Big Data, post-Facebook experiment

A recent application of Big Data which has become understandably controversial is the Facebook experiment, where Facebook data scientists manipulated the feed content of selected users to include only positive or negative feed content. I have previously written about this.

The Guardian’s exposé on the U.S State Department’s PRISM project—which collects data from large technology companies— clearly highlighted the footprint users leave behind when utilizing the Internet. While this particular scenario represents a more extreme and some would argue unethical application of Big Data Technologies, the Facebook experiment reminded many of us why we spoke out about data privacy and PRISM. While many Internet users are aware of the trace data created via online interactions, the power and potential of this information when collected, aggregated, and analyzed is enormous and often easy to forget. The Facebook experiment speaks to the capability for nongovernmental entities such as corporations to easily access information that was previously not available nor analyzable. This type of information, paired with the right technology, can lend a unique glance into a person’s life and ultimately lead to more advanced insights directed towards a person’s interests, hobbies, activities, work, and more. This can be a welcome development in some contexts (e.g. those who opt into health behavior change interventions to quit smoking or lose weight).

However, most of the time, online footprint data (derived from platforms such as Twitter and Facebook) are used to facilitate personalized and targeted advertising (Silberstein, et al. 2011) at best and hyper-surveillance at worst. Some do not have a problem with this use of personal data (as a trade-off for ‘free’ services such as Facebook). Others, see the Facebook experiment as yet one more reason to either minimize their use on the dominant social networking site or quit altogether.

References:

Silberstein, A., Machanavajjhala, A. and Ramakrishnan, R. 2011 ‘Feed following: the big data challenge in social applications’ Databases and Social Networks: ACM.