Extracting medical entities from social media

Šćepanović, Sanja; Martín-López, Enrique; Quercia, Daniele; Baykaner, Khan

doi:10.1145/3368555.3384467

Cited by 25 publications

(30 citation statements)

References 34 publications

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“…For example, the De Alva et al [ 40 ] study described selecting 32 posts containing keywords from 6 mental health subreddits without stating how the researchers searched for the keywords. Other examples included references to data being crawled [ 73 ], downloaded [ 65 ], and collected [ 43 ] without further specification and an uncited reference to a previously used data set [ 51 ]. Having conveyed an overview of data collection approaches, Table 3 presents the analytic focus of the included studies to better understand how researchers used their data sets to study depression and anxiety.…”

Section: Resultsmentioning

confidence: 99%

“…For example, the Shen and Rudzicz [ 68 ] study used ML to classify anxiety in user posts with a data set of 9971 posts from 4 anxiety subreddits and 12,837 posts from 25 control subreddits deemed unrelated to mental health. Human annotators contributed to labeling data in 75% (27/36) of studies and were variously described as layperson annotators [ 76 ], raters familiar with Reddit and its mental health communities [ 42 ], Amazon Mechanical Turk workers [ 65 ], 2 mental health domain experts [ 48 ] a clinical psychologist [ 67 ], a social media expert and clinical psychologist duo [ 41 ], and simply human annotators [ 53 , 66 ]. Of the 36 mental health classification studies, 14 (39%) studies incorporated external mental health data sets into data labeling procedures to support the ground truth of classification.…”

Section: Resultsmentioning

confidence: 99%

“…Of the 36 mental health classification studies, 14 (39%) studies incorporated external mental health data sets into data labeling procedures to support the ground truth of classification. External data set sources ranged from Wikipedia [ 36 ], Twitter [ 37 ], and AskAPatient [ 65 ] to formalized medical sources, including the Unified Medical Language System [ 31 ], the International Classification of Diseases, 10th Revision [ 48 ], and the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders [ 48 , 69 ].…”

Section: Resultsmentioning

confidence: 99%

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Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Boettcher¹

2021

JMIR Ment Health

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Background The study of depression and anxiety using publicly available social media data is a research activity that has grown considerably over the past decade. The discussion platform Reddit has become a popular social media data source in this nascent area of study, in part because of the unique ways in which the platform is facilitative of research. To date, no work has been done to synthesize existing studies on depression and anxiety using Reddit. Objective The objective of this review is to understand the scope and nature of research using Reddit as a primary data source for studying depression and anxiety. Methods A scoping review was conducted using the Arksey and O’Malley framework. MEDLINE, Embase, CINAHL, PsycINFO, PsycARTICLES, Scopus, ScienceDirect, IEEE Xplore, and ACM academic databases were searched. Inclusion criteria were developed using the participants, concept, and context framework outlined by the Joanna Briggs Institute Scoping Review Methodology Group. Eligible studies featured an analytic focus on depression or anxiety and used naturalistic written expressions from Reddit users as a primary data source. Results A total of 54 studies were included in the review. Tables and corresponding analyses delineate the key methodological features, including a comparatively larger focus on depression versus anxiety, an even split of original and premade data sets, a widespread analytic focus on classifying the mental health states of Reddit users, and practical implications that often recommend new methods of professionally delivered monitoring and outreach for Reddit users. Conclusions Studies of depression and anxiety using Reddit data are currently driven by a prevailing methodology that favors a technical, solution-based orientation. Researchers interested in advancing this research area will benefit from further consideration of conceptual issues surrounding the interpretation of Reddit data with the medical model of mental health. Further efforts are also needed to locate accountability and autonomy within practice implications, suggesting new forms of engagement with Reddit users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Boettcher¹

2021

JMIR Ment Health

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“…However, our methodology can be customized both for other sources of data, by modifying the lists of keywords, and domains, as we illustrated by adapting the vaccination schedule pipeline to identify and classify experiences of AEFI. Our models may be improved using deep-learning techniques [38] that may produce models both more generalizable to new domains and flexible in terms of different vaccination hesitancy stances (although, again, this may need an extensive annotation effort). These limitations, however, complement those of traditional survey-based methods.…”

Section: Discussionmentioning

confidence: 99%

Detecting adherence to the recommended childhood vaccination schedule from user-generated content in a US parenting forum

et al. 2021

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Vaccine hesitancy is considered as one of the leading causes for the resurgence of vaccine preventable diseases. A non-negligible minority of parents does not fully adhere to the recommended vaccination schedule, leading their children to be partially immunized and at higher risk of contracting vaccine preventable diseases. Here, we leverage more than one million comments of 201,986 users posted from March 2008 to April 2019 on the public online forum BabyCenter US to learn more about such parents. For 32% with geographic location, we find the number of mapped users for each US state resembling the census population distribution with good agreement. We employ Natural Language Processing to identify 6884 and 10,131 users expressing their intention of following the recommended and alternative vaccination schedule, respectively RSUs and ASUs. From the analysis of their activity on the forum we find that ASUs have distinctly different interests and previous experiences with vaccination than RSUs. In particular, ASUs are more likely to follow groups focused on alternative medicine, are two times more likely to have experienced adverse events following immunization, and to mention more serious adverse reactions such as seizure or developmental regression. Content analysis of comments shows that the resources most frequently shared by both groups point to governmental domains (.gov). Finally, network analysis shows that RSUs and ASUs communicate between each other (indicating the absence of echo chambers), however with the latter group being more endogamic and favoring interactions with other ASUs. While our findings are limited to the specific platform analyzed, our approach may provide additional insights for the development of campaigns targeting parents on digital platforms.

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“…This spans environmental, policy and other social factors such as racism 55 . Some work leveraging new data and machine learning methods has focused on using natural language processing, computer vision or other approaches to identify and generate relevant features for a specific task, which can be leveraged to generate features of the built and social environments from unstructured data in scalable ways (that is, for more environments and communities) [66][67][68][69] . Second, although social determinants have been studied extensively in the epidemiology literature, the findings from these studies have underscored the need for methods that can better capture flexible and complex relationships between social determinants and health outcomes 70,71 .…”

Section: Current Challengesmentioning

confidence: 99%