QUT KCB206 2014 Tutorial group 6

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Big Data: Knows You Better Than You Do!

Before the boom of online engagement and Web 2.0, the natural method of data collection was through human input into records or computers. These days, any individual that participates online essentially creates their own data imprint; automatically recorded whenever they click on an ad, ‘like’ a Facebook page or enter a website. The ever-present role of the Internet in our everyday lives means data now grows by 2.5 quintillion bytes per day (Siegel, 2013, 2). The sum of this huge, growing resource can only properly be measured by what Siegal (2013, 3) describes as “machine learning”: the use of computers to quantify and process this data to develop new knowledge and capabilities, such as predictions. "Predictive Analytics" describes the method of using data mining to determine likely outcomes, which organisations use determine the likely outcome of their products and services. Examples include the prediction of mouse clicks for websites with pay per click advertisements (Siegal, 3), dating predictions for online sites such as Match.com; and the ability of companies such as Hewlett Packard (HP) to predict the chance of an employee’s 'flight risk’ (whether they will quit their job).

Our willingness to contribute data to companies like Facebook allow them to monitor personal usage and on-sell information to interested parties such as advertising agencies. Algorithms developed by Cambridge University and Microsoft allow Facebook to make predictions based on characteristics such as demoraphics, page likes, Facebook usage, location and language and purchasing activity (Cohen, 2014). This following video provides a really good simple explanation of the type of data Facebook mines and how.

Facebook advertisements and increasingly their ‘page suggestions’ (subtly weaved into News Feeds), are based on individual user characteristics. For example, both my housemates are females in their late twenties – their Facebook pages constantly include ads for weight loss products, bridal advertisements and dating websites with ‘single, faithful policemen’ in their local area: all products for which they would be classified as the target audience. I recently went kayaking and posted a status about it along with photos and ever since, I have received ads for kite surfing on the side of my home page.

Similarly, I recently changed by mobile data plan and ever since, there has been an advertisement for Vodaphone’s new data plan on the side of my page. It seems impossible Facebook could know the details of my phone contract, but perhaps the mere fact that I am on Facebook regularly means I am a good target audience for affordable data products.

Below is a snapshot of Facebook’s Data Use Policy. Whilst it is extremely simple and easy to read, it raises some questions like:

What does it actually mean that we own our data?

What legal avenues are available to enforce these rights?

Is there a way we can extract our data and move or save it elsewhere?

Policies such as this are helpful and important but often vague, and fail to give meaningful examples to users of what the actual consequences of their participation could be. Facebook has certainly faced criticised for this before.

For those that are concerned about the invasive accuracy of data mining and predictive analytics, one has to wonder whether it is that different from any sort of audience research. Television ratings and research have been used to sell audiences to advertisers for years. Classic methods of measurement include written viewing diaries and the people-meter (introduced in 1987), to track television consumption in the home (Sullivan, 2013). Both these methods rely on volunteer provision of information by participants. In many ways, data mining is not that different, except that it has extremely widespread participation and as a result, is conducted on a much grander scale. This can result in targeted, efficient advertising and information about products that consumers might actually enjoy or value.  

Whilst data mining is considered by many as a form of surveillance, it is important that participants educate themselves about how their data could be used, however companies such as Facebook must also ensure honest and transparent policy disclosure.

References:

Dara Kerr. 2013. "Facebook faces criticism over its privacy policy." CNET. September 5. Accessed May 12, 2014. http://www.cnet.com/au/news/facebook-faces-criticism-over-its-privacy-policy/ 

David Cohen. 2014. "Facebook Audience Insights gives marketers an overview of their potential audiences." Inside Facebook. May 8. Accessed May 12, 2014. http://www.insidefacebook.com/2014/05/08/facebook-audience-insights-gives-marketers-a-better-overview-of-their-potential-audiences/ 

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

Sullivan, John L. (2013), “Chapter 4: Media ratings and target marketing”, Media Audiences: Effects, Users, Institutions and Power, Los Angeles: Sage

Woodford, Darryl, April 24, 2014. “Week 9 – Big Data and Telemetrics.” Accessed May 13, 2014. http://blackboard.qut.edu.au/

DNews. 2013. “Your Facebook Likes Reveal Everything.” YouTube video, posted March 12. Accessed May 11, 2014. https://www.youtube.com/watch?v=3SNeQXWn3xk

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KCB206: Week 9 Blog Post (New Media, Big Data and Telemetrics)

What is Big Data? (See Video)

The quantity of computer data generated online is growing exponentially. Retailers are building vast databases of recorded customer activity. Organisations working in financial services, logistics and healthcare are capturing more data, and public social media is creating extreme quantities of digital material. We constantly produce data, through social media, public transport, and GPS. But 'Big Data' is so much more than this. Daily, we produce 55 million pictures, 340 million tweets, and 1 billion documents. (GPredictive, 2013). Computers are able to extract meaningful information from Big Data, which will only advance along with the technology that propels it. 

The Internet and New Media technologies have enabled us to analyse millions of information in minutes, through patterns of usage and various Data available online. This process of predictive analytics has been defined as "technology that learns from experience (data) to predict the future behaviour of individuals in order to drive better decisions” (Siegel 2013, 11). He likens this to the 'Ensemble Effect', where Big Data can be used to Crowdsource and 'Supercharge Prediction."

For example, Video streaming website Netflix analysed the big data of their viewers (popular shows, watching patterns). This site relies heavily on predictive analytics and Big Data as 70% of movie choices arise from provided online recommendations (Siegel, 2013, 5:133). From this, they produced a successful online series with a popular combination of actors, directors, and story lines; these series include the popular 'House of Cards' and 'Orange is the New Black'. This exemplifies how an ordinary website has used Big Data technologies to generate value by using unconventional computing techniques, which provide a tailored, relevant and personal feel to customers time online. 

(Image Source) Popular Video Streaming Website Netflix uses Big Data to provide movie recommendations for customers.

Twitter and Big Data

"[Twitter] demonstrates a focus on the most important real-time stream message bus today." (Lang, 2013). Woodford, Prowd and Bruns comment on the nature of Twitter as exemplifying how influential Social Media is on attracting viewers in regards to things such as Television shows, where many users 'Live Tweet' about their favourite programs. Moreover, hashtags and social media interactions allow companies to discern audience reactions, whether that be through immediate live tweets, or conversations between friends with hashtags.  These online interactions assist in creating an environment of participation amongst fan bases, and encourage open discussion. This is clearly a successful strategy in garnering attention from non-viewers of the program, as Social Media is so intertwined in our every day lives, others' opinions undeniably have a great influence on Web users.

It will be interesting to see how technology progresses in implementing big data. I personally find hashtags, recommendations and other functions such as iCloud storage extremely useful, and as I use Social Media so often, it would be great if my future career -Hopefully within Film and Television- used these methods. 

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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. (Available on CMD)

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

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

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KCB206: Week 8 Blog Post (New Media, Politics and Engagement)

“Access to and use of wireless communication technology adds a fundamental tool to the arsenal of those who seek to influence politics and the political process without being constrained by the powers that be.” - Castells et. al.

As professed by Gladwell in his commentary on Social Media change, just as Social Media has revolutionised society, "the new tools of social media have reinvented social activism". We have established in earlier weeks that media is a platform reflective of societal change, and examined the many advantages of audiences becoming increasingly empowered and participatory due to our contemporary Web 2.0. Despite these obvious positives, the threat of corruption and exploitation remains just as it does in real life, especially in relation to political issues. 

The rise of the Web 2.0 and new media audiences has seen many blogs and social media users alerting others to pertinent issues such as breaches of human rights around the globe, prompting ultimate reform and raising awareness. Ultimately, social media has placed power in the hands of many minorities, giving voice to their concerns (Gladwell, 2010). This can effectively educate and empower the masses, but do participatory audiences have any real, lasting influence? Does a simple 'hashtag' change an International political issue?

How are new media implicated in power relations, politics and engagement?

As Gladwell states, "The Internet lets us exploit the power of these kinds of distant connections with marvellous efficiency." A modern day example of politics and engagement is the #BringBackOurGirls movement in relation to the recent Nigerian kidnapping of more than 300 girls at a school in Chibok. Many have criticised the movement for supporting Shirky's contention that New Media activism motivated people to "do things that people do when they are not motivated enough to make a real sacrifice." Reminiscent of 'Kony 2012', this 'Western Reaction' does tend to last a short amount of time, with many voicing concerns, but failing to take any action or make any 'real sacrifice' in relation to these events. 

(Image Source) Negative reactions to the #BringBackOurGirls global Twitter campaign.

Despite many perceiving these web campaigns as pointless and often ignorant, the millions of support messages and active responses from audiences worldwide surely can't be a bad thing- we can't expect the average twitter user to hold any real political power, nor one hashtag change horrific global events. Although they may not be directly contributing, it's often difficult for anyone to interject in complex political conflicts. These posts are still contributions, and raise awareness, as well as demonstrating empathy and compassion, which I find a welcome change from the majority of posts on social media. In other words, I find people forming opinions and voicing them more of a substantial act than mindless web browsing.

As demonstrated, new media can be fundamental in raising awareness for issues. It also allows us to examine how fluidly and immediately information can be shared across social media platforms such as Twitter, for a multifaceted range of purposes. The freedom, empowerment, and shareability of our New Media landscape is overall a beneficial feature, giving thousands of people a voice and the power to change, even if it is just by a hashtag.

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Reference List

 Gladwell, Malcom. 2010. “Small Change: Why the Revolution will not be Tweeted.” The New Yorker, October 4. Available at: http://www.newyorker.com/reporting/2010/10/04/101004fa_fact_gladwell?currentPage=all

Shirky, Clay. 2011 “The Political Power of Social Media: Technology, the Public Sphere, and Political Change.”In Foreign Affairs 90:1. Available at: http://www.foreignaffairs.com/articles/67038/clay-shirky/the-political-power-of-social-media

Faris, David. 2010. “(Amplified) Voices for the Voiceless.” Arab Media & Society, 11. Available at: http://www.arabmediasociety.com/?article=750

Mackey, Robert, 2014. "Can Hashtag Activism save our girls?" New York times. Avaliable at: http://thelede.blogs.nytimes.com/2014/05/07/can-hashtag-activism-save-kidnapped-nigerian-girls/

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Measurable Success

Success can now be collected and measured as numerical representations of online activity, in real time and updated constantly. TV shows can see how well the audience is responding to content unlimited by the dimensions of time and space. Success and popularity can be measured and compared by the count of interactivity from users. It can affect careers of politicians and celebrities, the lifespan of shows, can make or break technologies and companies, and is having a massive effect on how the world is being shaped. This blog post is going to look at a few examples of how the collection and results of big data has affected these entities.

  The popularity, particularly among younger demographics, of politicians and celebrities; films and TV shows, can be categorized and measured by the amount of interaction and following they have online. Politicians with a higher number of followers or likes appear more popular than their counterparts who generate less measurable data. Celebrities and their level of fame are relevant to their number of online supporters. Films and television shows are followed, re-tweeted, shared, and liked into popularity; low interactivity levels threatening to affect their lifespan and capital growth, both economic and symbolic (see more about types of capital in week 8 blog post).

  The ability to collect and collate big data enables the popularity of major figures to be easily displayed, interpreted, and analyzed (Siegel, 2013). This data can inform and affect political campaigns, pop culture, and the mainstream media (to name a few). For example, the data received from twitter informs news programs on the popularity and level of engagement audience members have to its specific segments. The show, for example The Project, is able to collect data and analyze the content to form decisions about its: 

-       Presenters

-       Segments

-       Content

-       Audience engagement in respect to the above aspects of the program

By analyzing the number of Tweets received by the show during the program, executives can make decisions about the program and how to better engage the audience (Woodford, Proud, & Bruns; 2014). This could be by stopping a segment, or replacing a presenter.

  New media conglomerates like Facebook and Twitter have technologies ingrained in their structure enabling mass data to be collected and displayed to the companies themselves, and users. What was previously reserved for conglomerates able to hire audience researchers, or extrapolate information from the ratings, is now accessible by almost all online entities. This process enables even the smallest of blogs to gather data about it’s reception and put it to use; possibly tailoring itself even more so to its primary audience’s wants. The ability to collect mass data has transformed media and communications research. The ratings and large-scale research companies are no longer the only bodies able to provide audience data. 

Bibliography

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

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Week 10 Blog: Big Data & Telemetrics

 “Social media is increasingly playing an important role in television” (woodford,2013)

 Social Media has grown dramatically over the past few years, to a point where TV shows have begun to incorporate it into their broadcasts in order to stir up as much discussion as possible whilst a show is airing. (Cohen,2014) Not only is it used to promote certain shows but it is also used to measure the success of a show, and one of the ways they do this is through the use of twitter.

 Discussions that appear on twitter throughout certain TV shows allow comparisons in genres between networks and countries (Woodford,2013) Comparisons are constantly being made amongst episode-to-episode and show-to-show in order to calculate the highest volumes. Social networks provide the same “granularity of information” (Woodford,2013). Siegel (2013) mentions that Wall Street predicted their stock prices based on the general public’s activities on Twitter. Firms such as AlphaGenius and Derwent Capital fund trading by following trends. (Siegel,2013) But how do they do it exactly? Woodford (2013) states that increased granularity is necessary in order to understand how Twitter uses are reacting to the content of particular broadcasts. They deliberate the maximum tweets per minute, as it is a form of engagement with the show (Woodford, 2013) “They key is to be able to evaluate episodes independent of their context” (Woodford,2013) So as to compare the different shows and seasons regardless of it’s audience size, they index each of the metrics against the average data for show seasons. However, more than volume is needed when comparing episodes via twitter. (Woodford,2013). Supplementary work is required to validate amongst the different genres and countries. The discrepancy was made obvious in ratings and tweets amongst the users, but the variation in the twitter audience was made to be more apparent. (woodford,2013)

Although, some shows have taken social media even farther than simply using twitter’s means of promotion to encourage audience participation. Some of these TV shows include: The Big Bang Theory, and Bones

The Big Bang Theory use social media in a way to get people to become more involved. They do this through Facebook, the GetGlue app, and rewards. Facebook displays teasers, and repost photos with dialogue to promote fan engagement. (cohen,2014) The GetGlue app allows people to know what their friends are watching and by using this app it allows their show to be circulated amongst the audience who will find it to be more appealing if they see their friends watching it. Rewards by liking, sharing, and following allows people to promote the show. (Cohen,2014)

 Bones allow people to partake in online conversations DURING their show to try and help each other to solve the mystery. It is the highest form of audience participation. These conversations are done via twitter, where the show actually reply to tweets an maintain open dialogue (Cohen,2014)

References: 

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

Cohen,Philip (2014). Television and Social Media Synergy Social Media Today. Available at: http://socialmediatoday.com/philcohen4/2252471/6-tv-series-integrate-social-media-their-

Woodford, Darryl, Katie Prowd and Axel Bruns. 2013. “Telemetrics: Towards Measuring Social Media Engagement with Television.”

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Week 9 Blog Post

“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 2014)

  Most people might think the word – data is quite boring. However, it somehow plays one of the most important roles in our daily lives. According to Siegel (2013) the truth about data is it embodies a priceless collection of experience from which to learn. Every medical procedure, credit application, Facebook post, movie recommendation, fraudulent act, spammy e-mail, and purchase of any kind – each positive or negative outcome, each successful or failed sales call, each incident, event, and transaction – is encoded as date and warehoused. This glut grows by an estimated 2.5 quintillion bytes everyday. It seems data has immerged into our society. Data provides a platform for all the information that we have produced in social media and collected it. Over the last decade, huge amount of attention in social media has been directed towards the idea of ‘convergence’ (Harrinton 2013). The new social media has changed the way how the content is distributed. Back to the old days, the only way to watch my favorite television show was to watch it when the local broadcaster chose to schedule it but now I can chose as many platform as I want from internet. It changed how, where and through what devices the television can be experienced. In addition, Twitter is certainly one of the most important social media that allows users to connect with other viewers in real time simply by hash-tagging. Twitter represents a remarkable opportunity that provides us with exactly such a means of inconspicuously observing the activities of television audiences. Supported by Harrinton (2013) the significant aspect of Twitter’s relationship with television concerns the opportunities the platform affords users for connecting with other viewers in real time, and engaging in a live, effectively unmediated, communal discussion of television programs. By using hashtags in Twitter it will connect with a group of Twitter users that extends far beyond one’s existing followees, audience members in this online community coalesce for the specific purpose of discussing the same piece of broadcast content. Users are able to engage with our users in a running commentary or conversation on what they are seeing, as it happens on their television screen. Twitter is no longer has been used as a backchannel, as a return channel as well: where tweets from viewers are then featured in the show itself (Harrinton 2013).  For instance, over 2000 posts has been tweet per-minute the #royalwedding hashtag on 29April 2011, as millions of people around the world watched Kate Middleton marry Prince William.

    Television has been well understood as a platform that easily catalyses audience discussion, interaction, fandom, and other social activity. Over time, that social activity has evolved, alongside changing cultural and political conditions, and the new affordances created by rapid developments in technology(Harrinton 2013). 

Reference list

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

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

Woodford, Darryl, Katie Prowd and Axel Bruns. 2013. “Telemetrics: Towards Measuring Social Media Engagement with Television.”

Woodford, Darryl. 2014. Week 9 – New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford). Retrieved from: http://blackboard.qut.edu.au/webapps/portal/frameset.jsp

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Big Data New ethical Dilemma

In less than a decade, the norms for online sociality have dramatically changed from emphasizing connectedness to aligning connectedness with connectivity  (Van Djick, 2013). 

This seamless integration of social media into our daily lives to the point where exclusion or non participation is seen as a form of marginalisation, (Light & Mcgrath, 2010) has thrown up increasingly important questions about ethics, privacy and the issue of Big Data – how individuals innocently release indepth personal information about themselves, their opinions, their likes and dislikes which is being aggregated into what is known as Big Data, and the realisation of its commercial value to marketers and brands.

Light & McGrath (2010)  argue a Benthamist approach, that technology should be used to facilitate the greatest good for the greatest number, but content developers are obsessed with a user oriented view, where by social networking sites are for people to play with, express themselves, connect,  mediate and transform information. They argue SNS developers create an illusion of individual control, whereas the operation and outcomes of the use of SNS are anything but transparent.

There is agreement in the literature on some of the ethical issues becoming apparent in our obsession with SNS. Sites such as Facebook encourage openness to the point of making limiting one’s personal information almost impossible (Van Djick 2013; Light & McGrath, 2010), raising issues of regulation of individual privacy, security of personal information and matters of identity.  As personal information moves more into the shared domain, it becomes less easy for the creator to manage.

Facebook for example, does not allow you to see who has been looking at your profile, it may connect you with people you have not sought as friends, it may exclude you from groups you wish to be part of and it may have consequences for your reputation you may not have anticipated, eg employers (Light & McGrath, 2010).

 A bigger issue has gathered traction: that of Big Data.  Every movement in social media and on the internet can be tracked and monitored. This information becomes valuable data with a very high commercial value.  Individuals are unknowingly creating digital currency and lining the pockets of the oligopolistic owners of social media sites, who have gone on carefully planned acquisition programmes to achieve vertical integration between their platforms and the capability to migrate data from one platform to another (Van Djick, 2013).

 Content by itself is worthless, but combined with metadata and behavioural and profiling data, predictive analytics and real time analytics become powerful segmenting, targeting and channelling gold for marketers and brand analysts (Van Djick, 2013).  In addition, undetectable algorithms work silently in the background to channel users in a seamless transition from social networking to commercial activity. For example, when users ‘like’ something, this makes others want that thing more, and marketers are able to construct a need.  Spiking traffic to a flu remedy website in a geographic area enables savvy marketers to target a flu remedy sales campaign to geographically identified users.

 Access to this type of information has undermined certain industries.  The ability to track cookies means that brands can do their own predictive and real time research and avoid the hefty costs of qualitative and survey based research consultants.  Ethnographic research is within reach for every brand which is SNS integrated.  Brands have easy and indepth access to the psychographic characteristics of their existing and potential customers, without having to pay consultants huge fees.

 Another ethical issue is the powerlessness of Governments to legislate to protect individual privacy and identity, and to prevent misuse of private information or to engage in unethical use of this information. They are at a disadvantage in developing and implementing standards and rules for SNS, as the activity is intangible and each person’s viewpoints of acceptable/unacceptable is entirely subjective.  SNS such as Facebook derive their success and power from the number of participants.  Peer pressure has morphed into a hybrid social/technological force – being SNS connected is an essential aspect of our lives.

As a result of this, in a crowded SNS landscape, Apple Microsoft and Twitter have begun to offer their users the possibility of not being tracked, turning the option into a competitive edge (Van Djick, 2013) but it will be interesting to see if the commercial value of the Big Data which is forgone in the exercise is worth the investment.  They have probably worked out another way to obtain it.

Reference List 

Light, Ben and Kathy McGrath. 2010. “Ethics and social networking sites: a disclosive analysis of Facebook”, Information Technology & People, 23(4): 290 – 311. 

Van Dijck, Jose. 2013. “The Ecosystem of Connective Media: Lock In, Fence Off, Opt Out?” In The Culture of Connectivity: A Critical History of Social Media, 155-176. New York, NY: Oxford University Press.

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The Dark Side of Big Data

With the significant domestication of new media it has become arguably impossible to avoid falling victim to big data. Technology is everywhere. In a modern world, it is essential for global communication via social media, as well as everyday tasks such as Internet research, watching television and even grocery shopping. However, to commercial entities, these daily practices aren’t as simple as they seem. For companies such as Nielsen SocialGuide and BlueFin Labs, which have recently been purchased by Twitter, these practices are a means of collecting data, tracking consumers and placing them into databases (Woodford et. al, 2013: Gray, 2014). Big data provides companies with a new way of thinking, enabling them to turn data into action (Woodford, 2014). The following post will discuss the dark side of big data, and in particular its invasion of our privacy as consumers and participators in social media.

An average person’s online data is worth around 10 cents to marketers. However, add pregnancy to the equation and that value jumps to $1.50 (Gray, 2014). American mother Janet Vertesi attempted to keep her entire pregnancy hidden from the Internet. The aim of this experiment was to avoid being flagged as an expecting mother, being tracked and exposed to an excessive amount of advertisement for baby products. However, this process was not as simple as signing off social media. Although keeping off social media ruled out cookies, bots and other online data collectors, in order to keep her pregnancy under wraps she and her husband avoided all use of technology. This included credit card use to ensure companies were not able to access or sell her purchase history information (Gray, 2014). All these efforts made to preserve the privacy of her pregnancy. In reflection of her experiences avoiding technology and big data, Vertesi said, “technologies are making us produce data in a particular way, and to opt out isn’t really an option….in certain situations it is critical to be able to opt out” (Gray, 2014).

This experiment highlights just how easy it is for corporate bodies to get their hands on our information and use it for marketing and advertising purposes. According to the Economist (2012), over 1800000 people can easily access your information under Facebook’s default privacy settings. Despite having privacy measures and policies in place, modern users of Web 2.0 are left vulnerable by lengthy and confusing Internet privacy policies. To make matters worse 72% of these polices still allow third parties to track internet activity (The Economist, 2012). Based on these statistics alone it is clear that in order to keep our private information private we must be extremely conscious of the ways in which we chose to participate in social media.

Big data does indeed present a threat to the privacy of new media users. However, when it comes to social media we must consider who the onus is on to keep our information private. Even greater, if we are really that threatened by big data invading our privacy on social media then perhaps we shouldn’t be posting our private details in the first place.

 REFERENCES

Gray, S. (2014). One woman’s attempt to hide her pregnancy from big data — it’s more difficult than you’d expect. Retrieved May 11, 2014 From http://www.salon.com/2014/04/28/one_womans_attempt_to_hide_her_pregnancy_from_big_data/ 

The Economist. (2012). The Dark Side of Big Data. Retrieved May 11, 2014 From https://www.youtube.com/watch?v=raJOkguPrH4

Woodford, Darryl, Katie Prowd and Axel Bruns. 2013. “Telemetrics: Towards Measuring Social Media Engagement with Television.”

Woodford, Darryl. 2014. Week 9 – New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford). Retrieved from: http://blackboard.qut.edu.au/webapps/portal/frameset.jsp

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Week 10. Big Data - What do they want with our tweets?

To see the impact of new media on today’s society, all you need to do is turn on the TV. There you might find a hashtag for the show - prompting you to tweet about what you are watching. Or maybe the username of the official twitter account of the show or it’s cast members - anything to get you talking and sharing when it comes to the content in front of you. But surely they don’t just want you to make some new friends, right? So why exactly do these shows and networks want you to tweet, and what do they get from it?

Big data allows for information to be collected from social media websites, particularly Twitter, that can clearly portray who is tweeting about what, when. In terms of television this comes in handy when gauging audience reaction, and as an indication of the relative success of each episode, which can even be dissected further into demographics such as country and other interests. This  data is measured via a “Twitter Excitement Index”, which can “Understand how Twitter users are reacting to the content of particular broadcasts”. This valuable data provides networks with almost instantaneous feedback and can even give an estimation of the episode’s ratings - without having to wait for the traditional ratings and reviews to be released. The data gathered can also be used to generate predictions for future episodes and audience reactions, using patterns and information from data obtained previously. This new method of data collection uses new media to benefit from social media’s need to share, and the speed at which they do so, to accurately identify vital data and reactions, in a way never possible in the past. 

It is fairly common these days for a TV show to prompt you in some way to take your viewing experience further and share your opinions, reactions, or just the fact that you are watching the show on social media. Some examples of shows doing this include The Voice, which suggests viewers tweet using the hashtag of the title of the show, and Pretty Little Liars, which gives audiences suggested hashtags throughout the episode of events and topics that may spark conversation between users. Their aim is to get the viewers talking about what they are watching in any way they can.

So why are we being encouraged to tweet, and how is anyone benefitting from this data? Networks want information about who is watching what and what their reaction to the content is, and they need us to share in order to collect this information. Whilst users may not be aware that their activity is being collected, they seem to have no problem sharing it with the world. 

References 

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

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

Woodford, Darryl, Katie Prowd and Axel Bruns. 2013. “Telemetrics: Towards Measuring Social Media Engagement with Television.”

Woodford, Darryl. 2014. Week 9 – New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford). Retrieved from: http://blackboard.qut.edu.au/webapps/portal/frameset.jsp

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Convergence: Week 10

Cultural and media studies have for many years presented a convincing argument that television, like other popular media, presents content that is for entertainment, pleasure and political messages that are perceived by the general population in many different ways. This form of entertainment is often re edited and used over and over again in a variety of ways to share, develop social relationships and present associated messages via different mediums. Like most things in the modern age, television has evolved as a medium and how the audience interacts with it in the digital realm (Harrington, 2013).

Twitter has forged an integral relationship with television in the digital age, where people share their unedited perception of television content in real time and in some cases of reality television, their thoughts are actually shared in real time on the program as it is shown live.

Through the utilisation of hashtags and connection with followers and other users of twitter, people can discuss and share their thoughts on broadcast content in real time. In the past these types of conversations were often had, but were limited to physical, time and social relationship limitations. In many cases this was often referred to as “water cooler” talk at work, where people would discuss what they had seen the previous evening at work the next day, with their work colleagues, customers and other daily acquaintances. Now these conversations and opinions can be expressed in real time with a much wider audience engaging, some known to a person and some total strangers. This can be referred to as the “virtual lounge room”. Television shows will now endorse this type of discussion through advertising a hashtag associated with their show (Harrington, 2013).

Television coverage of twitter conversations associated with the actual program are time dependent and can only be used when the content occurs live, such as the modern day fascination with reality television.

Television has been a well-accepted medium in modern society and often polarises public opinion, interaction, social activity and creating fan bases. In modern society the rapid evolution of social media has not become a competitor of television but a complimentary medium where the two can interact and social media will often be a promotional vehicle and interactivity for the television program (Woodford, 2013). This has not been lost on the advertising and business world where data collection services such as Nielsen now collate data from social media for marketing purposes. This has forced these data collectors and advertisers to broaden their analysis of metrics and what and how they measure audience numbers and participation (Woodford, 2013). 

When measuring these participation levels, an excitement index may be used to analyse the interaction and engagement with the television program. There will be programs which generate consistent twitter numbers evenly throughout the show, while others will have peaks and troughs, indicating that the audience is being prompted by a “hook” in the program content to engage. In many cases this is measured through maximum tweets per minute, as an engagement metric (Woodford, 2013). There is also an ability to measure the volatility of twitter discussions about a television show in comparison to other shows, genres, networks and even countries or cultures, independent of the program total audience. The advantage of these measures is that they can be calculated easily and quickly for a high volume of traffic on an episode to episode basis, which may also be used for other social networks that has the same transparency of information (Woodford, 2013).

This convergence of real time television and twitter are just the starting blocks to social medias complimentary engagement with television as an evolutionary step in the social and public media arenas.

  References:

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

Woodford, Darryl, Katie Prowd and Axel Bruns. 2013. “Telemetrics: Towards Measuring Social Media Engagement with Television.”

Woodford, Darryl. 2014. Week 9 – New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford). Retrieved from:http://blackboard.qut.edu.au/webapps/portal/frameset.jsp

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Week 10: Big Data and Telemetrics

This week’s readings discuss the ideas and purposes of big data and telemetrics which are ways academics and corporations gather, process, analyse and visualise data. The information we as internet users put into the public domain is part of this process; we human beings ARE data. Big data is used across industries for various purposes such as market research, election and stock market predicting, and predicting behaviours and analysing them. Telemetrics involves the study of the relationship between social media use and the performance of television shows. Firstly this blog will look at the predictive analytics of big data and its corporate value, as discussed in the reading, Predictive analytics: the power to predict who will click, buy, lie or die. Siegel defines predictive analytics as “technology that learns from experience (data) to predict the future behaviour of individuals in order to drive better decisions” (2013, 11). This data is us – the personal information that we share, generally online, that is then collated by corporations to target the right people for the right products and services. Each “incident, event and transaction” that we are involved in, becomes data that is stored; an amount that is growing by around 2.5 quintillion bytes everyday (Siegel 2013, 3). The ability to predict how a consumer is going to act, not specifically but vaguely, is an incredibly powerful tool for organisations to harness. This “machine learning” is a process that “has its roots in statistics and computer science” (Siegel 2013, 4). However, prediction isn’t about knowing for certain which people will act in a certain way, but knowing which people generally behave in particular situations. After all, as Siegel says, “A hazy view of what’s to come outperforms complete darkness by a landslide” (2013, 11). Telemetrics is studying social media, particularly Twitter, and the relation to TV performance. “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” as well as being a gauge in determining the success of particular programmes (Woodfood, Prowd and Bruns, n.d). In Telemetrics: Towards Measuring Social Media Engagement with Television, the authors discuss what they call the “Twitter Excitement Index” which they use to “understand how Twitter users are reacting to the content of particular broadcasts” (Woodfood, Prowd and Bruns, n.d). This is more accurate than traditional ratings, as through Twitter researchers are able to see what audiences think of certain shows, as opposed to ratings that can only show the size of the audience. Using a Seasonal Model, Woodford, Prowd and Bruns have examined the trends in television show performance, noticing that a show’s pilot rates higher than average then drops about mid-season, before continuing to build up towards the final episode. Although analysing Twitter seems to be more of an accurate depiction of a show’s performance throughout a season, as opposed to the traditional ratings system, it is flawed in that it only represents those shows that have an audience segment actively using social media. In conclusion, I am beginning to see how this all relates back to my discipline (film and TV), as predictive analysis is relevant in the film industry, such as predicting which demographic is more likely to watch particular genre films and how to market them accordingly. Also, telemetrics are very useful in the TV industry as what audiences say on social media affects a show’s performance and measures its success with certain audiences. REFERENCES Siegel, Eric. 2013. Predictive analytics: the power to predict who will click, buy, lie or die. Hoboken: Wiley. Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming). “Telemetrics: Towards Measuring Social Media Engagement with Television.”

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Week 9 Readings: Using Twitter as a Predictive Tool.

For this week I will be focusing on the use of twitter as a predictive tool.

In one of the readings for this week, Siegel (2013) asserts that predictive technology is commonplace for almost all interactions we undertake, and the central driving force behind this is the notion of machine learning, creating algorithms that allow a computer to ‘learn’ from datasets and to predict future outcomes from this 'knowledge'. Even if this prediction is not all that accurate, as Siegel puts it, “a hazy view of what’s to come outperforms darkness by a landslide.”

Twitter has been found to be a useful tool to predict a wide variety of events. As per the lecture for week 9, twitter is unique in that the data is readily accessible, and hashtags make for an easy way to collect data about specific issues and how they link together. So how can the data predict the stock market?

I chose the stock market example because of it is notoriously difficult to predict well. In this report on Gizmodo the author looks at a report released by the Cass Business School in City University in London that found 10 million hypothetical ‘monkeys’ outperformed the market-cap index over three year periods 60% of the time. In other words, stocks chosen at random resulted in a higher than coin toss chance of better returns than placing your faith in the market, and the people who ‘predict’ it.

Apple enthusiast and stock market genius

In this report, researchers found that using Twitter to predict the ‘public mood’ surrounding important news events did have a significant correlation with how the Dow Jones closed 3-4 days later. The study also found a reduction of mean average percentage error of more than 6% by including twitter analysis. But what does this mean in a practical sense?

In this article, the example is given of the social analytic company Dataminr, which was able to release information to its clients about an adverse event occurring on a cruise liner 48 minutes before it was released in mainstream media. When this event hit the mainstream media, it caused the share price in the cruise liner company to drop by 2.9%. Dataminr found this using an algorithm that compared newsworthy tweets from the past to predict newsworthy tweets from the future, not using hashtags but instead the language used in the tweet itself. Dataminr was contacted by a client directly that stated this information had saved them money.

I also chose the stock market because of the legal questions raised by usage of social media data, as it is my other area of study. If we take the above scenario, if you were to find out about that event in the course of your employment and you acted on that information before it was in the public domain to gain an advantage for yourself, you would be engaging in insider trading, which is illegal.

However, if you were a third party who found out this information from Dataminr through a subscription to their service and acted on it before it was in the public domain for personal gain, it would be much more difficult to get catch you under insider trading legislation, if not impossible. The end result is practically the same; you have gained from knowledge before it was widely available. One is illegal, one is arguably not.  

  Sources:

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

Paul Cologan. 2013. “Monkeys Make Better Stock Market Traders Than People: Study.” Gizmodo, April 11. Available at: http://www.gizmodo.com.au/2013/04/monkeys-make-better-stock-market-traders-than-people-study/

Stan Alcorn. 2013. “Twitter Can Predict the Stock Market, If You’re Reading the Right Tweets”. Co. Exist, April 25. Available at: http://www.fastcoexist.com/1681873/twitter-can-predict-the-stock-market-if-youre-reading-the-right-tweets

Bollen, Johan, 2011. “Twitter Mood Predicts the Stock Market”. IEEE Computer 44 (10): 91-94. Available at: http://arxiv.org/pdf/1010.3003.pdf?iframe=true&%25&%25

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Week 10: #tweet tracking

The traditional role of media, the producer and the audience has been completely revolutionized due largely to the inception of New Media. Technological convergence has demonstrated that numerous media devices and technologies have ‘been replaced by media content which is accessible across a range of technologies’ for example the computer or smart phone (Harrington, 2013). Although, much speculation exists to whether or not such change has positively or negatively affected the new media forms specifically the audience and television directly. This week’s readings focuses specifically on the role of big data and telemetrics within new media in particular Twitter.

Harrington (2013) mentions that funnily enough ‘new technologies rarely result in the displacement of long-standing audience practices, but are typically blended into existing routines and activities instead.’ Interestingly, Gray and Lotz (2012), highlight the face that ‘television is neither ‘beating’ nor ‘losing’ to new media’ the convergence of such technologies has allowed the audience a different way of watching television, rather than decreasing our interest.

Big data can be defined as ‘a currency across the industry... business intelligence and data markets including social media and online behavioral data’ (Woodford, 2014). Many practical challenges arose in previous studies relating to Telemetrics for example data accessibility, platform volatility and ethics (Woodford, 2014) . The public domain can now be monitored and studied due to the inception of a concept called ‘Telemetrics’ where companies are able to ‘establish metrics for social media’ through what users post to the public domain, most of the time sub-consciously  (Woodford, forthcoming). 

The ‘Twitter Excitement Index’  was created to determine the audience’s reaction to particular broadcasts (See Example Above). The number of tweets throughout the show and the average tweets per minute are recorded, demonstrating the audiences ‘Twitter Excitement Index’ reading. Television corporations in particular now promote the audience participation through the use of social media throughout television shows for example, the excitement of ‘The Voice’ where cleverly the audience is encouraged to tweet throughout the show and are constantly reminded throughout the show by onscreen pop ups.

A number of big data companies, not only within the Television industry now offer promotions, discounts and freebies to it’s Facebook and Twitter followers. A real life example is a post on The Iconic Australia’s Facebook page which encourages Facebook Friends to follow the Twitter Page for promotional bonuses (See Example Below).

In conclusion, technological convergence has encouraged audience participation through many multimedia devices and social media networks. This has allowed big data companies to use multimedia platforms and telemetrics to determine audiences satisfaction and gratification through interaction levels and tools such as ‘Twitter Excitement Levels.’

References

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

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

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

Woodford, Darryl, Katie Prowd and Axel Bruns. 2013. “Telemetrics: Towards Measuring Social Media Engagement with Television.”

Woodford, Darryl. 2014. Week 9 – New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford). Retrieved from:http://blackboard.qut.edu.au/webapps/portal/frameset.jsp

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Week Ten: It's a data invasion.. Not really.. But really

Social media plays a big part in our lives today, a facet of which has just started to take off is through our consumption and interaction with our old friend, the television. Increasingly today we see our favourite television shows throwing in references to social media, whether it be prompting you to a page to get a sneak peak of what’s coming up next week, or to ‘join a conversation’ with others, more and more people are getting involved, in ways not previously available or imaginable. So how does this benefit anybody?

 The first group of people that this level of audience participation benefits is of course the content creators and television networks who are distributing the content. The Twitter excitement index, introduces the ability to measure the level of discussion on Twitter about a specific show, whether before, during or after the show, this allows those who are distributing the content to see fans reactions directly, good or bad, and see how audiences change week to week, even country to country (Woodford, 2014). This is, when it all boils down to it, a new way to gather data from audiences in a way that is vastly quicker than content distributors have ever had, while the information gathered and analyzed through Neilson SocialGuide and other companies has not yet publically published, statistics can be drawn from sporting events and the like to draw on conclusions as to audience interaction (Woodford, 2014). 

 I’m sure we’ve all experienced this in some way shape or form, either we have ourselves participated in a shows discussion or gone to their Facebook or Twitter page to see their sneak peak of next weeks show. But what are we actually doing when we do this, besides annoying our friends who couldn’t care less what happened on My Kitchen Rules. We’re, as mentioned above, participating in the data collection for large corporations who can take it, and with a public forum such as Facebook, use it in a way that will build a new series of shows that we surprisingly like! By gathering data corporations have a priceless collection of experiences from many different worlds from which they can learn (Siegel, 2013).

 In summing up, while we think we’re just having some fun and talking with people of a like mind, watching the same television shows, there is a much greater purpose to us partaking in the online debate and hashtagging our lives away on social media. Corporations are collecting big data in ways they haven’t before been able to, which can be a scary thought, our lives are able to be accessed by a greater number of people, quicker and easier than ever before and overall we seem pretty happy with that, because we get to watch shows that appeal to us.

 Bibliography

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

Woodford, Darryl, Katie Prowd and Axel Bruns. 2013. “Telemetrics: Towards Measuring Social Media Engagement with Television.”

Woodford, Darryl. 2014. Week 9 – New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford). Retrieved from: http://blackboard.qut.edu.au/webapps/portal/frameset.jsp

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Tweets for Tracking - Week 10 Blog

“Data embodies a priceless collection of experience in which to learn.” (Siegel, 2013, p.3)

As every detail of our lives is put up for the world to see, from our relationships to the places we go; this thing we call information-overload is adding to the complexity of everyday life. Opting out of these practices is becoming less prominent and by no coincidence more prevalent. Information is at our fingertips, highly accessible and uploaded by the minute, causing a saturated mediasphere. The cluttered apps and websites, together with content and activities uploaded by the minute have contributed to the explosion of data that we as society now face (Woodford, 2014).

Marketers and social-media analysts however are not the only ones who translate data within these new-media interfaces (e.g. The Audience); the concept behind ‘tracking’ is also understood by the everyday person. We scan our news-feeds and “Facebook-stalk,” which in turn is a form of simplified analysis confirmed by The Economist (click here to watch video). Siegel (2013:) expresses that “with such an abundance of information comes new ways of depicting the world.”

In saying this however, data does not just encapsulate mere representations, its continuum emanates to become opportunities for innovation that challenge our traditional understandings of concepts. Bruns (2013:27) verifies this by considering hashtag engagement as an accurate measure of communication exchange. Pinpointing the different relationships between user and the tweeting purpose relates back to the different layers that are fundamental aspects of human communication.

Glancing at our phones while watching television has become, as Harrington (2013) puts it: “a programmed routine activity,” as we enter the digital age. This notion puts forward a valid question:

In practice how do traditional modes of communication, like the television industry sustain high ratings as we shift to online content?

Following the theme of Twitter and its role in our society, Bruns (2013) looks at how television is no longer entirely an outmoded platform, thanks to new-media’s strategic business models (Harrington, 2013).  In juxtaposing how social-media data occurs in all demographics, Twitter especially has emerged into a powerful tool for expression and capitalisation. Microblogging and its word limit induce us to not only tweet regularly, but more so on ‘easy-to-blog-about’ activities, such as the T.V programs we’re watching (Holt, 2013).

Watch video here: (http://www.youtube.com/watch?v=5yxuljHX09I)

Data extrapolates how transmedia prompts audiences to tweet about content on the show in an attempt to keep viewers offline (Woodford et.al, 2013). While Twitter has the abilitiy to convert television, as a once anti-social medium into a highly interactive one (Harrington, date), the audience hashtag engagement “can be instrumental in publicising content, resulting in new followers and participants” (Bruns, 2013a).

Basically, users care as much about being noticed as they do following the show. Limitations to data collection cannot feasibly identify hard-core fans as some people are spread across a range of hashtags, inclined to strengthen their follower-fan base (Harrington, 2013).

Photo source: (http://www.tvtonight.com.au/2011/02/my-kitchen-rules-to-add-mkr-tweets.html)

As no piece of data works in isolation, presenting the frameworks (i.e. participatory media, web 2.0, democracy, identity, empowerment, collectivism, capitalism etc.) in trends, fluctuations, predictions and findings have decoded this once binary language into various aspects of research and development (Nielsen, 2013). In previous years there had been little or no way of realising that a Tweet came with many repercussions and could say a lot about us as human beings and relationships. Today, more than ever before, data binds all; and is central to, both social norms in Internet and real life processes are applied to ourselves and our interpersonal transactions.

References:

Bruns, Axel. 2013. “Chapter 2. 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.

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.

Holt, Richard. March 18, 2013. “Twitter habits: TV the most popular thing to tweet about. http://www.telegraph.co.uk/technology/twitter/9937356/Twitter-habits-TV-the-most-popular-thing-to-tweet-about.html  

Nielsen, 2013. “Nielsen Launches ‘Nielsen Twitter TV Ratings.’ http://www.nielsen.com/us/en/press-room/2013/nielsen-launches-nielsen-twitter-tv-ratings.html

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

Twitter & TV. 2014. “Use the power of television to grow your impact.” https://business.twitter.com/twitter-tv.

Woodford, Darryl, Katie Prowd and Axel Bruns. 2013. “Telemetrics: Towards Measuring Social Media Engagement with Television.”

Woodford, Darryl. 2014. Week 9 – New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford). Retrieved from: http://blackboard.qut.edu.au/webapps/portal/frameset.jsp

Sarah Ford n8860599

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Big Data is changing our world - Week 10 Blog Post

What is Big Data?

Big data is an advancing technology trend that opens the door for people understanding the world and making decisions. According to IDC estimates, The data is growing all the time at 50 percent a year or even more then doubling every two years (Lohr 2012).

Improved access to information is also fueling the Big Data trend. For example, government data such as employment figures and other information has been steadily migrating onto the internet. In 2011, Australian government opened the data doors further by starting Data.gov.au, a Web site that makes all kinds of government data accessible to the public (data.gov.au 2014).

 Why is the big data so important?

While much of the news about big data and its benefits has come from larger companies, which traditionally deal with larger data sets such as Google, which analyzed frequency of searches for related phrases to help rapidly identify the answers—smaller businesses also have the opportunity to gain benefits from the emerging set of tools that allow them to tap the market.

 The data increase isn’t just more information on the same stuff, but entirely new data streams generated by countless digital sensors on nearly everything from shipping crates of goods produced thousands of miles away to location data stored in a teenager’s smartphone.

 Linking these sensors and their corresponding data to computing intelligence helps businesses better track shipments, define most efficient truck routes for goods, and target advertising to potential consumers.

The predictive power of Big Data is also being explored in fields like public health, economic development and economic forecasting (Siegel 2013). Researchers have found a spike in Google search requests for terms like “flu symptoms” and “flu treatments” a couple of weeks before there is an increase in flu patients coming to hospital emergency rooms in a region.

 Technology is improving Siri, powering driverless cars, improving cancer treatment and even being called Big Brother. But “big data” is what makes it possible, and why it’s so important.

 The big data is changing the world

Big data became the industry's hottest buzzword in 2012, but the impact of that technology on our day-to-day lives will be realized for years to come

 From healthcare, to sports, to the way we elect a president, big data will make big changes to the way we live our lives. Our ability to manage and analyze the massive amounts of data spawned by new data sources like social media.

 There will be many other application and new categories as the tools become more widespread. The powerful concept may revolutionize the planet with tools that can combat poverty, illness and crime while fueling unexpected and explosive innovation. Eventually, every aspect of our lives will be affected by big data.

 Reference List

Clark, Dorie. 2014. “Data visualization is the future”. Accessed May6, 2014. http://www.forbes.com/sites/dorieclark/2014/03/10/data-visualization-is-the-future-heres-why/

Data.gov.au. 2014. About. Accessed May 9, 2014. http://data.gov.au/about

 Lohr, Steve. 2012. “The Age of Big Data”. The New York Times, February 11. Accessed May 6, 2014. http://rkbusinessschool.com/uploads/2_The_New_York_Times_on_The_Age_of_Big_Data.pdf

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

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New Kind of Action

New media technologies are facilitating new types of activism dependent on networks and loose personal ties. This new type of activism allows long-term public conversation around various issues, instead of large-scale and ‘quick’ public events associated with other types of political activism. As noted by Malcom Gladwell (2010), “Where activists were once defined by their causes, they are now defined by their tools.” The tools regular citizens have access to has enabled the traditional relationship between political authority and mainstream will to be changed, and facilitate the previously powerless to collaborate, coordinate, and give voice to their concerns (Gladwell, 2010). Although this process has birthed some high-risk activism (i.e. violent protests, opposition of forces implementing violence), the Internet’s role in political and social reform is much more passive and focused on low-risk action than previous activist movements in history.

This is due to the loose-tie networked nature of social media. While social media permits a much larger number of personal connections to people, these connections lack strong emotional ties (Gladwell, 2010). It’s this close-but-distant aspect of new media technologies that facilitates new activism behaviors, most significantly ‘slacktivism’.  Political and social campaigns that seek minimal action from supporters (i.e. liking a page, sharing a video) usually offer social acknowledgement and praise in return for support (Gladwell, 2010). Gladwell noted, ‘social networks are good at increasing participation by lessening the level of motivation that participation requires,” (Gladwell, 2010). In other words, the Internet and its technologies make it easier for smaller and smaller actions to still be considered a worthwhile participation. Although slacktivism can cause a legitimate shift in public opinion, it is not as useful as traditional activism methods in the hopes of causing a significant systemic political change (Shirky, 2011).

Slacktivism is coherent with the environmental approach to Internet freedom outlined by Clay Shirky (2011). Shirky’s environmental approach to Internet freedom outlines that there should be a focus on long-term goals and changes built on a strong public sphere for conversation (Shirky, 2011). It puts emphasis on the conversations taking place in the public sphere as the core and cause of political change, and new media technologies as a tool to strengthen the public sphere long-term.  The idea that the media has a significant role in strengthening the public sphere and consequently supporting social change originated with the printing press, and has just expanded since the creation of the Internet’s new media technologies (Shirky, 2011). An example of this kind of activism is Gary White and Matt Damon’s website water.org – which is a global campaign bringing attention to the lack of drinkable water in some areas of the world. The site uses social media as a rallying point for supporters, and does not have a huge ‘call to action’, simply focuses on the small things supporters can do for the organization, mainly in an online environment.

While real-world activism still is important to the reformation of some political and social systems, new media technologies have enabled a new form of activism to come into existence: one focusing on long term effects, and the small contributions by the masses.

Bibliography

Gladwell, M. (2010, October 4). Small Change. Retrieved May 3, 2014, from The New Yorker:http://www.newyorker.com/reporting/2010/10/04/101004fa_fact_gladwell?currentPage=all

Shirky, C. (2011, February). The Political Power of Social Media. Retrieved May 3, 2014, from Foreign Affairs:http://www.foreignaffairs.com/articles/67038/clay-shirky/the-political-power-of-social-media