Social Monitoring for Public Health

Paul, Michael J.; Dredze, Mark; Marchionini, Gary

doi:10.1007/978-3-031-02311-8

Cited by 29 publications

(18 citation statements)

References 487 publications

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“…Social media data have consequently become a widely used source of data for public health research [11]. In comparison with established traditional surveillance systems, social media monitoring is inexpensive and near to real time and covers large populations [12], thus offering an easily accessible wide-ranging data source for tracking emerging trends-which may be unavailable or less noticeable in data gathered by traditional reporting systems [13].…”

Section: Introductionmentioning

confidence: 99%

Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study

Habibabadi¹,

Haghighi²,

Burstein³

et al. 2022

JMIR Med Inform

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Background Traditional monitoring for adverse events following immunization (AEFI) relies on various established reporting systems, where there is inevitable lag between an AEFI occurring and its potential reporting and subsequent processing of reports. AEFI safety signal detection strives to detect AEFI as early as possible, ideally close to real time. Monitoring social media data holds promise as a resource for this. Objective The primary aim of this study is to investigate the utility of monitoring social media for gaining early insights into vaccine safety issues, by extracting vaccine adverse event mentions (VAEMs) from Twitter, using natural language processing techniques. The secondary aims are to document the natural language processing techniques used and identify the most effective of them for identifying tweets that contain VAEM, with a view to define an approach that might be applicable to other similar social media surveillance tasks. Methods A VAEM-Mine method was developed that combines topic modeling with classification techniques to extract maximal VAEM posts from a vaccine-related Twitter stream, with high degree of confidence. The approach does not require a targeted search for specific vaccine reaction–indicative words, but instead, identifies VAEM posts according to their language structure. Results The VAEM-Mine method isolated 8992 VAEMs from 811,010 vaccine-related Twitter posts and achieved an F1 score of 0.91 in the classification phase. Conclusions Social media can assist with the detection of vaccine safety signals as a valuable complementary source for monitoring mentions of vaccine adverse events. A social media–based VAEM data stream can be assessed for changes to detect possible emerging vaccine safety signals, helping to address the well-recognized limitations of passive reporting systems, including lack of timeliness and underreporting.

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Section: Introductionmentioning

confidence: 99%

Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study

Habibabadi¹,

Haghighi²,

Burstein³

et al. 2022

JMIR Med Inform

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“…Social media such as Reddit, Twitter, Facebook, Weibo, and others provide a readily available source of abundant, organic, publicly accessible first-person narratives [17][18][19][20][21][22][23][24][25], which can serve as data sets for identifying outbreaks and providing situational awareness. Even more important during the COVID-19 pandemic, social media data provide a means of better understanding public attitudes and behaviors during a crisis to support communication and health-promotion messaging, especially in situations in which survey data are not readily available [15,26].…”

Section: Introductionmentioning

confidence: 99%

Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia

Hu¹,

Conway²

2022

JMIR Infodemiology

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Background Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging. Objective The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit. Methods We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19–related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country. Results Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19–related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits. Conclusions This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches.

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“…Diets lacking in adequate nutrition, such as those high in saturated fat and sugar, are a major contributor to the leading causes of chronic diseases and death in the United States (Bauer, Briss, Goodman, & Bowman, 2014). To monitor public opinions on diet, large surveys are commonly used but are costly and time consuming (Paul & Dredze, 2017).…”

mentioning

confidence: 99%

“…As social media has gained in popularity, it has become a mainstream channel of communication. Social media provides both archival and current data, enabling researchers to monitor the presence of diseases, prevention efforts, and associated trends (Paul & Dredze, 2017). Not only does the use of social media data reduce cost and time expenditures, it also provides high rates of self-disclosure which are often underreported in DOI: 10.1002/pra2.320 83rd Annual Meeting of the Association for Information Science & Technology October 25-29, 2020.…”

mentioning

confidence: 99%

Seasonal characterization of diet discussions on Reddit

Money

Karami

Turner‐McGrievy

et al. 2020

Proc Assoc Inf Sci Technol

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To monitor public opinions on diet, large survey data is commonly used though costly and time consuming. Social media has become a mainstream channel of communication and has drastically grown in popularity, connecting users to a ready stream of health information. While the literature has provided valuable information on the use of social media for health, a broader perspective informed by different types of social media platforms would be highly beneficial. Diet has been extensively explored on a few mainstream platforms, further informing public health research. However, diet conversations on Reddit have only been studied within a narrow scope, looking at specific sub‐communities. This study aims to characterize diet‐related posts and their seasonal patterns using a mixed method approach. We collected more than 500,000 posts with subsequent comments from Reddit over the course of a year. Our findings show that Reddit users across all sub‐communities primarily discussed health promotion, fitness plans, a healthy lifestyle, diet and fitness progress, food experiences, weight loss, as well as vegan and vegetarian diets. In addition, seasonal differences based on the weight of most topics, were found to be significant (p < .05).

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Social Monitoring for Public Health

Cited by 29 publications

References 487 publications

Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study

Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study

Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia

Seasonal characterization of diet discussions on Reddit

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