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Ethics of Big Data in practice: Social media research

Wednesday, 24 February 2016, 12.00pm to 2.00pm

Ethics of Big Data in practice: Social media research

Dr Dhiraj Murthy (Goldsmiths, Department of Sociology)


The ethics of using social media data are not being sufficiently taught in the social sciences. This has much to do with the fact that social scientists themselves are not well-versed in social media ethics. There are major ethical implications in using social media data in social research and this session broadly explores why this needs to be taken seriously. This session will explore the ethical challenges faced by the research community when using social media data. A starting point draws from the Association of Internet Researcher’s guideline that “all digital information at some point involves individual persons, [and] consideration of principles related to research on human subjects may be necessary even if it is not immediately apparent how and where persons are involved in the research data.” Given recent controversies in large-scale social media research, particularly around the Kramer, Guillory, & Hancock’s (2014) Facebook news feed experiment, there will be a particular focus on the use of big data methods. Using my own diverse research projects with Twitter, Yik Yak, Instagram, and YouTube data as case studies, I will discuss how my collaborators and I have endeavored to follow best practices in our research and the challenges of taking a strong duty of care.


Dhiraj Murthy is Reader of Sociology at Goldsmiths, University of London. His research explores social media, virtual organizations, virtual teams, and digital research methods. He has authored numerous articles, book chapters, and papers and a book about Twitter, the first on the subject. His work on social networking technologies in virtual organization breeding grounds was funded by the National Science Foundation’s Office of CyberInfrastructure. Dhiraj currently co-directs the interdisciplinary Centre for Creative & Social Technologies (CAST).