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

Abstract

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.

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis

“…Thus, the development of automated tools might contribute to ethical practices and research implications, though outside the standard framework of research ethics. Algorithms should pursue the maximum benefit minimising the risk of potential harm during data collection, analysis and publication, while researchers should assess algorithms' performance and routinely test them for effectiveness, avoiding the mislabelling of content [61]. Furthermore, discarding re-tweets may be considered a discretionary choice, since we aimed at preliminarily investigating individual-level data on social networking about alcohol-related behaviours.…”

The vaccination debate in the “post‐truth” era: social media as sites of multi‐layered reflexivity

“…7 We suppose that users perceived the page as public space where they intentionally wanted to express their own opinion on vaccination. On the other hand, we also suppose that many users are not completely aware of the privacy setting and its consequences (Williams et al 2017) and we thus considered the risk of harm and the sensitivity of the data (see Townsend and Wallace 2016). In light of this consideration, several potentially illustrative quotations were dropped or trimmed down.…”

A Computational Linguistic Study of Personal Recovery in Bipolar Disorder

“…However, in practice it is often infeasible to seek retrospective consent from hundreds or thousands of social media users. According to current ethical guidelines for social media research (Benton et al, 2017a;Williams et al, 2017) and practice in comparable research projects (O'Dea et al, 2015;Ahmed et al, 2017), it is regarded as acceptable to waive explicit consent if the anonymity of the users is preserved. Therefore, we will not ask the account holders of Twitter and Reddit posts included in our datasets for their consent.…”