Comparison of Voluntary versus Mandatory Vaccine Discussions in Online Health Communities: A Text Analytics Approach

Vaccines are vital health interventions. However, they are controversial and some people support them while others reject them. Social media discussion and big data are a rich source to understand people’s insights about different vaccines and the related topics that concern most of them. This study aims to explore the online discussions about mandatory and voluntary vaccines using text analysis techniques. Reddit social platform is popular in online health discussion and thus data from Reddit is analyzed. The results show that different aspects are discussed for different types of vaccines. The discussion of mandatory vaccines is more interactive and is focused on the risks associated with them. Voluntary vaccines’ discussion is focused on their effectiveness and whether to get them or not. The study have important implications for health agencies and researchers as well as for healthcare providers and caregivers

Computational Approaches to Characterizing Online Health Communities

Online health communities (OHCs) have been increasingly popular among patients with chronic or life-threatening illnesses for the exchange of social support. Contemporary research of OHCs relies on methods and tools to handle analytics of massive user-generated content at scale to complement traditional qualitative analysis. In this thesis, we aim at advancing the area of research by providing computational tools and methods which facilitate automated content analysis, and by presenting applications of these tools to investigating member characteristics and behaviors. We first provide a framework of conceptualization to systematically describe problems, challenges, and existing solutions for OHCs from a social support standpoint, to bridge the knowledge gap between health psychology and informatics. With this framework in hand, we define the landscape of online social support, summarize current research progress of OHCs, and identify research questions to investigate for this thesis. We then build a series of computational tools for analyzing OHC content, relying on techniques of machine learning and natural language processing. Leveraging domain-specific features, our tools are tailored to handle content analysis tasks on OHC text effectively. Equipped with computational tools, we demonstrate how characteristics of OHC members can be identified at scale in an automated fashion. In particular, we build up multi-dimensional descriptions for patient members, consisting of what topics they focus on, what sentiment they express, and what treatments they discuss and adopt. Patterns of how these member characteristics change through time are also investigated longitudinally. Finally, relying on computational analytics, members' behaviors of engagement such as debate and dropping-out are identified and characterized. Studies presented in this thesis discover static and longitudinal patterns of member characteristics and engagement, which are potential research hypotheses to be explored by health psychologists and clinical researchers. The thesis also contributes to the informatics community by making computational tools, lexicons, and annotated corpora available to facilitate future research

Themes and Participants’ Role in Online Health Discussion: Evidence From Reddit

Health-related topics are discussed widely on different social networking sites. These discussions and their related aspects can reveal significant insights and patterns that are worth studying and understanding. In this dissertation, we explore the patterns of mandatory and voluntary vaccine online discussions including the topics discussed, the words correlated with each of them, and the sentiment expressed. Moreover, we explore the role opinion leaders play in the health discussion and their impact on participation in a particular discussion. Opinion leaders are determined, and their impact on discussion participation is differentiated based on their different characteristics such as their connections and locations in the social network, their content, and their sentiment. We apply social network analysis, topic modeling, sentiment analysis, machine learning, econometric analysis, and other techniques to analyze the collected data from Reddit. The results of our analyses show that sentiment is an important factor in health discussion, and it varies between different types of discussions. In addition, we identified the main topics discussed for each vaccine. Furthermore, the results of our study found that global opinion leaders have more influence compared to local opinion leaders in elevating the health discussion. Our study has important theoretical and practical implications

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

Unsupervised Biomedical Named Entity Recognition

Named entity recognition (NER) from text is an important task for several applications, including in the biomedical domain. Supervised machine learning based systems have been the most successful on NER task, however, they require correct annotations in large quantities for training. Annotating text manually is very labor intensive and also needs domain expertise. The purpose of this research is to reduce human annotation effort and to decrease cost of annotation for building NER systems in the biomedical domain. The method developed in this work is based on leveraging the availability of resources like UMLS (Unified Medical Language System), that contain a list of biomedical entities and a large unannotated corpus to build an unsupervised NER system that does not require any manual annotations. The method that we developed in this research has two phases. In the first phase, a biomedical corpus is automatically annotated with some named entities using UMLS through unambiguous exact matching which we call weakly-labeled data. In this data, positive examples are the entities in the text that exactly match in UMLS and have only one semantic type which belongs to the desired entity class to be extracted (for example, diseases and disorders). Negative examples are the entities in the text that exactly match in UMLS but are of semantic types other than those that belong to the desired entity class. These examples are then used to train a machine learning classifier using features that represent the contexts in which they appeared in the text. The trained classifier is applied back to the text to gather more examples iteratively through the process of self-training. The trained classifier is then capable of classifying mentions in an unseen text as of the desired entity class or not from the contexts in which they appear. Although the trained named entity detector is good at detecting the presence of entities of the desired class in text, it cannot determine their correct boundaries. In the second phase of our method, called “Boundary Expansion”, the correct boundaries of the entities are determined. This method is based on a novel idea that utilizes machine learning and UMLS. Training examples for boundary expansion are gathered directly from UMLS and do not require any manual annotations. We also developed a new WordNet based approach for boundary expansion. Our developed method was evaluated on three datasets - SemEval 2014 Task 7 dataset that has diseases and disorders as the desired entity class, GENIA dataset that has proteins, DNAs, RNAs, cell types, and cell lines as the desired entity classes, and i2b2 dataset that has problems, tests, and treatments as the desired entity classes. Our method performed well and obtained performance close to supervised methods on the SemEval dataset. On the other datasets, it outperformed an existing unsupervised method on most entity classes. Availability of a list of entity names with their semantic types and a large unannotated corpus are the only requirements of our method to work well. Given these, our method generalizes across different types of entities and different types of biomedical text. Being unsupervised, the method can be easily applied to new NER tasks without needing costly annotations

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe