Since its onset, scholars have characterized social media as a valuable source for data collection since it presents several benefits (e.g. exploring research questions with hard-to-reach populations). Nonetheless, methods of online data collection are riddled with ethical and methodological challenges that researchers must consider if they want to adopt good practices when collecting and analyzing online data. Drawing from our primary research project, where we collected passive online data on Reddit, we explore and detail the steps that researchers must consider (...) before collecting online data: (1) planning online data collection; (2) ethical considerations; and (3) data collection. We also discuss two atypical questions that researchers should also consider: (1) how to handle deleted user-generated content; and (2) how to quote user-generated content. Moving on from the dichotomous discussion between what is public and private data, we present recommendations for good practices when collecting and analyzing qualitative online data. (shrink)

The objective of this research is to assess the impact of ethics on research productivity within laboratories of public universities. To achieve this objective, neural networks’ method is used to highlight impacting and impacted variable modalities. Findings show ethical variables having the greatest impact on research productivity are ethics supporting document and transparency, while the variables most impacted by ethics are publications and scientific projects. Finally, the originality of this work lies in the reconciliation of two dimensions that appear to (...) be irreconcilable a priori, namely, ethics which has a social connotation and productivity which has an economic connotation. It also resides in employing neural networks’ method, with is rarely used in social sciences. (shrink)

This paper analyzes the ethics of social science research employing big data. We begin by highlighting the research gap found on the intersection between big data ethics, SSR and research ethics. We then discuss three aspects of big data SSR which make it warrant special attention from a research ethics angle: the interpretative character of both SSR and big data, complexities of anticipating and managing risks in publication and reuse of big data SSR, and the paucity of regulatory oversight and (...) ethical recommendations on protecting individual subjects as well as societies when conducting big data SSR. Against this backdrop, we propose using David Resnik’s research ethics framework to analyze some of the most pressing ethical issues of big data SSR. Focusing on the principles of honesty, carefulness, openness, efficiency, respect for subjects, and social responsibility, we discuss three clusters of ethical issues: those related to methodological biases and personal prejudices, those connected to risks arising from data availability and reuse, and those leading to individual and social harms. Finally, we advance considerations to observe in developing future ethical guidelines about big data SSR. (shrink)

Research Ethics, Volume 18, Issue 2, Page 93-113, April 2022. Twitter is an increasingly common source of rich, personalized qualitative data, as millions of people daily share their thoughts on myriad topics. However, questions remain unclear concerning if and how to quote publicly available social media data ethically. In this study, focusing on 136 education manuscripts quoting 2667 Tweets, we look to investigate the ways in which Tweets are quoted, the ethical discussions forwarded and actions taken, and the extent to (...) which quoted Tweets are “discoverable.” A concerning result is that in almost all manuscripts, and for around half of all quoted Tweets, the original author could be identified. Drawing on our findings we share some ethical dilemmas, including those that arise from an apparent lack of understanding of the technical aspects of the platform, and offer suggestions for promoting ethically-informed practice. (shrink)