Toward an Ethical Framework for the Text Mining of Social Media for Health Research: A Systematic Review

Ford, Elizabeth; Shepherd, Scarlett; Jones, Kerina; Hassan, Lamiece

doi:10.3389/fdgth.2020.592237

Cited by 22 publications

(28 citation statements)

References 76 publications

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“…Table 6 demonstrates examples of each error type. Whilst the text of reviews is on an online public resource, the example text for each error type has been synthesised from text corpuses to protect the identity of the patients and staff of the associated practices [ 15 ].…”

Section: Resultsmentioning

confidence: 99%

Assessing the reliability of automatic sentiment analysis tools on rating the sentiment of reviews of NHS dental practices in England

et al. 2021

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Background Online reviews may act as a rich source of data to assess the quality of dental practices. Assessing the content and sentiment of reviews on a large scale is time consuming and expensive. Automation of the process of assigning sentiment to big data samples of reviews may allow for reviews to be used as Patient Reported Experience Measures for primary care dentistry. Aim To assess the reliability of three different online sentiment analysis tools (Amazon Comprehend DetectSentiment API (ACDAPI), Google and Monkeylearn) at assessing the sentiment of reviews of dental practices working on National Health Service contracts in the United Kingdom. Methods A Python 3 script was used to mine 15800 reviews from 4803 unique dental practices on the NHS.uk websites between April 2018 –March 2019. A random sample of 270 reviews were rated by the three sentiment analysis tools. These reviews were rated by 3 blinded independent human reviewers and a pooled sentiment score was assigned. Kappa statistics and polychoric evalutaiton were used to assess the level of agreement. Disagreements between the automated and human reviewers were qualitatively assessed. Results There was good agreement between the sentiment assigned to reviews by the human reviews and ACDAPI (k = 0.660). The Google (k = 0.706) and Monkeylearn (k = 0.728) showed slightly better agreement at the expense of usability on a massive dataset. There were 33 disagreements in rating between ACDAPI and human reviewers, of which n = 16 were due to syntax errors, n = 10 were due to misappropriation of the strength of conflicting emotions and n = 7 were due to a lack of overtly emotive language in the text. Conclusions There is good agreement between the sentiment of an online review assigned by a group of humans and by cloud-based sentiment analysis. This may allow the use of automated sentiment analysis for quality assessment of dental service provision in the NHS.

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Section: Resultsmentioning

confidence: 99%

Assessing the reliability of automatic sentiment analysis tools on rating the sentiment of reviews of NHS dental practices in England

et al. 2021

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“…The ethical considerations that need to be taken into account when conducting research using social media data are evolving through discussions and collaborations among active researchers in this space. Currently, there is some consensus that it is appropriate, and perhaps necessary, to utilize all available public data for addressing serious public health crises, such as the ongoing opioid overdose epidemic [ 48 ]. Excluding this vast source of knowledge can be seen as a missed opportunity to identify potential prevention strategies.…”

Section: Discussionmentioning

confidence: 99%

Signals of increasing co-use of stimulants and opioids from online drug forum data

Sarker

Al-Garadi

et al. 2022

Harm Reduct J

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Background Despite recent rises in fatal overdoses involving multiple substances, there is a paucity of knowledge about stimulant co-use patterns among people who use opioids (PWUO) or people being treated with medications for opioid use disorder (PTMOUD). A better understanding of the timing and patterns in stimulant co-use among PWUO based on mentions of these substances on social media can help inform prevention programs, policy, and future research directions. This study examines stimulant co-mention trends among PWUO/PTMOUD on social media over multiple years. Methods We collected publicly available data from 14 forums on Reddit (subreddits) that focused on prescription and illicit opioids, and medications for opioid use disorder (MOUD). Collected data ranged from 2011 to 2020, and we also collected timelines comprising past posts from a sample of Reddit users (Redditors) on these forums. We applied natural language processing to generate lexical variants of all included prescription and illicit opioids and stimulants and detect mentions of them on the chosen subreddits. Finally, we analyzed and described trends and patterns in co-mentions. Results Posts collected for 13,812 Redditors showed that 12,306 (89.1%) mentioned at least 1 opioid, opioid-related medication, or stimulant. Analyses revealed that the number and proportion of Redditors mentioning both opioids and/or opioid-related medications and stimulants steadily increased over time. Relative rates of co-mentions by the same Redditor of heroin and methamphetamine, the substances most commonly co-mentioned, decreased in recent years, while co-mentions of both fentanyl and MOUD with methamphetamine increased. Conclusion Our analyses reflect increasing mentions of stimulants, particularly methamphetamine, among PWUO/PTMOUD, which closely resembles the growth in overdose deaths involving both opioids and stimulants. These findings are consistent with recent reports suggesting increasing stimulant use among people receiving treatment for opioid use disorder. These data offer insights on emerging trends in the overdose epidemic and underscore the importance of scaling efforts to address co-occurring opioid and stimulant use including harm reduction and comprehensive healthcare access spanning mental-health services and substance use disorder treatment.

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“…Given that the content often examined in psychological research is of a sensitive nature (eg, mental health issues and personal experiences), it may be particularly relevant to consider the ethical implications of using publicly available data (eg, social media), which might be linked to a person’s identity. We encourage researchers to consult ethics boards when determining whether approval is needed to use such data, even if it is publicly available [ 121 , 122 ]. Furthermore, social media data can be more prone to grammatical errors and increased ambiguity (eg, owing to spelling errors and slang) compared with scientific literature and formal documentation and may require more in-depth preprocessing depending on the nature of the research question.…”

Section: Discussionmentioning

confidence: 99%

Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review

Hagg¹,

Merkouris²,

O’Dea³

et al. 2022

J Med Internet Res

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Background Topic modeling approaches allow researchers to analyze and represent written texts. One of the commonly used approaches in psychology is latent Dirichlet allocation (LDA), which is used for rapidly synthesizing patterns of text within “big data,” but outputs can be sensitive to decisions made during the analytic pipeline and may not be suitable for certain scenarios such as short texts, and we highlight resources for alternative approaches. This review focuses on the complex analytical practices specific to LDA, which existing practical guides for training LDA models have not addressed. Objective This scoping review used key analytical steps (data selection, data preprocessing, and data analysis) as a framework to understand the methodological approaches being used in psychology research using LDA. Methods A total of 4 psychology and health databases were searched. Studies were included if they used LDA to analyze written words and focused on a psychological construct or issue. The data charting processes were constructed and employed based on common data selection, preprocessing, and data analysis steps. Results A total of 68 studies were included. These studies explored a range of research areas and mostly sourced their data from social media platforms. Although some studies reported on preprocessing and data analysis steps taken, most studies did not provide sufficient detail for reproducibility. Furthermore, the debate surrounding the necessity of certain preprocessing and data analysis steps is revealed. Conclusions Our findings highlight the growing use of LDA in psychological science. However, there is a need to improve analytical reporting standards and identify comprehensive and evidence-based best practice recommendations. To work toward this, we developed an LDA Preferred Reporting Checklist that will allow for consistent documentation of LDA analytic decisions and reproducible research outcomes.

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Toward an Ethical Framework for the Text Mining of Social Media for Health Research: A Systematic Review

Cited by 22 publications

References 76 publications

Assessing the reliability of automatic sentiment analysis tools on rating the sentiment of reviews of NHS dental practices in England

Assessing the reliability of automatic sentiment analysis tools on rating the sentiment of reviews of NHS dental practices in England

Signals of increasing co-use of stimulants and opioids from online drug forum data

Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review

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