Ethical Research Protocols for Social Media Health Research

Benton, Adrian; Coppersmith, Glen; Dredze, Mark

doi:10.18653/v1/w17-1612

Cited by 172 publications

(162 citation statements)

References 27 publications

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“…In contrast, the harm that common types of social data research can produce is often of a different nature, such as suffering a breach of privacy, or being exposed to disturbing images. An ethics approval process designed specifically for social data research, like the one brought forward by Bowser and Tsai (2015), which includes questions that are social media specific, or the set of practices outlined by Benton et al (2017) could be more appropriate to decide whether a research activity should take place or not, and under what conditions. Ethical choices in social data research require deliberation.…”

Section: Navigating a Fine Line: Research Ethics Of Digital Datamentioning

confidence: 99%

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

et al. 2019

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Social data in digital form-including user-generated content, expressed or implicit relations between people, and behavioral traces-are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding "what the world thinks" about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naïve usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them. "For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated."-Ursula Franklin 1

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Section: Navigating a Fine Line: Research Ethics Of Digital Datamentioning

confidence: 99%

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

et al. 2019

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“…In addition, we manually review a random sample of the results from the attention mechanism, and provide a few paraphrased examples (Benton et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health

Ive¹,

Gkotsis²,

Dutta³

et al. 2018

Proceedings of the Fifth Workshop on Computational Linguistics And Clinical Psychology: From Keyboard to Clinic

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Mental health problems represent a major public health challenge. Automated analysis of text related to mental health is aimed to help medical decision-making, public health policies and to improve health care. Such analysis may involve text classification. Traditionally, automated classification has been performed mainly using machine learning methods involving costly feature engineering. Recently, the performance of those methods has been dramatically improved by neural methods. However, mainly Convolutional neural networks (CNNs) have been explored. In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health. We show that this architecture improves overall classification results as compared to previously reported results on the same data. Benefitting from the attention mechanism, it can also efficiently select text elements crucial for classification decisions, which can also be used for in-depth analysis.

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“…Information provided to participant teams included post id (a unique identifier for the post), user id (a unique numeric identifier for the user who authored the post), timestamp (time the post was created, encoded as a Unix epoch), subreddit (the name of the subreddit where the post appeared), post title (title of the post) and post body (text contents of the post). 4 As discussed further in Section 7, although Reddit data are publicly available and the site was created specifically for anonymous posting, discussions on the platform nonetheless need to be viewed as sensitive and subject to careful ethical consideration (Benton et al, 2017;Chancellor et al, 2019). For that reason, a number of steps were taken to further remove identifying information from the dataset for the shared task.…”

Section: Reddit Posts and Metadatamentioning

confidence: 99%

Untitled

Zirikly¹,

Resnik²,

Uzuner³

et al. 2019

Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

100

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The shared task for the 2019 Workshop on Computational Linguistics and Clinical Psychology (CLPsych'19) introduced an assessment of suicide risk based on social media postings, using data from Reddit to identify users at no, low, moderate, or severe risk. Two variations of the task focused on users whose posts to the r/SuicideWatch subreddit indicated they might be at risk; a third task looked at screening users based only on their more everyday (non-SuicideWatch) posts. We received submissions from 15 different teams, and the results provide progress and insight into the value of language signal in helping to predict risk level.

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Ethical Research Protocols for Social Media Health Research

Cited by 172 publications

References 27 publications

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health

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