Communicating diabetes and diets on Twitter – a semantic content analysis

This paper analyses: 1) the semantic content of tweets discussing diabetes and diets: 2) the conversational connections of those tweeting and those being mentioned in the tweets. The content analysis of the tweets aims at mapping what kinds of diets are mentioned in conversations about diabetes and in what context. Our data consists of 9,042 tweets containing the words &lsquo;diabetes&rsquo; and &lsquo;diet&rsquo;. The findings indicate that analysing Twitter conversations can be a fruitful and an efficient way to map public opinions about diabetes and diets, as well as other medical issues that concern many people. The results also showed that many private persons act as diabetes advocates spreading information and news about diabetes and diets. Surveying these topics can be useful for healthcare practitioners; as these are in contact with patients with diabetes, it is important that they are aware of both the most discussed topics and the most common information sources, who are often laymen.</div

Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.future.2020.04.025.Supplementary material 1: this file contains the 23 user communities detected using the GLay algorithm.In recent years, the number of active users in social media has grown exponentially. Despite the thematic diversity of the messages, social media have become an important vehicle to disseminate health information as well as to gather insights about patients experiences and emotional intelligence. Therefore, the present work proposes a new methodology of analysis to identify and interpret the behaviour, perceptions and appreciations of patients and close relatives towards a health condition through their social interactions. At the core of this methodology are techniques of natural language processing and machine learning as well as the reconstruction of knowledge graphs, and further graph mining. The case study is the diabetes community, and more specifically, the patients communicating about type 1 diabetes (T1D) and type 2 diabetes (T2D). The results produced in this study show the effectiveness of the proposed method to discover useful and non-trivial knowledge about patient perceptions of disease. Such knowledge may be used in the context of Health Informatics to promote healthy lifestyles in more efficient ways as well as to improve communication with the patients.This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684), the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019–2022) and the European Union (European Regional Development Fund - ERDF)- Ref. ED431G2019/06, and Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. The authors also acknowledge the Postdoc contract of Martín Pérez-Pérez, funded by the Xunta de Galicia.info:eu-repo/semantics/publishedVersio

Topic modeling and user network analysis on twitter during world lupus awareness day

Twitter is increasingly used by individuals and organizations to broadcast their feelings and practices, providing access to samples of spontaneously expressed opinions on all sorts of themes. Social media offers an additional source of data to unlock information supporting new insights disclosures, particularly for public health purposes. Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease that remains a major challenge in therapeutic diagnostic and treatment management. When supporting patients with such a complex disease, sharing information through social media can play an important role in creating better healthcare services. This study explores the nature of topics posted by users and organizations on Twitter during world Lupus day to extract latent topics that occur in tweet texts and to identify what information is most commonly discussed among users. We identified online influencers and opinion leaders who discussed different topics. During this analysis, we found two different types of influencers that employed different narratives about the communities they belong to. Therefore, this study identifies hidden information for healthcare decision-makers and provides a detailed model of the implications for healthcare organizations to detect, understand, and define hidden content behind large collections of text

Mining Social Media to Understand Consumers' Health Concerns and the Public's Opinion on Controversial Health Topics.

Social media websites are increasingly used by the general public as a venue to express health concerns and discuss controversial medical and public health issues. This information could be utilized for the purposes of public health surveillance as well as solicitation of public opinions. In this thesis, I developed methods to extract health-related information from multiple sources of social media data, and conducted studies to generate insights from the extracted information using text-mining techniques. To understand the availability and characteristics of health-related information in social media, I first identified the users who seek health information online and participate in online health community, and analyzed their motivations and behavior by two case studies of user-created groups on MedHelp and a diabetes online community on Twitter. Through a review of tweets mentioning eye-related medical concepts identified by MetaMap, I diagnosed the common reasons of tweets mislabeled by natural language processing tools tuned for biomedical texts, and trained a classifier to exclude non medically-relevant tweets to increase the precision of the extracted data. Furthermore, I conducted two studies to evaluate the effectiveness of understanding public opinions on controversial medical and public health issues from social media information using text-mining techniques. The first study applied topic modeling and text summarization to automatically distill users' key concerns about the purported link between autism and vaccines. The outputs of two methods cover most of the public concerns of MMR vaccines reported in previous survey studies. In the second study, I estimated the public's view on the ac{ACA} by applying sentiment analysis to four years of Twitter data, and demonstrated that the the rates of positive/negative responses measured by tweet sentiment are in general agreement with the results of Kaiser Family Foundation Poll. Finally, I designed and implemented a system which can automatically collect and analyze online news comments to help researchers, public health workers, and policy makers to better monitor and understand the public's opinion on issues such as controversial health-related topics.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120714/1/owenliu_1.pd

Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter

: Over 2.8 million people die each year from being overweight or obese, a largely preventable disease. Social media has fundamentally changed the way we communicate, collaborate, consume, and create content. The ease with which content can be shared has resulted in a rapid increase in the number of individuals or organisations that seek to influence opinion and the volume of content that they generate. The nutrition and diet domain is not immune to this phenomenon. Unfortunately, from a public health perspective, many of these ‘influencers’ may be poorly qualified in order to provide nutritional or dietary guidance, and advice given may be without accepted scientific evidence and contrary to public health policy. In this preliminary study, we analyse the ‘healthy diet’ discourse on Twitter. While using a multi-component analytical approach, we analyse more than 1.2 million English language tweets over a 16-month period in order to identify and characterise the influential actors and discover topics of interest in the discourse. Our analysis suggests that the discourse is dominated by non-health professionals. There is widespread use of bots that pollute the discourse and seek to create a false equivalence on the efficacy of a particular nutritional strategy or diet. Topic modelling suggests a significant focus on diet, nutrition, exercise, weight, disease, and quality of life. Public health policy makers and professional nutritionists need to consider what interventions can be taken in order to counteract the influence of non-professional and bad actors on social media

A Mixed-Method Examination of Primary Care Physician Message Strategies to Correct Patient-Held Health Misinformation: An Application of Goals-Plans-Action Theory

Given the prevalence of health misinformation (i.e., inaccurate health messaging that lacks scientific evidence), there is a need for successful communication strategies to combat this detrimental health issue (Krishna & Thompson, 2021). Guided by goals-plans-action theory (Dillard, 1990), which explains the communicative process of creating and implementing influence messages, the purpose of this dissertation was to: (a) uncover primary care physician goals, plans, and action when correcting patient-held health misinformation and (b) experimentally test corrective influence messages for their effectiveness from the patient’s perspective. Two studies addressed these two purposes. In Study One, results of surveys of primary care physicians (N = 105) discovered significant, positive relationships between their primary goal (i.e., correction of health misinformation) and the secondary goals of identity and conversation management. Additionally, Study One results revealed five types of primary care physician strategic message plans during these conversations (i.e., vocalics, clarity, body positioning, listening behavior, relationship-building tone), and five themes for communicative action strategies that primary care physicians use when correcting patient-held health misinformation (i.e., scientific evidence-based explication, recommendations for evaluating health-related information and sources, emotional and/or relationship-building appeal, simple correction, disregard/judgment). Scenario-based corrective influence messaging was created based on communicative action themes from Study One (i.e., scientific evidence, evaluation recommendation, emotional appeal), checked for validity, and pilot tested. In Study Two, U.S. IX adults ages 18 years and older (N = 371) were asked to imagine they have found information online saying vaccines contain toxic ingredients and decide to bring this information up to their primary care physician, were randomly assigned to read a scenario from one of these three corrective influence messaging themes, and then reported their perceptions of the primary care physician. Results revealed no significant differences between scientific evidence and emotional appeal messages on key patient outcomes including perceived source credibility, patient satisfaction, intent to communicate with and share online health information to a primary care physician. Results of the two studies provide evidence for the applicability of goals-plans-action theory to the context of health misinformation and corrective influence messages, and yield recommendations for primary care physicians to implement when correcting health misinformation

Social media narratives in non-communicable disease: their dynamics and value for patients, communities and health researchers

Background: Usage of social media is now widespread and growing, as is the number of people living with Non-Communicable Diseases (NCDs) such as diabetes and cancer. This thesis examines how social media are being used to share or discuss NCDs and the benefits, challenges and implications of these trends as a manifestation of digital public health. Aim and research questions: The aim of this research is to address the gap in empirical, evidence-based research into the secondary use of data from social media to understand patient health issues and inform public health research into NCDs. To this end, seven research questions, each linked to a sub-project, were defined and tested during the course of the six-year programme: 1.What is the status of the existing multi-disciplinary research literature based on analysis of data posted on social media for public health research, and where are the gaps in this research? 2.Can existing systematic review methods be re-purposed and applied to analyse data posted on social media? 3.How are research sponsors and researchers addressing the ethical challenges of analysing data posted on social media? 4.To what extent are diabetes-related posts on Twitter relevant to the clinical condition and what topics and intentions are represented in these posts? 5.In what ways do people affected by Type 1 diabetes use different social media (e.g. for social interaction, support-seeking, information-sharing) and what are the implications for researchers wishing to use these data sources in their studies? 6.Are these differences in platform usage and associated data types also seen in people affected by lung cancer? 7.Can characteristic illness trajectories be seen in a cancer patient’s digital narrative and what insights can be gained to inform palliative care services? Methods: A range of different qualitative and quantitative methods and frameworks were used to address each of the research questions listed. Arksey and O’Malley’s five-stage scoping review framework and the PRISMA guidelines are applied to the systematic scoping review of existing literature. The PRISMA guidelines and checklist are re-purposed and applied to the manual extraction and analysis of social media posts. Bjerglund-Andersen and Söderqvist’s typology of social media uses in research and Conway’s taxonomy of ethical considerations are used to classify the ethics guidelines available to researchers. The findings of these were used to inform the research design of the four empirical studies. The methods applied in the conduct of the empirical studies include a content and narrative analysis of cross-sectional and longitudinal data sourced from Twitter, Facebook, the Type 1 diabetes discussion forum on Diabetes.co.uk and the lung cancer discussion forum on Macmillan.org.uk, as well as the application of Bales’ Interaction Process Analysis and Emanuel and Emanuel’s framework for a good death. Results : Of the 49 systematic, quasi-systematic and scoping reviews identified, 24 relate to the secondary use of data from social media, with eight of these focused on infectious disease surveillance and only two on NCDs. Existing reviews tend to be fragmented, narrow in scope and siloed in different academic communities, with limited consideration of the different types of data, analytical methods and ethical issues involved, therefore creating a need for further reviews to synthesise the emerging evidence-base. The rapid increase in the volume of published research is evident, from the results of RQ1, with 87% of the eligible studies published between 2013-2017. Of the 105 eligible empirical studies that focused on NCDs, cancer (54%) and diabetes (20%) dominate the literature. Data is sourced from Twitter (26%), Facebook (14%) and blogs (10%), conducted, published and funded by the medical community. Since 2012, automated methods have increasingly been applied to extract and analyse large volumes of data. Those that use manual methods for extraction did not apply a consistent approach to doing so; the PRISMA guidelines and checklist were therefore re-purposed and applied to analyse data extracted from social media in response to RQ2. The deficit of ethical guidance available to inform research that involves social media data was also identified as a result of RQ3 and the guidelines provided by the ESRC, BPS, AoIR and NIHR were prioritised for the purposes of this research project. Results from the four empirical studies (RQ4-7) reveal that different forms of social interaction and support are represented in the variety of social media platforms available and that this is influenced by the type and nature of the condition with which people are affected, as well as the affordances offered by such platforms. In the pilot study associated with RQ4, Twitter was identified as a ‘noisy’ source of data about diabetes, with only 66% of the sample being relevant to the clinical condition. Twelve per cent of the eligible sample was associated with Type 2 diabetes, compared to 6% for Type 1, and most were information-giving in nature (49%) and correlated with the diagnosis, treatment and management of the condition (44%). A comparison of Twitter to the Type 1 Diabetes community on Facebook and the discussion forum on Diabetes.co.uk for RQ5 indicated that all three social media platforms were used to disseminate information about the condition. However, the Type 1 Diabetes Group on Facebook and the Type 1 discussion forum on Diabetes.co.uk were also used for social interaction and peer support, hence defying the generalisations made in public health studies, where social media platforms were often considered equal or synonymous. The results from the third empirical study into lung cancer (RQ6) support this, indicating that, by virtue of their digital architecture, user base and self-moderating communities, the Lung Cancer Support Group on Facebook and the lung cancer discussion forum on Macmillan.org.uk are more successful in their utility for social interaction and emotional and informational support. Meanwhile, the sample derived from Twitter hashtags showed greater companionship support. The final empirical study in this PhD research project is associated with RQ7 and used longitudinal data posted by a terminally ill patient on Twitter. This revealed that patient activity on social media mirrors the different phases of the end-of-life illness trajectory described in the literature and that it is comparable to or compliments insights garnered using more traditional qualitative research techniques. It also shows the value of such innovative methods for understanding how terminal disease is experienced by and affects individuals, how they cope, how support is sought and obtained and how patients feel about the ability of palliative care services to meet their needs at different stages. Conclusions: The analysis of health data posted on social media continues to be an expanding and evolving field of multi-disciplinary research. The results of the studies included in this thesis reveal the emergence of new methods and ethical considerations to inform research design as well as ethics policy. The re-purposed PRISMA guidelines and checklist were presented at the 2014 Medicine 2.0 Summit and World Congress whilst the review of ethical guidelines was published in the Research Ethics journal. The four empirical studies that extracted and analysed data from social media provide novel insight into the social narratives of those impacted by diabetes and cancer and can be used to inform future research and practice. The results of these studies have, to date, been presented at four international conferences and published in npj Digital Medicine and BMC Palliative Care. Although this thesis and associated publications contribute to an emerging body of knowledge, further research is warranted into the manual versus automated techniques that can be applied and the differences in social interaction and support needed by people affected by different NCDs

College Students’ Perceptions of Popular Diets and Orthorexia Nervosa

Data suggest that individuals’ purported dietary patterns influence others’ perceptions of them. However, few studies have investigated how adherence to specific popular diets might influence these perceptions. This study investigated female undergraduates’ (n = 463) perceptions of vignette characters described as adhering to specific dietary practices perceptions of a vignette character in a sample of 463 female undergraduates. Characters described as adhering to a Clean Eating diet were viewed most positively, followed by individuals on the Ketogenic diet or nondieters. Characters following an Intermittent Fasting diet, and those with Orthorexia Nervosa were viewed most negatively. These findings support the idea that individuals’ adherence to specific popular diets might influence others’ views of them. Correlates of Orthorexia Nervosa (ON) were also investigated; diet-related impairment and weight bias internalization were positively correlated with ON symptomatology. Attitudes towards the vignette character with ON were not significantly correlated with ON symptomatology. Future research should investigate potential links between impression management and dieting motivation and adherence