Reference:
Lazer, D., & Radford, J. (2017). Data ex machina: Introduction to big data. Annual Review of Sociology, 43, 19-39.
Reflection:
This article reminded me of two things, why I love sociology/psychology and why I like literature surveys. Literature surveys, Sociology and Big data seemed like a good amalgamation of things which reveal interesting findings about literature, groups of people and patterns respectively.
The article kind of puts the semester long class of social computing in one place by discussing Big data, social systems, bots and sock puppets creating the illusion of perfect user and social contagion, to name a few. The author emphasizes the strength of incorporating sociological studies and computer science to make the best use of Big Data. He explores the opportunities and threats and also provides suggestions for addressing future challenges with big data research.
The author did a great job of summarizing almost every aspect of the opportunities and vulnerabilities associated with Big Data . The role of big data in transforming learning and higher education seemed to be missing though. Big data its still a niche topic in the field of education, but governments have started to produce reports about the potential of big data in education [1]
Although at a glance, most of the studies designed to gather or examine big data to extract useful pattern look intrusive (like the behavioral involving college students who were provided with cell phones for the investigation of user data), I believe their is a need to educate people about the greater good of collaborating by providing passive, non-sensitive data for scientific and behavioral studies. Because we are generally skeptical about possible privacy invasions through our phones and social media accounts, but also want to remain foremost at the receiving end of scientific advancements.
Just as in an emergency people are willing to do monetary help, they should also be educated to contribute by providing data to investigate the situation. This will greatly reduce the vulnerabilities associated with big data such as errors and misinterpretations of data due to self-reporting.
[1]Eynon, R. (2013). The rise of Big Data: what does it mean for education, technology, and media research?.
