What Filmmakers and Other New World Storytellers Can Learn From Recent Academic Research About Social Media

If you don't have time to examine all the academic literature to see what's new in social media research, the good people at Harvard's Kennedy School - specifically the contributors to "Journalist’s Resource" from the Shorenstein Center on the Press, Politics and Public Policy - have simplified that task for you...

Their recent work is summarized in a Dec. 20th, 2012 post to the Nieman Journalism Lab blog that lists 10 research papers representing a "range of social media research produced in 2012 [that] has been wide and diverse: from what works on Twitter to explorations of meme “virality”; from Facebook’s power to motivate to the hidden dynamics of friend networks; from SMS power in the Arab uprising to the questionable creep of social “Big Data.”"

As someone who is profoundly interested in the power of algorithms to connect users to films they might enjoy, I am particularly interested in the Big Data paper by Microsoft researchers danah boyd and Kate Crawford. Even though they work for Microsoft, these researchers avoid buying into the pro-tech dogma and write provocatively about the context and consequences of our new "capacity to search, aggregate, and cross-reference large data sets:"

"Like other socio-technical phenomena, Big Data triggers both utopian and dystopian rhetoric. On one hand, Big Data is seen as a powerful tool to address various societal ills, offering the potential of new insights into areas as diverse as cancer research, terrorism, and climate change. On the other, Big Data is seen as a troubling manifestation of Big Brother, enabling invasions of privacy, decreased civil freedoms, and increased state and corporate control. As with all socio-technical phenomena, the currents of hope and fear often obscure the more nuanced and subtle shifts that are underway."

"Historically, sociologists and anthropologists collected data about people's relationships through surveys, interviews, observations, and experiments. Using this data, they focused on describing people's ‘personal networks’ – the set of relationships that individuals develop and maintain (Fischer 1982). These connections were evaluated based on a series of measures developed over time to identify personal connections. Big Data introduces two new popular types of social networks derived from data traces: ‘articulated networks’ and ‘behavioral networks’."

"Articulated networks are those that result from people specifying their contacts through technical mechanisms like email or cell phone address books, instant messaging buddy lists, ‘Friends’ lists on social network sites, and ‘Follower’ lists on other social media genres. The motivations that people have for adding someone to each of these lists vary widely, but the result is that these lists can include friends, colleagues, acquaintances, celebrities, friends-of-friends, public figures, and interesting strangers."

"Behavioral networks are derived from communication patterns, cell coordinates, and social media interactions (Onnela et al. 2007; Meiss et al. 2008). These might include people who text message one another, those who are tagged in photos together on Facebook, people who email one another, and people who are physically in the same space, at least according to their cell phone."

"Both behavioral and articulated networks have great value to researchers, but they are not equivalent to personal networks. For example, although contested, the concept of ‘tie strength’ is understood to indicate the importance of individual relationships (Granovetter 1973). When mobile phone data suggest that workers spend more time with colleagues than their spouse, this does not necessarily imply that colleagues are more important than spouses. Measuring tie strength through frequency or public articulation is a common mistake: tie strength – and many of the theories built around it – is a subtle reckoning in how people understand and value their relationships with other people. Not every connection is equivalent to every other connection, and neither does frequency of contact indicate strength of relationship. Further, the absence of a connection does not necessarily indicate that a relationship should be made."

Here (in bold) is the central message that I took away after reading the insightful "Critical Questions in Big Data" paper by danah boyd and Kate Crawford:

"Data are not generic. There is value to analyzing data abstractions, yet retaining context remains critical, particularly for certain lines of inquiry. Context is hard to interpret at scale and even harder to maintain when data are reduced to fit into a model. Managing context in light of Big Data will be an ongoing challenge."

Thanks to Ira Deutchman for the link.

1. Fischer, C. 1982. To Dwell Among Friends: Personal Networks in Town and City, Chicago: University of Chicago.

2. Onnela, J. P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J. and Barabási, A. L. 2007. Structure and tie strengths in mobile communication networks. Proceedings from the National Academy of Sciences, 104 ( 18 ): 7332 – 7336. [CrossRef], [PubMed], [Web of Science ®]

3. Meiss, M. R., Menczer, F. and Vespignani, A. 2008. Structural analysis of behavioral networks from the Internet. Journal of Physics A: Mathematical and Theoretical, 41 ( 22 ): 220 – 224. [CrossRef], [Web of Science ®]

4. Granovetter, M. S. 1973 . The strength of weak ties . American Journal of Sociology, 78 ( 6 ): 1360 – 1380. [CrossRef], [Web of Science ®], [CSA]

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