Ethical Research Protocols for Social Media Health Research

By Adrian Donald Benton, Glen A. Coppersmith, Mark Dredze. 2017

236 citations recorded by the source corpus.

Social media have transformed data-driven research in political science, the social sciences, health, and medicine. Since health research often touches on sensitive topics that relate to ethics of treatment and patient privacy, similar ethical considerations should be acknowledged when using social media data in health research. While much has been said regarding the ethical considerations of social media research, health research leads to an additional set of concerns. We provide practical suggestions in the form of guidelines for researchers working with social media data in health research. These guidelines can inform an IRB proposal for researchers new to social media health research.

Subfields: Artificial Intelligence, Social Psychology, Sociology and Political Science

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Thus, data, tools, and inferences obtained for one purpose can be used for another purpose (Hovy and Spruit, 2016; Benton et al., 2017); yet, the risks associated with secondary uses may not be well understood.”
Natural language processing applied to mental illness detection: a narrative review (2022, methodology): “During the research, the researchers should follow strict protocols similar to the guidelines 202 introduced by Bentan et al., to ensure the data is properly applied in healthcare research while protecting privacy to avoid further psychological distress.”
Depression and Self-Harm Risk Assessment in Online Forums (2017, background): “…in social media to better discover and understand mental health related concerns (Resnik et al., 2013; De Choudhury et al., 2013; Copper-smith et al., 2014b,a; Mitchell et al., 2015; Tsug-awa et al., 2015; Coppersmith et al., 2015a; Al-thoff et al., 2016; Mowery et al., 2016; Benton et al., 2017b).”
Case Study: Deontological Ethics in NLP (2020, background): “…Schmaltz, 2018), data handling practices (Lewis et al., 2017; Mieskes, 2017) and specific domains like education (Mayfield et al., 2019; Loukina et al., 2019), health care (Šuster et al., 2017; Benton et al., 2017) and conversational agents (Cercas Curry and Rieser, 2018; Henderson et al., 2018).”
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task (2021, background): “Horvitz and Mulligan (2015) provide one short, useful discussion speciﬁcally focused on data and privacy, and Benton et al. (2017) and Chancellor et al. (2019) discuss ethical issues speciﬁcally with regard to social media and work on mental health.”
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (2021, methodology): “Although informed consent of each user was not sought as it may be deemed coercive, we perform automatic de-identification of the dataset using named entity recognition (Benton et al., 2017a,b) to reduce the risk of including any identifying data in the raw data.”
Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing (2021, background): “For example, Benton et al. (2017) write: [R]esearch that requires the annotation of corpora for training models involves hu- man annotators.”