In this day in age , our lives is mediated through the use of technology, and a big part of how this functions is involved through data. The online fashion stores we order clothes and shoes, the songs we listen to on Spotify or Apple music and the posts and tweets we like on social media all accumulate to this big quantity of data, leaving our digital footprint on this technological world or in other words a digital shadow (Me And My Shadow, 2015). This so called digital shadow is ever growing, as we continue to surf the digital world and explore new avenues and meet new people, altering this digital shadow. In this essay i will argue the way in which data is mediated through our lives, defining who we are as individuals on a digital platform. First i will explain the various different types of data there are and how and what type of data is being recorded as individuals. Next i will talk about the types of data that plays a role in our lives through examples and can they define me. Finally through the examples discussed, dissect the ways in which this can be problematic in today's society as it takes something subjective and makes it objective and how the concept of power is ever so more effective.
To start, in order to understand, in its simplest form provided by Castell, the idea of power, how data mediates our lives, we have to understand what data is and the various different types there are in the digital world. First of all the idea of power. Castells defines power as “The relational capacity for a social actor to influence the decisions of another social actor, that favour the empowered actors wills, interest and value”. A concept that is fundamental towards human action and one that is emphasised through the use of data in tracking human behaviour in order to influence users in the network society (C. Fuchs, 2017)
The types of data that will be discussed includes Big Data, Metadata and Raw Data. Big data is a term that describes the vast volumes of data that we make as a global collective. It's not this that is significant but the way that its used for businesses through big data analysis. Big data analysis operates through pattern recognition as predictive technology and therefore allows those with authority, to have power, as they are able to observe and control what we see which can both be beneficial or detrimental. (Wikipedia, 2018) This is to help governments perform political economic through our data. Another way, in which our digital shadow is created and developed, is through metadata. Metadata is the information collected around our communication, examples include the time or date we call someone or the location we access our email. (Wikipedia, 2018) It doesn’t contain personal information but the transactional data that occurs. Finally the third type of data is Raw Data. Raw data is primarily unstructured or unformatted repository data. It can be in the form of files, visual images, database records or any other digital data. Raw data is extracted, analyzed, processed and used by humans or purpose-built software applications to draw conclusions, make projections or extract meaningful information (Wikipedia, 2018)
First of all data, can only define us on what we allow it to. When looking at the concept of digital shadows, it is developed and morphed through our exploration in the digital world and therefore any information outside of this wouldn’t be acknowledged. Lets look at streaming services such as spotify in which filters songs towards your personal preference. For example, me as an individual, listens to a wide range of music and genres. It can go from Rap music, to Indie, to an old classic like September by Fire and Rain but it's fairly rare that i'll go to a genre such as Bollywood music as i'm not a fan of this type of music. Its this type of human behaviour that creates trackable data which can be used for this service to create playlists or show artists i may like depending on the similar genres and simultaneously prevent any content that's outside of this digital bubble. This is known as collaborative filtering. (P. Grover 2017) It's the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets for it to function. However the idea of collaborative learning is that a large group of people using the same streaming may have the same preferences as me, and therefore filters recommendations to my taste.
With the use of metadata the playlists and recommendations set out to me can also be set out in what content might prefer the most. Spotify have a feature which is known as Daily mix. It brings selections of tracks to create a bottomless playlist to keep you listening and as the name implies, changes every day and range in between the number between one and six, depending on how productive you are on spotify. (I. Lunden, 2016) This is done through a combination of Metadata and Collaboration Filtering. As shown in the figure below where “Daily Mix 1” consists of Rap Artists which is in fact the my most favourite type of music where “Daily Mix 2” consists of Indie artists which is again my second favorite type of music i listen to. Metadata has the ability to turn something subjective such as my preferred genre type and turn it into an objective piece of data as it tracks how often i listen to something.
Figure One: The daily mix created while on spotify
This is also similar for the online streaming service netflix, in which on initial sign up asks for your top three preferences of the type of T.V Shows or movies you like to watch. From those three simple choices that you picked, the netflix service sorts each of its hundreds if not thousands of shows to be as closely related to the shows you picked, in order to keep you interested in the service. Furthermore as you continue to watch the content on Netflix, a new “Image Algorithm” will place cover art for its content that's catered to individuals in order to entice them to watch. (A. Schneck 2018) Its this type of digital process that can allow individuals to be defined on who they are as it sorts there interest, in this case music towards individual preference.
However Collaborative filtering can be problematic as it requires masses amount of viewership and rating for collaborative filtering to make content a recommended option. This problem is called sparsity. (T. Ha & S. Lee 2017) This makes it extremely difficult for smaller content creators to break mass markets as it has far too few ratings for it to be considered as an option for choice discouraging establishing artists to try and make new music as this sort of process favours popular artists. A process that favours popularity alongside having advertisers market the content, shows a highly unequal distribution of power. Take for example Drake’s “Scorpion” album which was the front page of spotify at launch which led him to have the most streamed album on Spotify. From this alone we see one artist creating an unequal market share within an industry. To conclude, data is efficient in providing content to its users for content they may like, however can be problematic as it favours those with popularity and money.
Secondly through the use of social media data can mediate social status. As social media has become such a valuable tool of interaction for communications, data that constitutes to the amount of friends or likes a individual has on their social profile page, can now be accounted with someone's social status. The term ‘facebook fame’ is a term developed through the millenial age and used for when individuals have a may have a large amount of following on their facebook account having a celebrity oriented social media behaviours (D. N. Greenwood 2013). For example those who have have profile pictures with 300 or more likes or even having more than 3000 friends on facebook is perceived known to be quite popular within a niche social group and can therefore be referred to as facebook fame. Although this type of status that data creates can make individuals feel less valuable due to the number that accounts for the amount of friends they have or how many people like their post. I myself don't consider myself to be popular, however data and interpretation by individuals may consider myself to be popular due to my 800+ plus friends on facebook, and it shows that this type of data can define my status as a person. Furthermore this sort of mass social following towards an individual can also give them power when influencing others towards political views when shared. To conclude, through social media, data can mediate social status by the amount of following an individual has and through this can also give individuals influential power.
Finally businesses have the ability to track what and where you look in order to influence consumer purchases. This is so Websites can market online products that you may be interested in when you surf other websites. This is possible with a tool known as HTTP cookies or in short, cookies. This tool is a piece of data that's stored on a computer by the users internet browser in which can store information about the products that you may be interested in or even simply just remembering what was in your online shopping cart (A. Ester, 2017). This can be helpful when going back to the website as it shows your previous items that were observed.
In some circumstances cookies have the ability to access and promote information outside the website as third parties are able to track these cookies (with permission). In some instances third parties can create advertisements from these cookies when exploring different websites. For example when looking for guitar tabs on the website Ultimate Guitar, a total of three advertisements popped up from websites that were in my browser history alongside the product i was observing. The first being HYPE DC with showing Adidas Running shoes, and the IPhone XS plans from Optus. Advertisers gain power when they have a built profile that categorises you by influencing the way in which you consume goods and services. Furthermore access aren’t just advertiser but can also be the government. The government can track user data to create and campaign the most effective political propaganda. This type of power is further emphasised as there are no longer laws to support Net Neutrality, therefore ISPs (Internet Service Providers) have the ability to censor or degrade network performance in favour for particular political agendas. (H. Guo 2017). When looking at Castells definition of power, it shows how social actors are able to alter user experiences in order to influence individual decisions out of their own interests and will.
In conclusion, it’s evident that our lives is mediated through data. Our digital shadow creates huge amounts of data that are added to databases. It's inevitable that the data that is created allows for organisations to adapt their service and/or product to serve the masses. For the government it allows them easier to perform political economics through our data and campaign ads to have favour in elections and bills.
Although it's not how this data is used to cater for the individuals but its how data is mediated through our lives. Streaming services such as netflix and spotify use collaborative filtering to filter option to each individual preference. This process is effective however it neglects smaller volume content that may not have the same popularity and therefore, theoretically enforces the idea that the rich get richer, giving more power to those with a higher fan base. Data can also mediate our lives through dictating self worth and value in our society. This was explored through social media where the term “Facebook Famous” became evident for those with an large amount of facebook friends, and therefore those with less facebook friends feel less valuable. Finally through HTTP Cookies, web browsers are able to keep track of all your websites that you surf and in some instances third parties can access personal information that may be stored on the cookie in which some individuals may be naive too if not understood.