Game Changer & Make Some Noise
Monterey Bay Aquarium
occasionally subtle
gracie abrams
tumblr dot com
roma★
I'd rather be in outer space 🛸
Discoholic 🪩
Cosmic Funnies
Phantogram Three
hello vonnie
Sweet Seals For You, Always
No title available
🩵 avery cochrane 🩵
art blog(derogatory)
2025 on Tumblr: Trends That Defined the Year
No title available
tannertan36
bliss lane
#extradirty
seen from United Kingdom
seen from United States
seen from Mexico
seen from Czechia
seen from Bangladesh
seen from Germany
seen from Argentina
seen from United Kingdom
seen from Malaysia
seen from United Kingdom
seen from United States
seen from United Kingdom
seen from Indonesia
seen from France
seen from Türkiye
seen from United States
seen from United Kingdom
seen from Türkiye
seen from Poland
seen from Germany
@kcb206group10-blog
The week 5 lecture focused on the entertainment industry, and how new media are changing and re-shaping its traditional structure. One point that I found particularly interesting was the concept of participatory fandom, which I will discuss in further detail in this post.
Only 10 years ago,...
This weeks lecture addressed how new media affect the workplace, and the meaning of it’s presence. The main point of interest that I will be discussing in this post is the emergence of crowdsourcing, in particular, ‘cloud labor’, the ethical implications of this, and what it means for the future...
Melissa Synnott FANDOMS Touching on the reading by (Shefrin, 2007), we unveiled a new meaning to the word “Fandom” and the colossal influence Web 2.0 has had on the concept. We’ve all gotten attached...
Solve Twitter, Solve the World (of media).
What was interesting about this weeks topic was the science (or psychology) behind our favourite social media platforms, namely Bruns descriptive breakdown of Twitters layers of communication (Bruns, A. 2013). We were introduced to Bruns three layers of communication on twitter, these being firstly the Micro level, which I summarised as being the person-to-person close and direct communication, the Meso Level where he described this as the follower-followee networks, and finally the macro level, where hashtags bring peoples common interests together and in a way can create communities of similar minded people.
(Image care of Bruns, A. 2013 reading, reference below)
Darryl Woodford in his lecture discussed the Twittersphere map (see below) created by QUT and based on user generated data retrieved from Twitter usage (Woodford,D. 2014). This map could not of been possible if not for the trail of data left behind from key aspects of the communication layers discussed in the previous paragraph. By analysing the data retrieved from Twitter ‘following’ patterns, #hashtag topics (topical and non-topical) and @replies (Bruns, A. 2013) computer programming allowed the developers of the Twittersphere (Woodford, D. 2014) to map topic trends and community discussions, this being information that will be very helpful for many different parties. This is where Telemetrics (Woodford, D. 2014) comes into play.
(Image from http://mappingonlinepublics.net/2012/04/01/many-maps-of-the-australian-twittersphere/)
For and example close to my field of observation I will focus on how media companies are using this data retrieved from social media users and tailoring their marketing plans around it. What many television shows are doing now is incorporating social media into their programming, using techniques that have both a direct effect on the outcome of the show and a more non-direct observational effect, both still giving the user a sense of connectivity with the program. As Stephen Harrington mentions “undoubtedly, the single most important 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 (Harrington, S. 2013). This therefore means that by incorporating Twitter handles and hashtags throughout a broadcast, they are no only increasing a consumers sense of relationship with a show, they are also increasing advertising due to use of the shows handles and hashtags across social media, but also and most importantly through these hashtags and handles are increasing there data being provided by consumers. As Eric Siegel states in his writings about 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 (Siegel, E. 2013). This meaning that the data is there but its what is done with the analysis of this data that is where the real value lies.
References
Bruns, A. et al, (2013). Chapter 2 : Structural Layers of Communication on Twitter pp. 15-28; Chapter 17 : The Perils and Pleasures of Tweeting with Fans pp. 221-236; Chapter 18 : Tweeting About the Telly : Live TV, Audiences, and Social Media pp. 237-248. In Weller, Katrin et al (eds),Twitter and society, (p.-). New York: Peter Lang.
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.
Siegel, Eric. 2013. “Introduction - The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ:John Wiley and Sons Inc.
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
CANCELLED
I recently discovered that three of the shows I’ve been watching are going to be cancelled by The CW, The Carrie Diaries, The Tomorrow People, and Star Crossed. In watching these shows I’ve become attached to the characters. I still want to know if Carrie and Sebastian can ever make it work on The Carrie Diaries. But alas, I’ll never know. However, in Siegel’s reading he investigated predictive technology and the ways in which it was possible due to big data. In the entertainment industry this predictive technology is used in order to, predict the success of a film or TV series, the audience it will resonate with, and how much revenue it will draw. In order for content to be made this predictive technology would have indicated a success. So, if success was predicted, why did my shows get cancelled?
Due to Web 2.0 significant changes have already occurred within the film and TV industry. Producers of content are relying more upon audience opinions. Big data gives these producers access to these opinions and thus allows them to make decisions based upon what the audience wants to watch. In some cases, as with House of Cards, the producers have used comments and responses from other shows to create original content which embodies the themes, ideas and concepts which have garnered a positive response (IndieWire 2014). The correlation of big data further allows producers to learn what specific scenes people are looking at. They can use this information in future projects to combine the strong ideas and create content which further speaks to their audience. The big data allows the production companies to see where and who their audience is. This again, enables them to better target their audience (Sinz 2013). Finally, multi-screen entertainment is becoming more popular. Whilst watching TV an individual will most likely have a second screen in front of them. Production companies are aware of this change and so encourage individuals to interact with the content across these different screens. Big Brother is an example of this as people are encouraged to tweet whilst they watch the show. Therefore they are interacting with both the televised show and the online twitter conversations (IndieWire 2014).
With all this information available to producers it makes it easier for them to see which types of shows and films will become successful. “That’s right, Hollywood is using big data to determine the best way to market, cast, and produce creative films and series that appeal to the masses.” (Idea to Screen 2014) When new content surfaces the predictive technology, which Siegel spoke about, has been used to determine whether audiences will respond to it. If it has been produced, then the research based upon big data has predicted it will be successful. Despite these predictions however, many shows are cancelled after a few episodes and many films still bomb at the box office. Siegel (10) explains that this is due to the fact that these predictions are just that, predictions. We have no real way to see what will actually happen in the future. Therefore, it happens quite often that shows or films will not deliver what was expected of them due to the inaccuracy of these predictions.
References:
Idea To Screen. 2014. “Big Data: Hollywood’s New Rising Star Is Influencing Creative Decisions.” Accessed May 11, 2014. http://www.ideatoscreen.com/big-data-hollywoods-new-rising-star/
IndieWire. 2014. “Infographic: Here’s How Big Data Is Revolutionising the Movie (and Music) Business.” Accessed May 11, 2014. http://www.indiewire.com/article/infographic-heres-how-big-data-is-revolutionizing-the-movie-and-music-business
Siegel, Eric. 2013. “Introduction - The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ:John Wiley and Sons Inc.
Sinz, Cameron. 2013. “Why Big Data is Defining the Film Industry: 5 Things We Learned From Tribeca Talk’s ‘Big Data and the Movies’ Panel.” Accessed May 11, 2014. http://www.indiewire.com/article/why-big-data-is-defining-the-film-industry-5-things-we-learned-from-tribecas-panel
Reality TV, It's Killing Me
After reading about Telemetrics this week, I have been left feeling... Rather sad. I couldn’t help but think about reality television the whole time, and the relationship that reality TV has with Twitter and other social media.
As a ‘wannabe’ film/tv show maker, there is one ultimately depressing thing to see. Currently, Australian law states that 55% of content on commercial channels between the hours of 6am and midnight must be produced in Australia. What is the cheapest option for the big cat media moguls of 9, 7 and 10? The aforementioned somewhat cancerous programming that calls itself reality TV.
OK, that may seem harsh, some of it may be a bit of a laugh and (I can’t lie), I may have been sucked in once or twice, but this is personal, OK!?
Seriously though, why is reality TV so popular?
Lets look at this first: I liked the example of #qanda that Harrison (2013) raised. Mostly because Q and A can be both highly entertaining and highly informative, and it’s great that a show of that format and national magnitude should be entertaining the idea of a really democratic question and answer session. If you’ve seen the program before, you’ll have seen that there can be some crackers that keep the discussion moving. In this case, I love the effect of Twitter on television. It isn’t primarily a marketing tool for the producers, and, even if it is somewhere down the line, that can really go by the by because it’s being used as a tool for the greater good of the people.
The problem I start to have is that, as Harrison also talks about, people start to provide input with the lure of their tweet/comment being posted on TV. I mean, OMG! It could make you famous!
This leads to the blatant love triangle between Twitter, Reality TV and rich media moguls. Harrison raises Wood and Baughman’s proposal that social media just provides these producers with the opportunity to further market programs through unpaid labour. The impression of Twitter upon reality TV give it renewed value. Now you don’t only see the intense drama of Sally and Sandra struggling in the kitchen or Bob bitching about Amy in the garden, you can talk about it! This also makes it feel like real time, even though the majority of the program has been filmed weeks ago.
At the end of the day, this results in more communicating and involvement for people in reality TV, and less of that involvement in traditional drama/comedy. Therefore less people watching and feeling involved with drama.
Which sucks.
Oh well. Maybe I’ll be able to edit some awesome kitchen action one day instead.
Richard Lawton
n8602531
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.
Woodford, Darryl. 2013. “Australian Reality TV on Twitter: A Two Horse Race.” Mapping Online Publics Blog, August 13. Accessed February 4, 2014. http://mappingonlinepublics.net/2013/08/13/australian-reality-tv-on-twitter-a-two-horse-race/
Big Data and Social Media
When I first started running fortnightly events back in 2007, social networking site MySpace was very popular among my target demographic of music lovers (Boyd, 2007). MySpace worked well for free promotion, allowing me to send out messages to all of my company’s followers. It also allowed me to research the musical interests of my target audience, which helped me to pick out bands for my events. I could even get my followers to help me promote shows by getting them to share my events on their walls.
Over time, I build up a valuable network of followers and soon other promoters were asking me to help promote their shows via my MySpace network. I had effectively turned my MySpace network into a marketing tool, and I was not the only one. Soon MySpace became a site dominated by bands and half naked women trying to sell subscriptions to porn sites. Everyone was trying to sell you something. By 2009 people were leaving MySpace in droves in favour of FaceBook (Goodings, 2012), drastically reducing the value of MySpace. To put it into perspective in 2005, Murdoch’s News Corporation brought MySpace for $580 million in 2011 they sold it for a measly $35 million (Goodings, 2012).
Suddenly the huge network I had build up over MySpace was worthless. I could not reach my target audience through MySpace anymore and my event attendance was suffering as a result.
So I began to build up a network on FaceBook. However, this time it was different. FaceBook placed restrictions on businesses that made it difficult to promote shows for free. To get around the restrictions, I created both a business account and a personal account in the name of my business, I also created a facebook group. At first this worked well, and I was able to build up a fairly large audience, however, FaceBook soon tightened its restrictions on how many followers/friends a personal account could have, and limited it to 5000 people, and eventually they forced me to turn my company account into a page.
Next FaceBook brought out new paid promotion options for businesses, forcing business to pay for something they used to get for free (Constine, 2014). FaceBook promoted this service to businesses as good long-term investment for companies to build their brand (Constantine, 2014), but their strategy was flawed, or perhaps they were purposely misleading people. Maybe FaceBook is guilty of the biggest ‘bait and switch’ scam ever.
How you ask? Do you ever wonder why you can’t see all of your friend’s posts in your news feed? This is because the more pages a user likes, the less space there is to display their posts in your newsfeed. So FaceBook filters your news feed to only show you things they think you are interested in (Goodings, L 2012) They justify their choices by saying they are trying to keep FaceBook relevant, and thus avoiding the mistakes of MySpace.
But this, unfortunately, devalues the followers that companies already have because it limits the reach of their FaceBook posts. “The roughly 50% decline in reach over the past year matches the 50% increase in Page Likes per typical Facebook user over the same time period. As people ‘Like’ more Pages, the organic reach of each drops" (Constine 2014). Now in order to for your post to be displayed in all of your followers newsfeed, you must pay FaceBook a fee calculated on the amount of people you wish to target.
So all of those businesses and bands that worked hard and in some cases paid to build their audiences, now find that they are actually reaching less people the more followers they gain (Constine, 2014).
References
boyd d. (2007) Why youth (heart) social network sites: The role of networked publics in teenage social life. In: Buckingham D. (ed.) MacArthur Foundation Series on Digital Learning – Youth, Identity, and Digital Media Volume. Cambridge, MA:MIT Press, 119–142.
Constine, J (2014) Why is Facebook Page Reach Decreasing? More Competition and Limited Attention. Accessed Via: http://techcrunch.com/2014/04/03/the-filtered-feed-problem/
Goodings, L (2012) Understanding social network sites: lessons from MySpace, University of Roehampton, London. Accessed via: http://vcj.sagepub.com.ezp01.library.qut.edu.au/content/11/4/485.full
images
Big Data and Social Media
MySpace V’s Facebook
How does FaceBook choose what to show in news feeds?
And there goes another pay check, thanks Big Data...
Hello, my name’s Mikaela and I have an addiction to online shopping. Yes it's serious, I spend hours browsing through pages and pages of possible purchase items. Thanks to sites such as eBay my problem isn’t getting better. In fact as soon as I log into my eBay account I become confronted with endless suggestions of things I’d like to purchase. To my disadvantage these suggestions are extremely close to my real preferences and likes, leading to more poor choices and too much spending.
This week we were introduced to the idea of new media and big data. Darryl Woodford provided a video explaining exactly what big data is and where its leading to. In this video a lady by the name of Andie Grace explains that big data is “Global access to all the possible information about human responses and human innovation.” Explaining that every time we click like, visit a website or share a page the Internet is collecting endless amounts of data and information about us as individuals (Darryl, 2014). Andrew Chung explains big data as ”whenever we click on a website, make purchase… every time a light switch goes on and off it is generating a piece of data”(The Economist, 2012).
Even though eBay is making my life harder by hindering my powers of restraint, the idea of prediction which big data provides is ultimately to better our online experiences. Big data allows us to be connected and confronted with more networking, careers and shopping websites leading to an unlimited amount of opportunities online. The overall aim is to provide us with a better and efficient experience online, with the unlimited information and sources we are given. Eric Siegel explains, “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.”(Siegal, 2013).
So if big data is going to make our lives easier, what’s the problem? Where once we felt safe participating in the online world, now individuals are advised to step back and think every time they share something about themselves or anything online. Once any piece of information is submitted to the Internet, we have zero control of who sees it, where it goes and what uses it. This is where the issue of privacy is once again brought up in the ever-questionable outcomes of new media. Personally I feel big data is something that should be embraced and accepted, we are told daily about the dangers of the internet and the privacy breaches that may take place. However I feel it is a small price to pay for the benefits and knowledge we are given accordingly.
By Mikaela Charlton
References
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.
The Economist. 2012. “What is Big data”. YouTube video, posted June 26. Accessed May 3, 2014. http://youtu.be/ahZGEusG13A
Woodford, Darryl. 2014. “New Media, Big Data & Telemetric: Week 9 lectures notes.” Accessed on 4 May 2014
Audiences will bring Stefan back!!!!!
In the latest episode of vampire dairy, Stefan Salvatore is dead, almost every tweets about “vampire dairy” was yelling out- give me my Stefan back!!! So was I!!!! However, I pretty much sure that Stefan will definitely come back as the power of social media!
Social media, in particular, Twitter-are therefore not so much a rival for television, but often a complementary technology which has opened up a new space for audience-audience, and audience-text interactivity. (Harrington, Stephen 2013). TV series producer therefore keep up with trend and creates their virtual lounge room which bridge the strong connection and communication between particular TV series producer and its audience. How does these TV producer take advantage of this new space? What this new space offer?
It is no doubted that the TV manager and TV producer pay more attention on its rating which closely related to its commercial purpose, rating depends on its audience. How can a TV series get high rating? Basically, it needs compelling storyline, characters and promotion. Now, I am going to take vampire dairy as an example.
2, May vampire dairy’s Facebook official page update a picture and a doubtful question to promote that night’s episodes. Fans participated in discussion by writing comments, fans who support STELENA expected that what will happen between them.
In order to get the answer to those questions, fans will watch it on time. In fact, nothing excited happened to Stefan and Elena, but TV show producer really got what they actually want- a steady but high rating. The development of the plot and characters can greatly affect the rating. Fans communicate with producer on twitter or Facebook by commenting, @mention, reply and retweet about their expectation in next season/ episodes, their reaction after watching latest episode…..etc. for example, Stefan was dead in last night’s episode, his fans have expressed outrage. 3 hours of trending, 33k tweets: “No TVD without Stefan Salvatore”, those data collected from twitter predicts the trend that the viewship will go down, TV producer will make the right decision if they recover viewship.
Twitter, Facebook and the other social media platforms provide TV producer with exactly such a means of inconspicuously observing the activities of television audiences. They now have the capacity to efficiently collect enormous amounts of these data, at relatively minimal cost and effort, about how people react to TV in real time, rather than many weeks or months after a particular program goes to air, as is usually the case. The data can be analyzed both quantitatively and qualitatively, and provide rich detail about who is saying what, where, when, and to whom. (Harrington, Stephen 2013)
The arrival of web 2.0 era makes the information shared, makes it valued. To compare with traditional form such as paper questionnaires, by using new media way, communication becomes mutual, can be far more effective in understanding people's true feelings and attitudes.
sunjiawen n8985138
Reference list
Harrington, Stephen. 2013. “Tweeting about the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt and Cornelius Puschmann, 237-248. New York, NY: Peter Lang.
RIGHT HERE RIGHT NOW !!!!
“Prediction is power” (Siegel 2013), according to Eric Siegel’s Prediction Effect, which explains the valuable uses of big data in our world today. In a world where everyone uses digital technology, prediction has become a primary source for most companies to guarantee success. It seems as if our world today depends almost entirely on predictive technology though, for company and commercial success, electoral success, entertainment success, and so on. Increasingly, big data is continuously being used to predict results whether good or bad regardless of the effects it may cause. Although there is not a right or wrong opinion for the use big data to predict success, I feel that big data usage helps create a better user experience for our digital age.
Ideally, big data can be used to enhance digital networking and give users a better online experience overall. Big data today offers us opportunities to have a faster, more efficient, and a more personal and appropriate online experience. “One of the great ironies of big data is that it can be used to make commerce experiences feel smaller and more intimate to customers” (Saracino 2013). With the use of big data, everything that we experience on the web and through social media can have a positive affect on us. Big data these can be used to help us connect with job opportunities, shopping and selling items, and through social media, create connections with people we may have never known we had something in common with or had mutual friends with. Big data generally allows us to experience a smarter and more efficient method for online networking. While this is the case that big data should follow, internet users may feel that big data still has the potential to violate user privacy.
In some instances big data is believed to be used to violate user privacy whether users agree to sharing their personal information or not. Big data allows companies to track your online searches, interests, and information. For me, big data is doing what it was designed to do, which is to learn about the user and accommodate to their needs. The issue of big data usage rises when the argument of whether user information online should be kept completely private or not. While most companies like Facebook do agree to keep user information private, there is still the potential of user information leaking out to unwanted companies…“The Facebook bug that leaked the private contact information of 6 million users, confirmed by the company on Friday, appears to be worse than originally thought, according to a group of security researchers” (Guarini 2013). However, as long as online businesses honor and value customer information, big data has the potential to create an efficient user experience. Overall, because society relies on social media and already, we submit our information to the companies on the internet, then we are only hurting ourselves if we do not embrace and use big data. A better user experience is available to us with the help of big data, and it is up to us to recognize the potential of big data and embrace it.
Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.
Saracino, Adria. 2013. “Interesting Ways Businesses Use Big Data to Improve Personalization.” Click Z | Marketing News and Expert Advice, 23 April 2013 . http://www.clickz.com/clickz/column/2263262/interesting-ways-businesses-use-big-data-to-improve-personalization
Guarini, Drew. 2013. “Experts Say Facebook Leak Of 6 Million Users’ Data Might Be Bigger Than We Thought.” The Huffington Post. 27 June 2013 . http://www.huffingtonpost.com/2013/06/27/facebook-leak-data_n_3510100.html
Target Practice - (Blog #7)
You are searching the internet for a new archery bow. You find the perfect one, but it’s too expensive. With reluctance you close the tab and return to Facebook. Your newsfeed refreshes. “No, it can’t be!” you think, astounded. The very bow you were just looking at has appeared in the advert column to the right of your page. “Is this a sign?” you ponder. You feel something strange on your head. You look in the mirror. It’s a candy red apple and you realise, “This is not a sign, this is data fuelled marketing and I’ve just become the target.”
Every time you Google an image, shop online, log into your email or click an advertisement you create something; that something is data. On its own the 2.5 quintillion bytes worth of data created daily seems like an overwhelming ocean of useless information that you are sure to drown in. However, thanks to Dan Steinberg, an entrepreneurial scientist who provided the bow that is machine learning, this informational data is now analysed and used to predict the wants of individuals. Machines learning from data unleashes the power of these two singularly harmless resources, combining them to create a high velocity marketing weapon. A weapon that according to Siegel (2013) “... uncovers what drives people and the actions they take” to predict which customer will buy/buy into their product or service and henceforth provide them with some form of corporate gain. To most, data seems like a drab result of our time spent on the internet, however, each click is identifying information about ourselves, creating a virtual version to be ridiculed and exploited by the machine learning process.
Perhaps this colourless interpretation of the process is due to the nature in which our ‘digitally native’ society has been influenced to accept the way it has no control over the arrows they produce and who becomes targeted.
Along with the evolution of this machine learning process that enables targeted marketing, is the evolution of the number of platforms this data is allowing us be targeted on. Society is now encouraged by one platform to utilise another in order to achieve something. For example, whilst watching television you may be encouraged to tweet your opinion on Twitter for the opportunity to see your Tweet on screen as is the case with Big Brother Australia. The amount of Tweets selected to be on-screen are few, though Big Brother is essentially harnessing the power of the crowd to advertise for them at virtually no cost other than the data they have already bought that tells them exactly how to inspire you to do so. This vicious circle only continues as the data you create by participating tells them information regarding your age, gender and location and how successful they are at swindling you.
Data is created by you and everyday internet users who have little (if any) access to and no control over the way the fruits of their labour is used. Maybe this doesn’t seem like such a bad occurrence, perhaps this is society’s way of paying the pied piper for the use of his services, right? That may have been the case if equal access were provided to the population of the product the crowd has created. However, the commercial hierarchy behind 72% of websites have lucrative privacy policies that allow third parties (the highest bidders) to track internet activity which, in an efficiency driven society, few would spend the time to read (The Economist, 2012). This means efforts of the crowd is being exploited and furthermore, forced to relinquish both privacy and control over what they see when browsing. Corporate entities aren’t just targeting you with un-nocked bows, they are using your own arrows to do it; encouraging a new media consumerism of content.
Ultimately, the rich get richer and you get duped into standing under the apple.
Shayla Girdler ( http://shaylagirdkcb206.tumblr.com/ )
References
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.
The Economist. 2012. “The Dark Side of Big Data”. YouTube video, posted June 26. Accessed May 7, 2014. http://www.youtube.com/watch?v=raJOkguPrH4
Data is the new oil
Blog Post #7- Melissa Synnott Data is the new oil - European Consumer Commissioner Meglena Kuneva
The Prediction Effect, a fascinating reading by Eric Siegel had me hooked from page one…. “Data is the modern age genuine gold rush” as Siegel puts it. You and I already know that where there is data, there is space for learning. But if you multiply that information by billions you get Big Data, the mother load of information and potential wealth. 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, Eric 2013).
From our posts on Facebook, the pages we ‘like’, the websites we access and the surveys we take, that information is being processed by computers via digital methods such as Data Gathering, Processing, Analytics and Visualisation (Sauter, Theresa 2014). But don’t be fooled! That harmless link you shared, that meaningless update of your day on social media… all of that information may seem worthless but while we continue to stay oblivious, the reality is that it is becoming the crux of every business in today’s new age of technology. Our data becomes a priceless collection of experience from which to learn.
To exemplify on the effects that social media have on television programs in relation to Big Data and Telemetrics, I was watching the new season of The Voice that started this week, and I was thinking to myself “this year I'm going to vote.” I have never voted for any TV show before and to be honest I didn't give it much thought whilst watching shows like Big Brother or MKR (My Kitchen Rules), but I decided that I would give it a go. Giving the audience control of the outcomes of people’s fate on television and the power to determine the success of a program is the rapid advancement of a new media audience. This new media audience take to platforms like twitter and Facebook to voice their opinions and to acquire some sort of validation from their like-minded peers and It’s platforms like these that attract the most attention for Big Data to be analysed by businesses to gauge public reactions and their likes and dislikes about certain programs.
http://metaversemodsquad.com/twitter-and-big-data-whats-in-a-tweet/
I believe that in the creative industries, Big Data and Telemetrics has never been more crucial than they are now, and analysing the information we provide could mean to rise or collapse of an industry, business or franchise.
References:
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.
Sauter, Theresa. 2014. “Week 8: New Media Politics and Engagement.” Accessed 4th May, 2014. http://blackboard.qut.edu.au/webapps/portal
Trending!
The topic today will resolve around big data, example will be showed and the challenges that face by big data researcher and practitioner
A petabytes is a lot of data, 1 petabytes is equal to 13.3 years of hd tv content, imagines the amount of video you can download.
"Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization (Douglas, 2012)
http://etomicmail.com/blog/wp-content/uploads/2013/03/big-data-infographic.jpg
To shrink it down it just basically is a huge amount of traffic data collected through the web and used to the full benefits of itself. The amount of data changes constantly, but will not go down only will increase in quantity.
The trend to larger data sets is due to the additional information that constantly being feed from analysis of a single large set of related data, allowing the result to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions. Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set (Jacob, 2012).
Comparing big data with web 2.0, it simply gather information data from a larger and demanding sources which are more inclusive to our personal lives.
Wal-Mart Stores Inc. and search. The mega-retailer's latest search engine for Walmart.com includes semantic data. Polaris, a platform that was designed in-house, relies on text analysis, machine learning and even synonym mining to produce relevant search results. Wal-Mart says adding semantic search has improved online shoppers completing a purchase by 10% to 15%. "In Wal-Mart terms, that is billions of dollars” (Laskowski, 2013).
Facebook, which I believed most of you are intrigued to use right now has over 901 millions active users generating social interaction data. And even 5 billion of people are using their mobile phones as of now.
But nothing comes without challenges. The challenges of capture, curation, storage,search, sharing, transfer, analysis and visualization (Vance, 2012)
Even in practical sense, the data are more empirical to certain information company that are restricted access from the public. The ethic issue and the lack of talent on analysing data (Woodford, 2014)
Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers
Although challenges are faced, we as human always come up with simpler way to finish our business.
With twitter hash tag implementation, researchers are more convenient to investigate a certain topic which are more accessible and easier to analyse.
So in the end, the question of privacy are introduced. What would you share in consideration of these factors?
Reference List
Laney, Douglas. "The Importance of 'Big Data': A Definition". Gartner. Accessed on 4 May 2014.
Jacobs, A. 2012. "The Pathologies of Big Data".ACMQueue. Accessed on 4 May 2014. http://queue.acm.org/detail.cfm?id=1563874
Vance, Ashley. 2010. "Start-Up Goes After Big Data With Hadoop Helper". New York Times Blog. Accessed on 4 May 2014. http://bits.blogs.nytimes.com/2010/04/22/start-up-goes-after-big-data-with-hadoop-helper/?_php=true&_type=blogs&dbk&_r=0
Laskowski, Nicole. 2013. “Ten big data case studies in a nutshell.” Accessed on 4 May 2014. http://searchcio.techtarget.com/opinion/Ten-big-data-case-studies-in-a-nutshell
Woodford, Darryl. 2014. “New Media, Big Data & Telemetric: Week 9 lectures notes.” Accessed on 4 May 2014
The New Media Psychics
As mentioned in a previous post, once something is online it is always online. Every like, comment, post, purchase, article read all contribute to an online database which is monitored to predict your future moves online.
Big data is the data sets created from every computerised action made, due to the large amount of information, it is too complex to be integrated or manipulated by standard methods or tools. However, the large amounts of data is compacted and summarised into smaller databases which can be generated to find realistic statistics. Big Data has the ability to predict more things than one realises. From predicting the weather, to the recommended clip on Youtube, to your next purchase from a particular online retail outlet, to the people you may possibly know on Facebook. Big Data is every bit of data ever generated, mainly for the purpose of statistics. Big Data is used in many industries to help predict the likelihood of success.
Within the film and television industry, Big Data is used to predict the likelihood of success for a film. The film industry uses data sets to analyses how media is being consumed.
“Now with the new technology we have driving the content creation… I think that the change of technology is going to break down the artificiality of the storytelling to make it more pure. I think there is an opportunity here. What I'm hopeful about, is that it's not just the data. It's the technology that comes along with the data. It's the technology that the data brings with it and that feedback process gets faster and faster."-
Whether you agree with this method of research or not, Big Data has revolutionised the way films are produce. Big Data has had a massive effect on the film industry, however it is not the only industry which uses Big Data to increase the revenue.
Everyone has a strong opinion towards psychics, whether it is positive or negative, and their method of predicting the important things in our life including health and death. However, would you believe it, the most confronting thing in life can be accurately predicted. Studies show Big Data has the ability to predict our health, our risk of obtaining certain diseases, our risk of being in a car accident, and the likelihood of death. The health industry uses Big Data to produce statistics and apply health insurance levels to their patients/customers.
Big Data has revolutionised the way all business act towards helping their consumers, customers, and patients.
Calcee Preston
References
Shiz, Cameron. 2013. “Why Big Data Is Defining The Film Industry” IndieWire. Accessed 10/05/2013 http://www.indiewire.com/article/why-big-data-is-defining-the-film-industry-5-things-we-learned-from-tribecas-panel
Siegel, Eric. 2013. “Introduction – The Prediction Effect.” Predictive Analytics. 1-16. Hoboken NJ: John Wiley and Sons Inc.
Woodford, Darryl, Katie Prowd and Axel Bruns. n.d. “Telemetrics: Towards Measuring Social Media Engagement with Television.” Accessed May 03, 20014. http://blackboard.qut.edu.au.
This week’s lecture discussed various aspects of new media, big data and telemetrics. One item of particular interest explored was the new phenomenon of television viewers commenting and discussing on the events of television programs in real time – and for all to see. This topic allows for many...
People vs Machines vs Cats : The Fallibility of Big Data (Blog 7)
The lecture on big data this week was interesting for many reasons, and the further readings by Bruns (2013) and Woodford (2013) gave examples of how big data can be analysed to reveal trends in both the political sphere and reality television audiences respectively. Siegel (2013) imparts the process whereby big data, which he equates with crud, is mined through “machine learning” in order to be turned into useful predictions, which he equates with gold. After admitting that such predictions are tricky at best, citing an accuracy rate of only 50 percent as optimistic, Siegel (2013) assures us that, “Predictions need not be accurate to score big value.” While this idea seems sound enough to me given the right scenario, what about the other side of the coin? At what point can fuzzy predictions actually be harmful?
During Darryl's lecture, he played the Big Data + Old History YouTube video (2013), which outlines the process of “distant reading”, wherein a computer algorithm is written to automatise the sorting and collection of relevant data (video embedded below). Yet the one thing I could not stop thinking about was the potential fallibility of distant reading, how a flawed algorithm could allow for derived courses of action based off inaccurate data leading to situations which could be potentially life threatening. While mining big data to make predictions for something as innocuous as reality television viewing trends may only be useless at worst and helpful at best, if the process was to be used for something more important, like how many hospital beds will the hospital need next year, relying upon fuzzy predictions with a 50 percent accuracy rating could lead to serious harm to human lives.
That is the thing with computers and machine learning, there are some things that computers are just not as good at as humans. Chatfield (2014) uses the example of the difficulties which computer algorithms have faced in trying to achieve successful image recognition of cats. In 2012, Google put 10 million cat images into a massively powerful custom-built system to see if the alchemy of big data and machine learning could allow an algorithm to learn from a sufficient number of examples to approximate accurate solutions to the question “what is that?” The results were not encouraging with the resultant accuracy rating struggling to reach just 15.8 percent (Chatfield 2014). It would seem, identifying cats is not a computer algorithms strong suit.
Image from: http://www.jokeroo.com/pictures/animal/11302.html
While cat recognition may be a far cry from the ability for an algorithm to predict the necessary number of hospital beds required for the future, the implicit dilemma remains. There are bounds to both what we as humans, and our derivative technological counterparts, algorithms, are able to accurately quantify. As long as we recognise the banality of phrases like "big data", and do not view the results generated from such systems as impartial representations of the world, or substitutes for actuality, then we shall only benefit from such data mining. Begin viewing big data predictions as infallible or reliable, however, and we shall be welcoming the potential for catastrophe.
Please read more about the fallibility of big data predictions at the following links:
http://www.theguardian.com/commentisfree/2014/jan/06/artificial-intelligence-understanding-big-data?CMP=twt_gu
http://www.inc.com/erik-sherman/4-ways-big-data-can-trick-you.html
http://whatsthebigdata.com/2012/03/30/big-data-six-links/
http://www.forbes.com/sites/gilpress/2014/01/31/big-data-debates-machines-vs-humans/
Sam Honey
08803307
References
Bruns, Axel. 2013. “Follower Accession : How Australian Politicians Gained their Twitter Followers.” Mapping Online Publics Blog, July 8. Accessed May 10, 2014. Available at: http://mappingonlinepublics.net/2013/07/08/follower-accession-how-australian-politicians-gained-their-twitter-followers/
Chatfield, Tom. 2014. “Forget artificial intelligence. It's artificial idiocy we need to worry about.” The Guardian, January 7. Accessed May 10, 2014. Available at: http://www.theguardian.com/commentisfree/2014/jan/06/artificial-intelligence-understanding-big-data?CMP=twt_gu
Siegel, Eric. 2013. “Introduction – The Prediction Effect.” Predictive Analytics. 1-16. Hoboken NJ: John Wiley and Sons Inc.
Woodford, Darryl. 2013. “Australian Reality TV on Twitter : A Two Horse Race.” Mapping Online Publics Blog, August 13. Accessed May 10, 2014. Available at: http://mappingonlinepublics.net/2013/08/13/australian-reality-tv-on-twitter-a-two-horse-race/
PHD Comics. 2013. “Big Data + Old History.” YouTube video, posted September 6. Accessed May 10, 2014.Available at: http://www.youtube.com/watch?v=tp4y-_VoXdA
Big Brother's Big Makeover
By Erin Smolenski
One of the themes of this week’s lecture content was the impact of social media on television programs. In particular reality television programs such as My Kitchen Rules, Big Brother, and The Voice each have their own sizable Twitter and Facebook following. With this being apparent, what does the virtual aspect of television programs offer?
At first, the arrival of Web2.0 brought with it uncertainties for the future of viewing television the traditional way. Of course an initial evaluation made television seem out-dated and that consumers want to choose what they watch and when they watch it. What was neglected was the fact that viewer’s experience is enriched when shared with others. This can be seen in many cases throughout history when thousands of people flock the streets to watch a sports game on a live screen when they could have watched it more conveniently and conventionally in the comfort of their own home (Chambers, 2010)
Television producers picked up on this trend and created a “virtual livingroom” (Harrington, 2013) to gain a higher quantity of viewers as well as strengthening the bond between television program and consumers. I’m going to take the television program Big Brother and use it as an example. Big Brother was axed after 8 seasons due to decrease of daily ratings, decrease of interest and its lack of surprise (Idato, 2008). Four years later- despite the known lack of interest from the public – the Nine Network brought Big Brother back to air with the reworking of many features of the show. The biggest change made was the audience involvement. Firstly Big Brother flooded social media in the hype before the Launch of the 9th season (Channel 9’s first season). A facebook page and Twitter were created and consistently updated, keeping the viewers informed of what was going on. Big Brother became a very sociable television program with the displaying of fans tweets throughout the program as well as enabling viewers to “vote” for their favourite housemate online and allowing viewers to dictate certain twists.
This season and the one following had a new found group of dedicated social media followers. With over 740 000 followers of their Facebook page and 170 000 followers of their Twitter, Big Brother has a free portal to their fans year-long round. When you think about it, how many times did you see an advertisement on television about the auditions for the newest season of Big Brother? There was no need for paid ads when they had free access to all the dedicated viewers in one place.
The introduction of Web2.0 did call for some television programs to update their methods of reaching and maintaining audiences, however, this wasn’t a bad thing at all. It brought with it a new viewing experience for consumers with even more ways to interact with both the program and other fans. With this modern day concept I am excited for the future and for whatever may be next in the advancement in television viewing.
Chambers, Geoff. 2010. “No joy for frozen Socceroos fans at FIFA Fan Fest” The Daily Telegraph, June 14. Accessed May 9, 2014. http://www.dailytelegraph.com.au/news/socceroos-fans-cop-blast-from-germans/story-e6freuy9-1225879296184
Harrington, Stephen. 2013. “Tweeting about the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt and Cornelius Puschmann, 237-248. New York, NY: Peter Lang.
Idato, Michael. 2008. "Channel Ten evicts Big Brother". The Sydney Morning Herald, July 14. Accessed May 9, 2014. http://web.archive.org/web/20080716165346/http://www.smh.com.au/news/tv--radio/ibig-brotheris-eight-year-run-comes-to-and-end/2008/07/13/1215887442035.html
