Digital Traces in Context| Social Media Giveth, Social Media Taketh Away: Facebook, Friendships, and APIs

The early aughts saw an explosion of interest in social network sites. Many such sites, including Facebook, LinkedIn, and Twitter expanded to become “platforms,” meaning they are both websites and distributors of data. These data would typically be distributed to third parties in privacy-sensitive ways and regulated by the platform. Such access is based on balancing three issues: user privacy, generativity (i.e., capacity for novelty) for third parties, and control for platforms. Platforms appear to be seeking progressively more control at the cost of generativity by severely restricting third-party access to data about the user’s friends. The reductions or outright lack of access means that the insights from our digital traces are no longer as knowable to either third parties or users. This article unpacks this shift by clarifying some of the technical issues involved (particularly APIs, the main means of external data access). The case study of social network visualization is used to exemplify how social network sites seek control at the expense of generativity. The article notes how this shift was done with little oversight.

[1]  N. Ellison,et al.  Social capital, self-esteem, and use of online social network sites: A longitudinal analysis , 2008 .

[2]  Daniel W. Archambault,et al.  Communities Found by Users -- not Algorithms: Comparing Human and Algorithmically Generated Communities , 2016, CHI.

[3]  J. Moody The Structure of a Social Science Collaboration Network: Disciplinary Cohesion from 1963 to 1999 , 2004 .

[4]  Barry Wellman,et al.  The Relational Self-Portrait , 2014 .

[5]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[6]  Jessica Lingel,et al.  The age of sharing , 2017, New Media Soc..

[7]  Lada A. Adamic,et al.  Exposure to ideologically diverse news and opinion on Facebook , 2015, Science.

[8]  Bernie Hogan From Invisible Algorithms to Interactive Affordances: Data after the Ideology of Machine Learning , 2013 .

[9]  Robert E. Kraut,et al.  Social capital on facebook: differentiating uses and users , 2011, CHI.

[10]  Keith N. Hampton,et al.  Testing the validity of social capital measures in the study of information and communication technologies , 2014 .

[11]  Jinyoung Kim,et al.  "You can't block people offline": examining how facebook's affordances shape the disclosure process , 2014, CSCW.

[12]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[13]  Jean Underwood,et al.  When 'friends' collide: Social heterogeneity and user vulnerability on social network sites , 2016, Comput. Hum. Behav..

[14]  Ulrik Brandes,et al.  Studying Social Networks - A Guide to Empirical Research , 2013 .

[15]  Henry L Hu The Generative Internet(创生性的互联网) , 2010 .

[16]  Bernhard Rieder,et al.  Studying Facebook via data extraction: the Netvizz application , 2013, WebSci.

[17]  Bernhard Rieder,et al.  Data critique and analytical opportunities for very large Facebook Pages: Lessons learned from exploring “We are all Khaled Said” , 2015, Big Data Soc..

[18]  Danah Boyd,et al.  I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience , 2011, New Media Soc..

[19]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[20]  Noshir Contractor,et al.  Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants , 2016, CHI.

[21]  B. Bahrami,et al.  Online social network size is reflected in human brain structure , 2011, Proceedings of the Royal Society B: Biological Sciences.

[22]  Y. Bian Bringing strong ties back in: Indirect ties, network bridges, and job searches in China , 1997 .

[23]  Anabel Quan-Haase,et al.  Uses and Gratifications of Social Media: A Comparison of Facebook and Instant Messaging , 2010 .

[25]  Cliff Lampe,et al.  Facebook as a toolkit: A uses and gratification approach to unbundling feature use , 2011, Comput. Hum. Behav..

[26]  B. Kahle THE INTERNET ARCHIVE , 2012 .

[27]  Christine Greenhow,et al.  First-Generation Students and College: The Role of Facebook Networks as Information Sources , 2016, CSCW.

[28]  Lawrence Lessig,et al.  Code and Other Laws of Cyberspace , 1999 .

[29]  Anja Bechmann,et al.  Using APIs for Data Collection on Social Media , 2014, Inf. Soc..

[30]  Morten Tromholt,et al.  The Facebook Experiment: Quitting Facebook Leads to Higher Levels of Well-Being , 2016, Cyberpsychology Behav. Soc. Netw..

[31]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[32]  Danah Boyd,et al.  Vizster: visualizing online social networks , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[33]  Bernie Hogan,et al.  Assessing the Structural Correlates between Friendship Networks and Conversational Agency in Facebook Groups , 2015, ICWSM.

[34]  C. Fuchs Social Media: A Critical Introduction , 2013 .

[35]  Eli Pariser,et al.  The Filter Bubble: What the Internet Is Hiding from You , 2011 .

[36]  Mihai Udrescu,et al.  Uncovering the fingerprint of online social networks using a network motif based approach , 2016, Comput. Commun..

[37]  Robin I. M. Dunbar Social Brain Hypothesis , 1998, Encyclopedia of Evolutionary Psychological Science.

[38]  Cliff Lampe,et al.  Assessing structural correlates to social capital in Facebook ego networks , 2014, Soc. Networks.

[39]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..