Monthly Archives: November 2016

Social media is heavily influenced by algorithms. For example, the Facebook feed algorithm, from what we know about it, is based on what you and your friends are liking, posting, and doing on the platform (and perhaps even ‘people like you’ that Facebook is data mining). Many social media algorithms are designed around homophily. And algorithms theoretically are value neutral. If someone consumes and produces criminal content, the algorithm will try to be helpful and guide the user to relevant criminal content. The algorithms are just following what they are programmed to do.  algorithms can equally encourage content around positive civic responsibility, if a user has displayed a preference in that direction.

To be critical about algorithms, we do have acknowledge the advantages and disadvantages of algorithm proliferation. For example, some algorithms are designed for safeguarding and this can be a real positive. There might be algorithmically-based filters for Internet searching or video delivery specific to kids for example. If a child has a profile on Netflix which is specifically set to Netflix’s child setting, then by the algorithm’s definition, they are not supposed to receive content that is age inappropriate. This tends to work in practice. Though, if content is inappropriately categorized, the algorithm would of course just follow its rule-based rubric instructions and would guide kids to inappropriate content as well. So humans are very much part of this process and if errors occur, then not having humans in the loop can partially be attributable to some of the issues of whether algorithms break down in these instances.

Ultimately, the algorithms driving social media are what are called ‘black box algorithms’. These can be defined as algorithms that are generally proprietary, and which are open-source. The algorithm is meant to be private in terms of its design and operation and documentation is not made publicly available, nor is data made available in terms of the decisions made by the algorithm. In this way, black box algorithms are also similar in that we can only infer particular aspects of the algorithm based on observing the algorithm’s behavior ‘in the wild’.