013). In doing this, content creators, corporations and organisations have the ability to analyse their consumers behaviours with thanks to Twitter’s characteristics of hashtagging, keywords and further investigating trends from macro to micro forms (Bruns, 2013, 20). This is particularly helpful for the entertainment industry as majority of consumers are now encouraged to be ‘active.’ Harrington (2013, 244) discusses that since the notion of the ‘active’ audience has become relevant, television is understood as a medium that readily catalyses audience discussion, interaction and fandom. Organisations will then take this data, from interactions and discussions to further market other products to you, to cater to your preferences.

 However, it is sometimes difficult to escape. One woman actually attempted to hide an entire pregnancy from big data by specifically avoiding any conversations online about her pregnancy, shopping for, or searching for anything baby related. As you can imagine, this proved to be extremely difficult, but by avoiding these practices she was not flagged as an expected mother, tracked and then bombarded with pregnancy related advertisements. (Gray, 2014).  

Therefore, it is obvious how big data is able to aid organisations in tracking and marketing their products, but that is not all. Now more than ever, we have access to an abundance of information that can be used for further advancement throughout a number of industries, not just entertainment. Again, new media has provided the ability for further exploration, innovation and understanding of consumers and their behaviours. 

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REFERENCE LIST:

Bruns, Axe and Hallvard, Moe, 2013. “Structural Layers of Communication on Twitter.” In Twitter and Society, edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt & Cornelius Puschmann, 237-248. New York, NY: Peter Lang.

Gray, Sarah. 2014. “One woman’s attempt to hide her pregnancy from big data – it’s more difficult that you’d expect.” Salon, April 29. Accessed May 7, http://www.salon.com/2014/04/28/one_womans_attempt_to_hide_her_pregnancy_from_big_data/

Harrington, Stephen.  2013. “Tweeting about the telling: 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.

Woodford, Daryl. “KCB206 Internet, Self and Beyond: Week 9 lecture notes.” Accessed May 7, 2014. http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf

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