Sustainable Prospects – Privacy Issues of Social Media
The recent rise of social media sites and other digital media has opened a can of worms in terms of privacy issues surrounding users.
“Such apparent openness to reveal personal information to vast networks of loosely defined acquaintances and complete strangers calls for attention.”
– (Gross and Acquisti, 2005: 72)
Where data is accessible (often unintentionally), our personal information is available to third-party viewers including employers and other organisations. It is our fault as users though. Gross and Acquisti argue that whilst many of us express our concerns about privacy online, we often fail to properly adjust our privacy settings. They go on to discuss how “across different sites, anecdotal evidence suggests that participants are happy to disclose as much information as possible to as many people as possible.” (Gross and Acquisti, 2005: 72)
Too often, we as users, are naive and make the assumptions that the data and information we have shared is only being used and viewed by those we have chosen to share it with. We are not aware that the data can be accessed by other people. The things we like and share can be used to predict our sexual orientation, ethnic background, and political affiliations. (Kosinski, Stillwell and Graepel, 2013). Kosinski et al recruited more than 58,000 volunteers from Facebook. Background information was compiled by the researchers on each volunteer.
The model that the researchers devised was able to determine the correct gender of the user in 93% of the data. The user’s race was correctly determined in 95% of users. The model produced an 80% success rate in determining the political affiliation and religious group. The research results success rates are shown in the chart below (figure 1)
Figure 1: Kosinski, Stillwell and Graepel 2013: 5803
We regularly like posts on Facebook and other social media platforms. We do not realise what these reveal about us. Facebook likes are publically available by default.
Reading the research paper that Kosinski et al produced at the end of their study really shocked me. I had not realised the level to which this information could be used to predict my attributes and interests. I had assumed, like many others, that as my privacy settings were set to keep as much of my information private as I can, that my likes would be public and could be used to profile me.
Likes represent a very generic class of digital records, similar to Web search queries, Web browsing histories, and credit card purchases.
– (Kosinski, Stillwell and Graepel, 2013: 5802)
So what does this mean to me and my practice?
For each post where interaction is required, I need to make sure that I am transparent in how the data will be used. I need my followers and potential connections to feel confident in my use of their information. Directing them to a sign-up page on a website that details the intended use of the data, may prove to be a more reassuring way for people to engage with my work.
I also need to ensure that I protect the privacy of participants in my work to the best of my ability. Not revealing personal information about them without express permission will help do this. I intend to maintain a level of trustworthiness and confidentiality to all the work that I produce. Ensuring I have the correct model releases in place permitting digital dissemination and identification of the subject need to be in place for any shared images. These model release contracts exist so that both parties can agree on expectations both during and after the photoshoot. The document needs to be signed by myself and the subject as proof that an agreement was made about how the images can be used and disseminated. Verbal agreements provide no evidence and can become one person’s word against another. This is why the contracts need to be in the written form.
I also need to be fully aware of how my data could be used to determine things about me and my practice. Moving forwards with the active promotion of my practice, I will need to be mindful at all times of the impact of each keystroke. My intention is to maintain these accounts for professional use only, without the expression of personal beliefs.
Gross, R. and Acquisti, A. (2005) Information revelation and privacy in online social networks. Proceedings of the 2005 ACM workshop on Privacy in the electronic society. (2005). New York, NY: ACM, pp.71-80.
Kosinski, M., Stillwell, D. and Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), pp.5802-5805.
Figure 1: Kosinski, M., Stillwell, D. and Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), pp.5802-5805.