How Twitter Is Studied in the Medical Professions: A Classification of Twitter Papers Indexed in PubMed

Evaluation of Korean-Language COVID-19-Related Medical Information on YouTube: Cross-Sectional Infodemiology Study

Background

In South Korea, the number of coronavirus disease (COVID-19) cases has declined rapidly and much sooner than in other countries. South Korea is one of the most digitalized countries in the world, and YouTube may have served as a rapid delivery mechanism for increasing public awareness of COVID-19. Thus, the platform may have helped the South Korean public fight the spread of the disease.

Objective

The aim of this study is to compare the reliability, overall quality, title–content consistency, and content coverage of Korean-language YouTube videos on COVID-19, which have been uploaded by different sources.

Methods

A total of 200 of the most viewed YouTube videos from January 1, 2020, to April 30, 2020, were screened, searching in Korean for the terms “Coronavirus,” “COVID,” “Corona,” “Wuhan virus,” and “Wuhan pneumonia.” Non-Korean videos and videos that were duplicated, irrelevant, or livestreamed were excluded. Source and video metrics were collected. The videos were scored based on the following criteria: modified DISCERN index, Journal of the American Medical Association Score (JAMAS) benchmark criteria, global quality score (GQS), title–content consistency index (TCCI), and medical information and content index (MICI).

Results

Of the 105 total videos, 37.14% (39/105) contained misleading information; independent user–generated videos showed the highest proportion of misleading information at 68.09% (32/47), while all of the government-generated videos were useful. Government agency–generated videos achieved the highest median score of DISCERN (5.0, IQR 5.0-5.0), JAMAS (4.0, IQR 4.0-4.0), GQS (4.0, IQR 3.0-4.5), and TCCI (5.0, IQR 5.0-5.0), while independent user–generated videos achieved the lowest median score of DISCERN (2.0, IQR 1.0-3.0), JAMAS (2.0, IQR 1.5-2.0), GQS (2.0, IQR 1.5-2.0), and TCCI (3.0, IQR 3.0-4.0). However, the total MICI was not significantly different among sources. “Transmission and precautionary measures” were the most commonly covered content by government agencies, news agencies, and independent users. In contrast, the most mentioned content by news agencies was “prevalence,” followed by “transmission and precautionary measures.”

Conclusions

Misleading videos had more likes, fewer comments, and longer running times than useful videos. Korean-language YouTube videos on COVID-19 uploaded by different sources varied significantly in terms of reliability, overall quality, and title–content consistency, but the content coverage was not significantly different. Government-generated videos had higher reliability, overall quality, and title–content consistency than independent user–generated videos.

A Bibliometric Overview of Twitter-Related Studies Indexed in Web of Science

Twitter has been one of the most popular social network sites for academic research; the main objective of this study was to update the current knowledge boundary surrounding Twitter-related investigations and, further, identify the major research topics and analyze their evolution across time. A bibliometric analysis has been applied in this article: we retrieved 19,205 Twitter-related academic articles from Web of Science after several steps of data cleaning and preparation. The R package “Bibliometrix” was mainly used in analyzing this content. Our study has two sections, and performance analysis contains 5 categories (Annual Scientific Production, Most Relevant Sources, Most Productive Authors, Most Cited Publications, Most Relevant Keywords.). The science mapping included country collaboration analysis and thematic analysis. We highlight our thematic analysis by splitting the whole bibliographic dataset into three temporal periods, thus a thematic evolution across time has been presented. This study is one of the most comprehensive bibliometric overview in analyzing Twitter-related studies by far. We proceed to explain how the results will benefit the understanding of current academic research interests on the social media giant.

Using social media to monitor mental health discussions - evidence from Twitter

Objectives

Given the public health importance of communicating about mental illness and the growing use of social media to convey information, our goal was to develop an empirical model to identify periods of heightened interest in mental health topics on Twitter.

Materials and Methods

We collected data on 176 million tweets from 2011 to 2014 with content related to depression or suicide. Using an autoregressive integrated moving average (ARIMA) data analysis, we identified deviations from predicted trends in communication about depression and suicide.

Results

Two types of heightened Twitter activity regarding depression or suicide were identified in 2014: expected increases in response to planned behavioral health events, and unexpected increases in response to unanticipated events. Tweet volume following expected increases went back to the predicted level more rapidly than the volume following unexpected events.

Discussion

Although ARIMA models have been used extensively in other fields, they have not been used widely in public health. Our findings indicate that our ARIMA model is valid for identifying periods of heightened activity on Twitter related to behavioral health. The model offers an objective and empirically based measure to identify periods of greater interest for timing the dissemination of credible information related to mental health.

Conclusion

Spikes in tweet volume following a behavioral health event often last for less than 2 days. Individuals and organizations that want to disseminate behavioral health messages on Twitter in response to heightened periods of interest need to take this limited time frame into account.

The Utility of Social Media in Providing Information on Zika Virus

Introduction

In 2015, there was an outbreak of Zika virus in Brazil that spread throughout the Americas. The association of Zika virus with birth defects in infants born to infected pregnant women created concern for women of childbearing age. Social media is an important platform for health promotion, communication, and education on preventative methods during Zika virus outbreaks.

Methods

We evaluated the utility of social media on providing information regarding Zika virus. Facebook, Instagram, Twitter, and YouTube were utilized for our study. A search of the term “#Zikavirus” on Twitter and Instagram, and “Zika virus” on Facebook and YouTube was performed. The first 50 search results were analyzed from each source. Only English, Spanish, or Portuguese results were included. Results were categorized into three groups: “Useful”, “Not Useful”, or “Misleading”.

Results

Search was conducted on December 17th, 2016, with 185 results. Forty (21.6%) were from Facebook, 50 (27%) from Twitter, 48 (25.9%) from YouTube, and 47 (25.4%) from Instagram. A total of 104 (56.22%) results were "Useful", 67 (36.2%) "Not Useful", and 14 (7.5%) were "Misleading”. There were significantly more “Useful” results compared to “Not Useful” and “Misleading” results (Fisher’s exact: p < 0.0001).

Conclusion

Social media is a useful resource for providing relevant information on Zika virus. Young women can utilize social media for Zika virus information. The role of social media in public health should be further investigated and established. Patient education interventions should focus on social media impact on behavior modification and education of public to recognize useful information.

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations

BACKGROUND

Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner.

OBJECTIVE

This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed.

METHODS

We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines.

RESULTS

The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes.

CONCLUSIONS

We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP.

Twitter use in physics conferences

An analysis of Twitter use in 116 conferences suggests that the service is used more extensively at PACS10 conferences (those devoted to the physics of elementary particles and fields) and PACS90 conferences (those devoted to geophysics, astronomy, and astrophysics) than at conferences in other fields of physics. Furthermore, Twitter is used in a qualitatively different manner. A possible reason for these differences is discussed.

Tweeting back: predicting new cases of back pain with mass social media data

BACKGROUND

Back pain is a global health problem. Recent research has shown that risk factors that are proximal to the onset of back pain might be important targets for preventive interventions. Rapid communication through social media might be useful for delivering timely interventions that target proximal risk factors. Identifying individuals who are likely to discuss back pain on Twitter could provide useful information to guide online interventions.

METHODS

We used a case-crossover study design for a sample of 742 028 tweets about back pain to quantify the risks associated with a new tweet about back pain.

RESULTS

The odds of tweeting about back pain just after tweeting about selected physical, psychological, and general health factors were 1.83 (95% confidence interval [CI], 1.80-1.85), 1.85 (95% CI: 1.83-1.88), and 1.29 (95% CI, 1.27-1.30), respectively.

CONCLUSION

These findings give directions for future research that could use social media for innovative public health interventions.

The emerging diabetes online community

BACKGROUND

Diabetes self-management is complex and demanding, and isolation and burnout are common experiences. The Internet provides opportunities for people with diabetes to connect with one another to address these challenges. The aims of this paper are to introduce readers to the platforms on which Diabetes Online Community (DOC) participants interact, to discuss reasons for and risks associated with diabetes-related online activity, and to review research related to the potential impact of DOC participation on diabetes outcomes.

METHODS

Research and online content related to diabetes online activity is reviewed, and DOC writing excerpts are used to illustrate key themes. Guidelines for meaningful participation in DOC activities for people with diabetes, families, health care providers, and industry are provided.

RESULTS

Common themes around DOC participation include peer support, advocacy, self-expression, seeking and sharing diabetes information, improving approaches to diabetes data management, and humor. Potential risks include access to misinformation and threats to individuals' privacy, though there are limited data on negative outcomes resulting from such activities. Likewise, few data are available regarding the impact of DOC involvement on glycemic outcomes, but initial research suggests a positive impact on emotional experiences, attitudes toward diabetes, and engagement in diabetes management behaviors.

CONCLUSION

The range of DOC participants, activities, and platforms is growing rapidly. The Internet provides opportunities to strengthen communication and support among individuals with diabetes, their families, health care providers, the health care industry, policy makers, and the general public. Research is needed to investigate the impact of DOC participation on self-management, quality of life, and glycemic control, and to design and evaluate strategies to maximize its positive impact.