Insulin pricing and other major diabetes-related concerns in the USA: a study of 46 407 tweets between 2017 and 2019

IntroductionLittle research has been done to systematically evaluate concerns of people living with diabetes through social media, which has been a powerful tool for social change and to better understand perceptions around health-related issues. This study aims to identify key diabetes-related concerns in the USA and primary emotions associated with those concerns using information shared on Twitter.Research design and methodsA total of 11.7 million diabetes-related tweets in English were collected between April 2017 and July 2019. Machine learning methods were used to filter tweets with personal content, to geolocate (to the USA) and to identify clusters of tweets with emotional elements. A sentiment analysis was then applied to each cluster.ResultsWe identified 46 407 tweets with emotional elements in the USA from which 30 clusters were identified; 5 clusters (18% of tweets) were related to insulin pricing with both positive emotions (joy, love) referring to advocacy for affordable insulin and sadness emotions related to the frustration of insulin prices, 5 clusters (12% of tweets) to solidarity and support with a majority of joy and love emotions expressed. The most negative topics (10% of tweets) were related to diabetes distress (24% sadness, 27% anger, 21% fear elements), to diabetic and insulin shock (45% anger, 46% fear) and comorbidities (40% sadness).ConclusionsUsing social media data, we have been able to describe key diabetes-related concerns and their associated emotions. More specifically, we were able to highlight the real-world concerns of insulin pricing and its negative impact on mood. Using such data can be a useful addition to current measures that inform public decision making around topics of concern and burden among people with diabetes.

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Insulin pricing and other major diabetes-related concerns in the USA: A study of 46,407 Tweets between 2017 and 2019

10.1101/2020.01.04.20016527 ◽

2020 ◽

Author(s):

Adrian Ahne ◽

Francisco Orchard ◽

Xavier Tannier ◽

Camille Perchoux ◽

Beverley Balkau ◽

...

Keyword(s):

Social Media ◽

Online Community ◽

Positive Emotions ◽

Negative Impact ◽

Real Life ◽

Epidemiological Studies ◽

Diabetes Distress ◽

List Type ◽

New Findings ◽

The Usa

ABSTRACTIntroductionLittle research has been done to systematically evaluate concerns of people living with diabetes through social media, which has been a powerful tool for social change and to better understand perceptions around health-related issues. This study aims to identify key diabetes-related concerns in the USA and primary emotions associated with those concerns using information shared on Twitter.Research Design and MethodsA total of 11.7 million diabetes-related tweets in English were collected between April 2017 and July 2019. Machine learning methods were used to filter tweets with personal content, to geolocate (to the US) and to identify clusters of tweets with emotional elements. A sentiment analysis was then applied to each cluster.ResultsWe identified 46,407 tweets with emotional elements in the USA from which 30 clusters were identified; five clusters (18% of tweets) were related to insulin pricing with both positive emotions (joy, love) referring to advocacy for affordable insulin and sadness emotions related to the frustration of insulin prices, five clusters (12% of tweets) to solidarity and support with a majority of joy and love emotions expressed. The most negative topics (10% of tweets) were related to diabetes distress (24% sadness, 27% anger, 21% fear elements), to diabetic and insulin shock (45% anger, 46% fear) and comorbidities (40% sadness).ConclusionsUsing social media data, we have been able to describe key diabetes-related concerns and their associated emotions. More specifically, we were able to highlight the real-world concerns of insulin pricing and its negative impact on mood. Using such data can be a useful addition to current measures that inform public decision making around topics of concern and burden among people with diabetes.Significance of StudyWhat is already known about this subject?It is very challenging to collect representative data at a population level to understand what are the key concerns of people with diabetes in real life.Social media platforms, such as Twitter, may serve as a relevant source of information to supplement traditional population health studies.There are worldwide inequalities in access to insulin.What are the new findings?With 18% of the tweets related to insulin pricing, this is a major concern in the diabetes community in the USA.People regularly express fear, anger and sadness about potential diabetes-related complications and comorbidities.However, there is a lot of support and solidarity among the diabetes online community, with numerous posts related to positive emotionsHow might these results change the focus of research or clinical practice?Our work presents a reproducible approach to easily capture information about key diabetes-related concerns, that is usually not available in typical clinical or epidemiological studies. This information can supplement data from clinical or epidemiological studies to inform public health strategies to deal with diabetes-related prevention, management and treatment

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Mining Social Media Data for Biomedical Signals and Health-Related Behavior

Annual Review of Biomedical Data Science ◽

10.1146/annurev-biodatasci-030320-040844 ◽

2020 ◽

Vol 3 (1) ◽

pp. 433-458 ◽

Cited By ~ 1

Author(s):

Rion Brattig Correia ◽

Ian B. Wood ◽

Johan Bollen ◽

Luis M. Rocha

Keyword(s):

Mental Health ◽

Social Media ◽

Population Level ◽

Data Access ◽

Health Conditions ◽

Social Phenomena ◽

Medical Treatments ◽

Social Media Data ◽

Health Related ◽

Media Data

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

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Comprehensive scoping review of health research using social media data

BMJ Open ◽

10.1136/bmjopen-2018-022931 ◽

2018 ◽

Vol 8 (12) ◽

pp. e022931 ◽

Cited By ~ 3

Author(s):

Joanna Taylor ◽

Claudia Pagliari

Keyword(s):

Public Health ◽

Social Media ◽

Scoping Review ◽

Ethical Issues ◽

Social Media Data ◽

Related Research ◽

Health Related Research ◽

Health Related ◽

Google Search ◽

Media Data

IntroductionThe rising popularity of social media, since their inception around 20 years ago, has been echoed in the growth of health-related research using data derived from them. This has created a demand for literature reviews to synthesise this emerging evidence base and inform future activities. Existing reviews tend to be narrow in scope, with limited consideration of the different types of data, analytical methods and ethical issues involved. There has also been a tendency for research to be siloed within different academic communities (eg, computer science, public health), hindering knowledge translation. To address these limitations, we will undertake a comprehensive scoping review, to systematically capture the broad corpus of published, health-related research based on social media data. Here, we present the review protocol and the pilot analyses used to inform it.MethodsA version of Arksey and O’Malley’s five-stage scoping review framework will be followed: (1) identifying the research question; (2) identifying the relevant literature; (3) selecting the studies; (4) charting the data and (5) collating, summarising and reporting the results. To inform the search strategy, we developed an inclusive list of keyword combinations related to social media, health and relevant methodologies. The frequency and variability of terms were charted over time and cross referenced with significant events, such as the advent of Twitter. Five leading health, informatics, business and cross-disciplinary databases will be searched: PubMed, Scopus, Association of Computer Machinery, Institute of Electrical and Electronics Engineers and Applied Social Sciences Index and Abstracts, alongside the Google search engine. There will be no restriction by date.Ethics and disseminationThe review focuses on published research in the public domain therefore no ethics approval is required. The completed review will be submitted for publication to a peer-reviewed, interdisciplinary open access journal, and conferences on public health and digital research.

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From FAIR data to fair data use: Methodological data fairness in health-related social media research

Big Data & Society ◽

10.1177/20539517211010310 ◽

2021 ◽

Vol 8 (1) ◽

pp. 205395172110103

Author(s):

Sabina Leonelli ◽

Rebecca Lovell ◽

Benedict W Wheeler ◽

Lora Fleming ◽

Hywel Williams

Keyword(s):

Social Media ◽

Well Being ◽

Research Process ◽

Future Research ◽

Social Media Data ◽

Data Practices ◽

Health Related Research ◽

Health Related ◽

Use Of Social Media ◽

Media Data

The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced.

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Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing (Preprint)

10.2196/preprints.22734 ◽

2020 ◽

Author(s):

Oladapo Oyebode ◽

Chinenye Ndulue ◽

Ashfaq Adib ◽

Dinesh Mulchandani ◽

Banuchitra Suruliraj ◽

...

Keyword(s):

Social Media ◽

Natural Language Processing ◽

Language Processing ◽

Social Issues ◽

Public Perceptions ◽

Psychosocial Issues ◽

Social Media Data ◽

Health Related ◽

The Impact ◽

Media Data

BACKGROUND The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. OBJECTIVE This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data. METHODS We apply natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collect over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we perform data preprocessing which involves applying NLP techniques to clean and prepare the data for automated theme extraction. Third, we apply context-aware NLP approach to extract meaningful keyphrases or themes from over 1 million randomly-selected comments, as well as compute sentiment scores for each theme and assign sentiment polarity (i.e., positive, negative, or neutral) based on the scores using lexicon-based technique. Fourth, we categorize related themes into broader themes. RESULTS A total of 34 negative themes emerged, out of which 15 are health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues are increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues include frustrations due to life disruptions, panic shopping, and expression of fear. Social issues include harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes include public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. CONCLUSIONS We uncover various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommend interventions that can help address the health, psychosocial, and social issues based on the positive themes and other remedial ideas rooted in research. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, as well as in reacting to any future pandemics.

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Health-related hypothesis generation using social media data

Social Network Analysis and Mining ◽

10.1007/s13278-014-0239-8 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 10

Author(s):

Jon Parker ◽

Andrew Yates ◽

Nazli Goharian ◽

Ophir Frieder

Keyword(s):

Social Media ◽

Hypothesis Generation ◽

Social Media Data ◽

Health Related ◽

Media Data

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Developing a standardized protocol for computational sentiment analysis research using health-related social media data

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa298 ◽

2020 ◽

Author(s):

Lu He ◽

Tingjue Yin ◽

Zhaoxian Hu ◽

Yunan Chen ◽

David A Hanauer ◽

...

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Research Design ◽

Careful Consideration ◽

Analysis Tool ◽

Systematic Analysis ◽

Social Media Data ◽

Standardized Protocol ◽

Health Related ◽

Media Data

Abstract Objective Sentiment analysis is a popular tool for analyzing health-related social media content. However, existing studies exhibit numerous methodological issues and inconsistencies with respect to research design and results reporting, which could lead to biased data, imprecise or incorrect conclusions, or incomparable results across studies. This article reports a systematic analysis of the literature with respect to such issues. The objective was to develop a standardized protocol for improving the research validity and comparability of results in future relevant studies. Materials and Methods We developed the Protocol of Analysis of senTiment in Health (PATH) based on a systematic review that analyzed common research design choices and how such choices were made, or reported, among eligible studies published 2010-2019. Results Of 409 articles screened, 89 met the inclusion criteria. A total of 16 distinctive research design choices were identified, 9 of which have significant methodological or reporting inconsistencies among the articles reviewed, ranging from how relevance of study data was determined to how the sentiment analysis tool selected was validated. Based on this result, we developed the PATH protocol that encompasses all these distinctive design choices and highlights the ones for which careful consideration and detailed reporting are particularly warranted. Conclusions A substantial degree of methodological and reporting inconsistencies exist in the extant literature that applied sentiment analysis to analyzing health-related social media data. The PATH protocol developed through this research may contribute to mitigating such issues in future relevant studies.

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Automating Dynamic Consent Decisions for the Processing of Social Media Data in Health Research

Journal of Empirical Research on Human Research Ethics ◽

10.1177/1556264619883715 ◽

2019 ◽

Vol 15 (3) ◽

pp. 187-201

Author(s):

Chris Norval ◽

Tristan Henderson

Keyword(s):

Social Media ◽

Health Research ◽

User Study ◽

Machine Learning Algorithms ◽

Middle Ground ◽

Social Media Data ◽

Dynamic Consent ◽

Significant Burden ◽

Health Related ◽

Media Data

Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a “middle-ground” approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this article, we present an exploratory user study ( n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimizing undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.

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Network based model of social media big data predicts contagious disease diffusion

Information Discovery and Delivery ◽

10.1108/idd-05-2017-0046 ◽

2017 ◽

Vol 45 (3) ◽

pp. 110-120 ◽

Cited By ~ 4

Author(s):

Lauren S. Elkin ◽

Kamil Topal ◽

Gurkan Bebek

Keyword(s):

Social Media ◽

Big Data ◽

Disease Outbreaks ◽

Social Media Data ◽

Content Type ◽

Contagious Diseases ◽

The Social ◽

The Usa ◽

Disease Diffusion ◽

Media Data

Purpose Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided the ability to track patterns and trends across the world. This study aims to analyze social media micro-blogs and geographical locations to understand how disease outbreaks spread over geographies and to enhance forecasting of future disease outbreaks. Design/methodology/approach In this paper, the authors use Twitter data as the social media data source, influenza-like illnesses (ILI) as disease epidemic and states in the USA as geographical locations. They present a novel network-based model to make predictions about the spread of diseases a week in advance utilizing social media big data. Findings The authors showed that flu-related tweets align well with ILI data from the Centers for Disease Control and Prevention (CDC) (p < 0.049). The authors compared this model to earlier approaches that utilized airline traffic, and showed that ILI activity estimates of their model were more accurate. They also found that their disease diffusion model yielded accurate predictions for upcoming ILI activity (p < 0.04), and they predicted the diffusion of flu across states based on geographical surroundings at 76 per cent accuracy. The equations and procedures can be translated to apply to any social media data, other contagious diseases and geographies to mine large data sets. Originality/value First, while extensive work has been presented utilizing time-series analysis on single geographies, or post-analysis of highly contagious diseases, no previous work has provided a generalized solution to identify how contagious diseases diffuse across geographies, such as states in the USA. Secondly, due to nature of the social media data, various statistical models have been extensively used to address these problems.

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