Towards an Ethical Framework for Publishing Twitter Data in Social Research Taking into Account Users’ Views, Online Context and Algorithmic Estimation

• Published in: Sociology (Oxford) Vol. 51; no. 6; pp. 1149 - 1168
• Main Authors: Williams, Matthew L, Burnap, Pete, Sloan, Luke
• Format: Journal Article
• Language: English
• Published: London, England Sage Publications, Ltd 01.12.2017; SAGE Publications; Sage Publications Ltd
• Subjects: Algorithms; Classification; Data; Data science; Diet; Disinhibition; Electronic publishing; Ethics; Informed consent; Internet; Publishing; Reflexivity; Research methodology; Social media; Social networks; Social research; Sociologists; algorithms; context collapse; Twitter; ethics; computational social science; social media; social data science
• ISSN: 0038-0385; 1469-8684
• DOI: 10.1177/0038038517708140

New and emerging forms of data, including posts harvested from social media sites such as Twitter, have become part of the sociologist’s data diet. In particular, some researchers see an advantage in the perceived ‘public’ nature of Twitter posts, representing them in publications without seeking informed consent. While such practice may not be at odds with Twitter’s terms of service, we argue there is a need to interpret these through the lens of social science research methods that imply a more reflexive ethical approach than provided in ‘legal’ accounts of the permissible use of these data in research publications. To challenge some existing practice in Twitter-based research, this article brings to the fore: (1) views of Twitter users through analysis of online survey data; (2) the effect of context collapse and online disinhibition on the behaviours of users; and (3) the publication of identifiable sensitive classifications derived from algorithms.