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image source :http://steadman.io/blog/2013/01/06/25-quintillion-bytes-of-data-every-day/

Every single move of us in the digital world can turn into a datum. Everyday, we create 2.5 quintillion bytes of data, which equal to 2,500,000,000,000,000,000,000,000,000,000 bytes! This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data. (IBM, 2014) 

The value of Big Data is not in the data itself. The value comes from the highly useful and actionable insights powered by custom algorithm development, efficiently processing huge data sets, machine learning and data visualisation. (DiUS, 2014) 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 the new knowledge gained, prediction is possible. (Siegel, 2013) 

Using data to make prediction was not a new idea. Company use their past sale records to predict their benefit in the coming season. The prediction will affect the budget setting for different expense such as media fee and the rise of salary of employees. Government conduct census and use the result to predict the country’s/ city’s growth, hence to decide policy or budget for health care, education, public service and infrastructure etc. Those predictions are all about probability and statistic. The result will be more accurate when the sample size is closer to the universe and more factors are effectively considered. In the other words, the more relevant data fed for machine leaning the more accurate the prediction is. Therefore, the accuracy of prediction are advanced by the growth of technology in big data usage. 

image source :http://www.linkedin.com/today/post/article/20140306073407-64875646-big-data-the-5-vs-everyone-must-know

Organizations can have more accurate prediction due the 4Vs of big data technology: volume, velocity, variety and veracity. Volume refers to the vast amounts of data generated every second (Linkedin, 2014). As mentioned, the more relevant data used the more accurate the prediction is. The big data technology provide affordances to store and use these data sets with the help of distributed systems, where parts of the data is stored in different locations and brought together by software. Velocity refers to the speed at which new data is generated and the speed at which data moves around (Linkedin, 2014). Data is meaningless unless we are possible to analysis it and conclude the result before the data is ‘outdated’. Big data technology allows us now to analyse the data while it is being generated, without ever putting it into databases. Variety refers to the different types of data we can now use (Linkedin, 2014). As I said in the last paragraph, the more factors are effectively considered the more accurate the result will be. So, variety of types of data are under consider the prediction will be more reliable. Veracity refers to the messiness or trustworthiness of the data (Linkedin, 2014). 1 in 3 business leader don’t trust the information they use to make decision (IBM, 2014). Even big data can predict the future by statistic calculation, however, many forms of big data, quality and accuracy are less controllable. Big Data service provider like IBM, Linkedin, DiUS work to help keep data clean and processes to keep ‘dirty data’ from accumulating in systems.

image source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

Reference:

DiUS. 2014. “predictive analytics”. Accessed May 10, 2014. http://inside-bigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/ 

IBM. 2014. “Big data at the speed of business”. Accessed May 10, 2014. http://www-01.ibm.com/software/data/bigdata/

LinkedIn. “Big Data: The 5 Vs Everyone Must Know”. Accessed May 10, 2014. http://www.linkedin.com/today/post/article/20140306073407-64875646-big-data-the-5-vs-everyone-must-know

Week 9 of Internet, Self & Beyond looked at new media, big data and telemetrics. Big data and telemetrics are major topics within themselves, so I have chosen to focus on big data.

Big data has now reached the point where its size is simply unfathomable. It is therefore also impossible to store, monitor, analyse and create value from this data without digital assistance. Gathering data through Twitter hashtags, for example, is a relatively easy way in which to capture, analyse and ultimately utilise data. The data is grouped into similar topics, and the volume/velocity of specific hashtags can easily be measured through computer programming.

Dr Axel Bruns’ piece on politicians’ use of Twitter provided a noteworthy look at the analysis potential of Twitter information, and ultimately how conclusions can be drawn from this. Upon review of a set of Twitter data he writes, “both former Prime Minister Kevin Rudd and his successor Julia Gillard have managed to attract substantially more followers than their former and current opponents, Malcolm Turnbull and Tony Abbott – even in spite of the latter’s commanding lead in recent opinion polling.” (Bruns, 2013) This is information that could not be gathered through older means of political polling, and contradicts the polling conducted previously, so he has then further explored why this might be the case. In this instance the data has acted as a check and balance for that which was obtained previously.

Monitoring likes and clicks on Facebook is a similar system. Technology has again afforded us the ability to gather the data in a systemic and logical way, so that the results of the data analysis can be translated into value by illuminating trends, for example.

What I found extremely interesting about this process, is the amount of personal data that is collected, and the way in which that data is used. As The Economist has illustrated through their video The Dark Side of Data (2012), the amount of personal information uploaded to Facebook is nothing short of colossal, and the way in which it is distributed is not exactly what users imagine when they take the 300,000,000 pictures that are uploaded to Facebook daily. 

Without even considering the private distribution of our personal data by Facebook to other companies, The Economist (2012) provides that 1,803,330,457 people have access to your information under Facebook’s default privacy settings. That includes advertising and marketing firms, data analysis companies, government agencies and anyone else who may be interested in your contributions to big data.

In 2012 an article was written about the specific marketing strategies enabled by big data collections. “One of the sub-stories in the article pointed to Target, which in its quest to identify and exploit a new market segment of expectant mothers, alerted an American father to his teenage daughter’s pregnancy – when she received a custom-designed catalogue targeted at pregnant women.” (Li, 2013) This clearly demonstrates how enormous the implications of social media contributions to big data can potentially be.

Big data is inevitable in this digital era; though it appears we may need to explore avenues of controlling or policing its use for financial gain. Unfortunately, like copyrighting online material, this seems like an impossible task.

-          Emily Charles (N8552886)

References

Bruns, Axel. 2013. “Follower Accession: 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/

Li, Christine. 2013. “Big Data Gone Wild: The Ways in Which Companies are Misuising Your Data”. Accessed 10 May, 2014. http://vibewire.org/2013/08/big-data-gone-wild-the-ways-in-which-companies-are-misusing-your-data/

Sauter, T, 2014. KCB206 Internet, Self & Beyond: Week 10 lecture notes.” Accessed May 10, 2014. http://blackboard.qut.edu.au/webapps/portal/frameset.jsp?tab_tab_group_id=_4_1&url=%2Fwebapps%2Fblackboard%2Fcontent%2FlistContent.jsp%3Fcourse_id%3D_108110_1%26content_id%3D_5232451_1

By the growth of Web 2.0, the traditional relationship between political authority and popular will has been upended, making it easier for the powerless to collaborate, coordinate, and give voice to their concerns (Gladwell, 2010). Hence, social media have become an important tool for nearly all of the world’s political movements (Shirky, 2010). As Web 2.0 provide us an easy way to give our voice in political issue and engaging with it, people think Web 2.0 is rising public’s activism. However, people on the other side argue that online activism is differ from the real activism and Web 2.0 can merely increase people’s online contribution but cannot generate any real life respond or actions.

  image source: http://www.agent-x.com.au/comic/slacktivism/

Is a click/ like/ share equal to participation? The words “clicktivism” and “slacktivism” which defined Oxford dictionary as “the use of social media and other online methods to promote a cause” and “actions performed via the Internet in support of a political or social cause but regarded as requiring little time or involvement” respectively. Those two criticism argue the online activism is diametrically difference with activism at the level of contribution. Gladwell point out that social media is effective to get people involved in easy task but not high-risk activism. Social networks are effective at increasing participation—by lessening the level of motivation that participation requires (2010). Our voice/ opinion can be express as easy as pressing a click/ like/ share on the social media platform, it makes people more willing to participation in political debate. But when a higher level of motivation is required social media does not work anymore. Traditional political activism like protests need participants’ time and effort, sometime participants even need to take the risk to break the law and being caught. Therefore social media generate activism.

  However, there are successful examples showing social media do help to rise public’s activism even in the offline world. Egyptian Revolution in 2011 was held by the use of social media tools. An Egyptian activist succinctly tweeted during the protests there, "We use Facebook to schedule the protests, Twitter to coordinate, and YouTube to tell the world."(Howard, 2011). Iranian election protests during 2009 to 2010 which also called “Twitter Revolution”, the government choked off the flow of news via radio, TV News and press, no information can leak from the capital. Protesters' reliance on social media like Twitter, Facebook and Youtube to spread information nationally and internationally, and communicated with each other (Washington Times, 2009).

  image source :http://commons.wikimedia.org/wiki/File:Facebook-jan26.jpg

Even though clicks cannot speak as sound as action, clicks can generate action. I personally agree that online activism is different with the traditional activism, however I think social media is still a good tools for political parties or political concern groups to make use of. It is a simply logic that more people know about the issue, a higher probability for more citizen to talk about or concern about the top, hence, a higher probability to recruit people to join the offline activism. And social media do great in letting the mass notice issues.

  Gladwell, Malcolm. 2010. “Small Change” The New Yorker, October 4 2010. Accessed May 4, 2014

http://www.newyorker.com/reporting/2010/10/04/101004fa_fact_gladwell?currentPage=all

Shirky, Clay. 2010. “The Political Power of Social Media” Foreign Affairs, December 20 2010. Accessed May 4, 2014

http://www.foreignaffairs.com/articles/67038/clay-shirky/the-political-power-of-social-media

  The Washington Time. 2009. "EDITORIAL: Iran's Twitter revolution" The Washington Time. June 16. Accessed May 4, 2014 http://www.washingtontimes.com/news/2009/jun/16/irans-twitter-revolution/

In this weeks’ reading, Burgess and Green investigate the role of YouTube in mainstream media. I am particularly interested by the discussion regarding the meanings of amateur video and the DIY celebrity culture they produce. According to Burgess and Green, “YouTube has been mythologised as literally a way to ‘broadcast yourself’ into fame and fortune” (2009, 22). While there are few cases where amateur content from YouTube has actually lead to ‘fame and fortune’, it does occur. 

Part 1 of A Very Potter Musical

A Very Potter Musical is a parody based on the Harry Potter series, it was written, produced and directed by University students. It was first performed at the University of Michigan, and in 2009 a video recording of the musical was uploaded to YouTube. It quickly went viral, generating millions of views from global audiences and fans of the series. Since then, the musical has lead to the creation of StarKid – a successful theatre production company that has  (StarKid, 2014). Founding member of StarKid and lead actor (playing Harry Potter), Darren Criss, has subsequently become a well-known and respected actor and musician. You probably know him best from the hit show Glee, or perhaps the Broadway musical How to Succeed in Business Without Really Trying (which, funnily enough, he replaced Daniel Radcliffe). In this case it is as Burgess and Green suggests, “raw talent combined with digital distribution can convert directly to legitimate success and media fame” (2009, 21).

A Very Potter Musical also demonstrates the opportunity and ability we have as an audience and as consumers to contribute through a blurring of distinctions, which is one of the propositions from the lecture. The cast/producers of this musical were amateurs, and YouTube provided an accessible platform for the commercialisation of their content. As a result the amateurs (including StarKid, and Darren Criss) have been turned into celebrities, essentially becoming apart of the ‘bona fide media world’ that the Burgess and Green discussed.  

Interestingly, musical was conceptualised whilst one of the creators was actually reading the fourth book in the Harry Potter series. This ties in with the propositions surrounding fandom and audience alliance. As discussed in Shefrin’s reading, “…fans often appropriate corporate-generated imagery, and then embellish or transform it with person artistic expressions such as…creative fanfiction…[or] digital films” (2004, 273). A Very Potter Musical was essentially fanfiction that came to life through a musical and subsequent YouTube video.

A peek at Pottermore

Shefrin also discussed the importance of participatory fandom and forming alliances with audiences. It is clear that actively involving fans (especially in a franchise) in the decision-making and production processes is extremely important, and will ultimately determine a person’s ‘symbolic capital’ (Shefrin 2004). As I discussed in my 5-minute review, J.K. Rowling has done this with the Harry Potter fandom, albeit not to the same extent. Whilst the 'HP' fandom wasn’t consulted as such throughout the film series’ production, she did form an alliance with her audience, “drawing inspiration from fans’ thoughtful perspectives and studied observations” (Shefrin 2004, 262-264), and most importantly respecting them as a community. This is evident through the Pottermore interactive online experience, which was launched for the fandom. Pottermore allows users to interact with the Wizarding World and create their own Harry Potter experience. “….It’s the same story but a few crucial additions; the most important one is you” (Valeri 2011).

    REFERENCES

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

Sauter, Theresa. 2014. “Week 5 – New Media and the Entertainment Industry.” Accessed April 2, 2014. 

Shefrin, Elana. 2004. “Lord of the Rings, Star Wars, and Participatory Fandom: Mapping New Congruencies Between the Internet and Media Entertainment Culture.” Critical Studies in Media Communication 21 (3): 261-281

StarKid. 2014. “About”. Accessed April 3, 2014. http://www.teamstarkid.com/about.html

We are now living in a participatory culture. Have you recently thought of yourself as a publisher?  Well you are if you have sent a Snapchat, updated your Facebook page, shared an Instagram photo or… written on your KCB206 blog? Modern technologies have reduced restrictions that we have historically had such as time, location and social standing. This now allows new styles of communities to arise (yes - even through your Instagram account) where the driving force is shared mutual interest. 

We are now able to access mountains and mountains of user-generated content.

So if everyone is now generating the content we are all reading, can we really be considered under the role of “Audiences” or “Consumers”? Would it not be better to think of us as the equalizing term “Users”. Rosen explains that we are now “All each other’s audience…The publisher-audience relationship remains, but today it is a loop, not a pipe” (Rosen, 2006)

 Image sourced from here

This change in power is affecting the companies once responsible for distribution of media. According to Green and Jenkins corporations feel threatened by their loss of control over cultural circulation, with companies using terms like “theft” or “piracy” to describe those accessing cultural artifacts at a different rate than the companies desire (such as people forming communities to share music that the Sonys and Warners of the world have rights to distribute) (Green & Jenkins, 2011, p.121). The values of these types of commodities (information, music and more) are hotly disputed between communities of “users” and the classically-modeled “producers” as the ability to make infinite copies of the data remove the inherent value in the artifact. People aren’t trading in physical objects, but instead information! Green and Jenkins(2011, p.119) also suggest that the term “appraise” can be a monetary idea (like Antiques Roadshow appraisals), but also art, and culture can be appraised in a way that values the cultural signifigance/value, not the monetary value. Memes being shared by communities fit this description, as they are valuable purely on the basis of their spreadability! The value of a meme lies instead in what they show about the culture wanting to share the memes.

The success of an online community is one that gives the users the ability to shape their space, and to utilise their individual talents, or as Lessig describes it: “the losers launched websites, the winners launched vibrant communities. The losers built walled gardens, The winners built public squares” (Lessig, 2008, p.221). While Rosen suggests that many successful “online communities attract their members by “offering them originality and quality that they can react, relate and incorporate in their creative work” (Rosen, 2006) the successful way in which image-heavy, language-sparse communities that are forming around memes and photo sharing apps suggest success in lowest-common denominator style communities, that survive on and reward expressing universality rather than depth and insight. This trait could be explored further.

Image taken from spousebuzz.com

References:

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

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

All four articles from week 1 and 2 consider the social implications of new media and changing technologies, ways to adequately frame the discussion around new media and the consequences of normalising different media. Baum frames her article by outlining the concerns of “Technological Determinism” (the perception that “machines change us”), “Social Construction of Technology” (the perception that machines are the product of influences on creators such as social, economic and geographical) and suggests that the reality is a middle ground between both; where culture influences technology and technology can act as a magnifying glass or accelerator for societal issues.

image sourced from someecards.com

individuals involved, their motives, and the concessions of trust given to further the discourse, Gibson and Papacharissi (2011) suggests that social media such as Facebook are not only designed for sharing chunks of individuals’ personal identities, but these sites need constant persona sharing to avoid becoming static and dying. These sites become a constant and ever-changing battleground for retaining individual privacy, while still engaging with others through the medium. Deuze (2011) frames this battle in terms of a “normalisation” of the media within the individual’s life, to the point where we now live in media rather than with media and the best defence is to become a producer of content, rather than simply the Truman-esque subject. If going “off the grid” is unfeasible, Deuze suggests that we become masters of identity creation, and embrace our multi-faceted personas that exist both on the Internet and in one’s physical space. 

References:

Baym, Nancy. 2010. “Ch 2: Making New Media Make Sense.” In Personal Connections in the Digital Age, 22-45. Cambridge MA: Polity Press 

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