Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List

Adams, Nikki; Artigiani, Eleanor; Wish, Eric D.

doi:10.1177/0022042619833911

Cited by 19 publications

(14 citation statements)

References 17 publications

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“…In a nonmedical cannabis-related study, word embeddings created from Twitter and Reddit data sets discovered synonyms and slang terms that could not be identified using other means. The study recommends this method of synonym discovery in advance for any data collection based on keyword filtering [ 65 ].…”

Section: Resultsmentioning

confidence: 99%

Consumer-Generated Discourse on Cannabis as a Medicine: Scoping Review of Techniques

Habibabadi¹,

Hallinan²,

Bonomo³

et al. 2022

J Med Internet Res

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Background Medicinal cannabis is increasingly being used for a variety of physical and mental health conditions. Social media and web-based health platforms provide valuable, real-time, and cost-effective surveillance resources for gleaning insights regarding individuals who use cannabis for medicinal purposes. This is particularly important considering that the evidence for the optimal use of medicinal cannabis is still emerging. Despite the web-based marketing of medicinal cannabis to consumers, currently, there is no robust regulatory framework to measure clinical health benefits or individual experiences of adverse events. In a previous study, we conducted a systematic scoping review of studies that contained themes of the medicinal use of cannabis and used data from social media and search engine results. This study analyzed the methodological approaches and limitations of these studies. Objective We aimed to examine research approaches and study methodologies that use web-based user-generated text to study the use of cannabis as a medicine. Methods We searched MEDLINE, Scopus, Web of Science, and Embase databases for primary studies in the English language from January 1974 to April 2022. Studies were included if they aimed to understand web-based user-generated text related to health conditions where cannabis is used as a medicine or where health was mentioned in general cannabis-related conversations. Results We included 42 articles in this review. In these articles, Twitter was used 3 times more than other computer-generated sources, including Reddit, web-based forums, GoFundMe, YouTube, and Google Trends. Analytical methods included sentiment assessment, thematic analysis (manual and automatic), social network analysis, and geographic analysis. Conclusions This study is the first to review techniques used by research on consumer-generated text for understanding cannabis as a medicine. It is increasingly evident that consumer-generated data offer opportunities for a greater understanding of individual behavior and population health outcomes. However, research using these data has some limitations that include difficulties in establishing sample representativeness and a lack of methodological best practices. To address these limitations, deidentified annotated data sources should be made publicly available, researchers should determine the origins of posts (organizations, bots, power users, or ordinary individuals), and powerful analytical techniques should be used.

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Section: Resultsmentioning

confidence: 99%

Consumer-Generated Discourse on Cannabis as a Medicine: Scoping Review of Techniques

Habibabadi¹,

Hallinan²,

Bonomo³

et al. 2022

J Med Internet Res

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“…Depending on the objective of the study, this could result in "contaminated" data that did not truly reflect the sentiments of the target study population, and could consequently lead to imprecise or incorrect conclusions. 20,21,39,40 Future studies may consider adopting the methods proposed by Kim et al 41 and Adams et al 42 on how to develop and iteratively refine search keywords (eg, through word embeddings) for retrieving the content of interest from social media platforms, and how to thoroughly evaluate the relevance, and comprehensiveness (if applicable), of the information retrieved using manually annotated data. Furthermore, few studies described how they handled special types of data such as images or videos, hashtags, emojis, and hyperlinks, which are commonly used in social media discourses and can in fact convey important information about the sentiments being expressed.…”

Section: Discussionmentioning

confidence: 99%

Developing a standardized protocol for computational sentiment analysis research using health-related social media data

Yin

et al. 2020

Journal of the American Medical Informatics Association

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Objective Sentiment analysis is a popular tool for analyzing health-related social media content. However, existing studies exhibit numerous methodological issues and inconsistencies with respect to research design and results reporting, which could lead to biased data, imprecise or incorrect conclusions, or incomparable results across studies. This article reports a systematic analysis of the literature with respect to such issues. The objective was to develop a standardized protocol for improving the research validity and comparability of results in future relevant studies. Materials and Methods We developed the Protocol of Analysis of senTiment in Health (PATH) based on a systematic review that analyzed common research design choices and how such choices were made, or reported, among eligible studies published 2010-2019. Results Of 409 articles screened, 89 met the inclusion criteria. A total of 16 distinctive research design choices were identified, 9 of which have significant methodological or reporting inconsistencies among the articles reviewed, ranging from how relevance of study data was determined to how the sentiment analysis tool selected was validated. Based on this result, we developed the PATH protocol that encompasses all these distinctive design choices and highlights the ones for which careful consideration and detailed reporting are particularly warranted. Conclusions A substantial degree of methodological and reporting inconsistencies exist in the extant literature that applied sentiment analysis to analyzing health-related social media data. The PATH protocol developed through this research may contribute to mitigating such issues in future relevant studies.

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“…However, recently there has been a move towards a more data-driven means of iteratively identifying and evaluating keywords (and their associated synonyms), with word embeddings and other empirical synonym discovery methods (e.g. [108]). This shift towards a more principled method of selecting keywords for data sampling is to be welcomed.…”

Section: Discussionmentioning

confidence: 99%

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

Conway

Chapman

2019

Yearb Med Inform

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Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. Results: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review “modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than “classical" machine learning methods.

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Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List

Cited by 19 publications

References 17 publications

Consumer-Generated Discourse on Cannabis as a Medicine: Scoping Review of Techniques

Consumer-Generated Discourse on Cannabis as a Medicine: Scoping Review of Techniques

Developing a standardized protocol for computational sentiment analysis research using health-related social media data

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

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