scholarly journals Risk Communication in Asian Countries: COVID-19 Discourse on Twitter (Preprint)

2020 ◽  
Author(s):  
Sungkyu Park ◽  
Sungwon Han ◽  
Jeongwook Kim ◽  
Mir Majid Molaie ◽  
Hoang Dieu Vu ◽  
...  
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Risk Communication ◽  
Language Processing ◽  
World Health ◽  
The Novel ◽  
Asian Countries ◽  
Public Attention ◽  
Novel Coronavirus

BACKGROUND The novel coronavirus disease (hereafter COVID-19) caused by severe acute respiratory coronavirus 2 (SARS-CoV-2) has caused a global pandemic. During this time, a plethora of information regarding COVID-19 containing both false information (misinformation) and accurate information circulated on social media. The World Health Organization has declared a need to fight not only the pandemic but also the infodemic (a portmanteau of information and pandemic). In this context, it is critical to analyze the quality and veracity of information shared on social media and the evolution of discussions on major topics regarding COVID-19. OBJECTIVE This research characterizes risk communication patterns by analyzing public discourse on the novel coronavirus in four Asian countries that suffered outbreaks of varying degrees of severity: South Korea, Iran, Vietnam, and India. METHODS We collect tweets on COVID-19 posted from the four Asian countries from the start of their respective COVID-19 outbreaks in January until March 2020. We consult with locals and utilize relevant keywords from the local languages, following each country's tweet conventions. We then utilize a natural language processing (NLP) method to learn topics in an unsupervised fashion automatically. Finally, we qualitatively label the extracted topics to comprehend their semantic meanings. RESULTS We find that the official phases of the epidemic, as announced by the governments of the studied countries, do not align well with the online attention paid to COVID-19. Motivated by this misalignment, we develop a new natural language processing method to identify the transitions in topic phases and compare the identified topics across the four Asian countries. We examine the time lag between social media attention and confirmed patient counts. We confirm an inverse relationship between the tweet count and topic diversity. CONCLUSIONS Through the current research, we observe similarities and differences in the social media discourse on the pandemic in different Asian countries. We observe that once the daily tweet count hits its peak, the successive tweet count trend tends to decrease for all countries. This phenomenon aligns with the dynamics of the issue-attention cycle, an existing construct from communication theory conceptualizing how an issue rises and falls from public attention. Little work has been performed to identify topics in online risk communication by collectively considering temporal tweet trends in different countries. In this regard, if a critical piece of misinformation can be detected at an early stage in one country, it can be reported to prevent the spread of misinformation in other countries. Therefore, this work can help social media services, social media communicators, journalists, policymakers, and medical professionals fight the infodemic on a global scale. CLINICALTRIAL N/A

scholarly journals (Preprint)

2021 ◽  
Author(s):  
Sungkyu Park ◽  
Sungwon Han ◽  
Jeongwook Kim ◽  
Mir Majid Molaie ◽  
Hoang Dieu Vu ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Public Discourse ◽  
World Health ◽  
Asian Countries ◽  
Public Attention ◽  
Data Set ◽  
The Public ◽  
Issue Attention

BACKGROUND COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. OBJECTIVE This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. METHODS We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time–topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. RESULTS This research found that each government’s official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. CONCLUSIONS This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic.

scholarly journals A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness (Preprint)

2021 ◽  
Author(s):  
Vishal Dey ◽  
Peter Krasniak ◽  
Minh Nguyen ◽  
Clara Lee ◽  
Xia Ning
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Breast Implant ◽  
Public Attention ◽  
Social Media Data ◽  
Media Data

BACKGROUND A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. OBJECTIVE The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. METHODS We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. RESULTS Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. CONCLUSIONS Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. CLINICALTRIAL

scholarly journals Natural Language Processing of Social Media as Screening for Suicide Risk

2018 ◽  
Vol 10 ◽  
pp. 117822261879286 ◽  
Author(s):  
Glen Coppersmith ◽  
Ryan Leary ◽  
Patrick Crutchley ◽  
Alex Fine
Keyword(s):  
At Risk ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Suicide Risk ◽  
Automated System ◽  
World Health ◽  
Primary Care Doctor ◽  
Trade Off

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people’s lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media—and the near-ubiquity of mobile devices used to access social media networks—offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have “opted in” for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially identifying many people who are at risk preventively and prior to any engagement with a health care system. This raises a significant cultural question about the trade-off between privacy and prevention—we have potentially life-saving technology that is currently reaching only a fraction of the possible people at risk because of respect for their privacy. Is the current trade-off between privacy and prevention the right one?

scholarly journals Trend of Social Media News: A Viewpoint of COVID-19 Tweets Using Natural Language Processing

2021 ◽  
Author(s):  
AISDL
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Developed Countries ◽  
World Health ◽  
The World ◽  
Twitter Users ◽  
The Developed Countries ◽  
Health Organization

The meteoric rise of social media news during the ongoing COVID-19 is worthy of advanced research. Freedom of speech in many parts of the world, especially the developed countries and liberty of socialization, calls for noteworthy information sharing during the panic pandemic. However, as a communication intervention during crises in the past, social media use is remarkable; the Tweets generated via Twitter during the ongoing COVID-19 is incomparable with the former records. This study examines social media news trends and compares the Tweets on COVID-19 as a corpus from Twitter. By deploying Natural Language Processing (NLP) methods on tweets, we were able to extract and quantify the similarities between some tweets over time, which means that some people say the same thing about the pandemic while other Twitter users view it differently. The tools we used are Spacy, Networkx, WordCloud, and Re. This study contributes to the social media literature by understanding the similarity and divergence of COVID-19 tweets of the public and health agencies such as the World Health Organization (WHO). The study also sheds more light on the COVID-19 sparse and densely text network and their implications for the policymakers. The study explained the limitations and proposed future studies.

scholarly journals A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness

10.2196/29768 ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. e29768
Author(s):  
Vishal Dey ◽  
Peter Krasniak ◽  
Minh Nguyen ◽  
Clara Lee ◽  
Xia Ning
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Breast Implant ◽  
Public Attention ◽  
Social Media Data ◽  
Media Data

Background A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. Objective The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. Methods We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. Results Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. Conclusions Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses.

scholarly journals Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Experience ◽  
Language Processing ◽  
Performance Metrics ◽  
Free Text ◽  
Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

Using Natural Language Processing to Assess the Psychological Effect of COVID-19 Pandemic on Insomnia via Tweets: A Pre-Post Retrospective Pilot Study (Preprint)

2021 ◽  
Author(s):  
Arash Maghsoudi ◽  
Sara Nowakowski ◽  
Ritwick Agrawal ◽  
Amir Sharafkhaneh ◽  
Sadaf Aram ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Temporal Analysis ◽  
Health Condition ◽  
Additional Stress ◽  
Mental Health Condition ◽  
Twitter Users

BACKGROUND The COVID-19 pandemic has imposed additional stress on population health that may result in a higher incidence of insomnia. In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. OBJECTIVE In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. METHODS We designed a pre-post retrospective study using public social media content from Twitter. We categorized tweets based on time into two intervals: prepandemic (01/01/2019 to 01/01/2020) and pandemic (01/01/2020 to 01/01/2021). We used NLP to analyze polarity (positive/negative) and intensity of emotions and also users’ tweets psychological states in terms of sadness, anxiety and anger by counting the words related to these categories in each tweet. Additionally, we performed temporal analysis to examine the effect of time on the users’ insomnia experience. RESULTS We extracted 268,803 tweets containing the word insomnia (prepandemic, 123,293 and pandemic, 145,510). The odds of negative tweets (OR, 1.31; 95% CI, 1.29-1.33), anger (OR, 1.19; 95% CI, 1.16-1.21), and anxiety (OR, 1.24; 95% CI: 1.21-1.26) were higher during the pandemic compared to prepandemic. The likelihood of negative tweets after midnight was higher than for other daily intevals, comprising approximately 60% of all negative insomnia-related tweets in 2020 and 2021 collectively. CONCLUSIONS Twitter users shared more negative tweets about insomnia during the pandemic than during the year before. Also, more anger and anxiety-related content were disseminated during the pandemic on the social media platform. Future studies using an NLP framework could assess tweets about other psychological distress, habit changes, weight gain due to inactivity, and the effect of viral infection on sleep.

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