Social Media Mining Toolkit (SMMT)

Tekumalla, Ramya; Banda, Juan M.

doi:10.5808/gi.2020.18.2.e16

Cited by 39 publications

(37 citation statements)

References 14 publications

(23 reference statements)

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“…The provided data set only contains the identifiers of corresponding tweets, so we used the application programming interface (API) provided by Twitter to extract the full content of each tweet. The Social Media Mining Toolkit provided by the Panacea Lab was used to hydrate the data set [ 22 ]. There is no limitation regarding the days prior to the extraction.…”

Section: Methodsmentioning

confidence: 99%

Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study

Wang¹,

Li²,

Hutch³

et al. 2021

J Med Internet Res

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Background The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. Objective This study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media. Methods We used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures. Results The machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R0 of 7.62 for trends in the number of Twitter users posting health belief–related content over the study period. The fluctuations in the number of health belief–related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians’ speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (P=.78 and P=.92 for perceived benefits and perceived barriers, respectively). Conclusions As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is “unhealthy” that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians’ speeches, might not be endorsed by substantial evidence and could sometimes be misleading.

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Section: Methodsmentioning

confidence: 99%

Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study

Wang¹,

Li²,

Hutch³

et al. 2021

J Med Internet Res

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“…Any tweets from accounts with unusually high tweeting activity (possible bots) or that only shared other tweets were removed. We then annotated tweets using the Social Media Mining Toolkit 3 , Spacy NER annotator and a dictionary created from the Observational Health Data Sciences and Informatics (OHDSI) vocabulary 4 , which allows the annotated terms to tie into clinical conditions and observations. We identified the final set of tweets with clinical concepts for review.…”

Section: Methodsmentioning

confidence: 99%

Long-term patient-reported symptoms of COVID-19: an analysis of social media data

Banda

Singh

Alser

et al. 2020

Preprint

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As the COVID-19 virus continues to infect people across the globe, there is little understanding of the long term implications for recovered patients. There have been reports of persistent symptoms after confirmed infections on patients even after three months of initial recovery. While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually not publicly available or standardized to perform longitudinal analyses on them. Therefore, there is a need to use additional data sources for continued follow-up and identification of latent symptoms that might be underreported in other places. In this work we present a preliminary characterization of post-COVID-19 symptoms using social media data from Twitter. We use a combination of natural language processing and clinician reviews to identify long term self-reported symptoms on a set of Twitter users.

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“…We utilized the largest available Covid-19 dataset (Banda et al, 2020) curated using a Social Media Mining Toolkit (SMMT) (Tekumalla and Banda, 2020b). Version 15 of the Covid-19 dataset was utilized for our experiments since it was the latest released version at the time of experiments.…”

Section: Introductionmentioning

confidence: 99%

Characterizing drug mentions in COVID-19 Twitter Chatter

Tekumalla¹,

Banda²

2020

Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

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Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommended for COVID-19, there are several drugs that can potentially help with symptoms. In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around drug mentions. While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning alongside traditional automated methods to aid in this task. By applying these complementary methods, we are able to recover almost 15% additional data, making misspelling handling a needed task as a pre-processing step when dealing with social media data.

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Social Media Mining Toolkit (SMMT)

Cited by 39 publications

References 14 publications

Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study

Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study

Long-term patient-reported symptoms of COVID-19: an analysis of social media data

Characterizing drug mentions in COVID-19 Twitter Chatter

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