When people access online health information, unfortunately, they have access to both clinically accurate and inaccurate information that they may then utilize to make informed personal health decisions. This research fills a gap in the literature of online health communities as they relate to chronic obstructive pulmonary disease (COPD). The conduct of this research required a multi-phased and multi-method approach, best presented in three distinct essays. In Essays 1 and 2, data gathering within two online health communities specific to COPD allowed this study to address three research questions: (1) what are the information needs of COPD patients that result in their participation in online health communities; (2) what are the information sources offered to the participants in these online communities; and (3) is the information obtained via those communities credible. Essay 1 harvested data from a moderated website hosted by a non-profit organization for patients with COPD and Essay 2 harvested data from a non-moderated Facebook group also serving this unique group. Data Miner, a Chrome extension designed to extract data, was used to collect data, key words and themes which brought an understanding of the health information needs of participants and identified what health information sources were preferred. Using NIH guidelines, the credibility of sources exchanged were evaluated for both groups. The research presented in Essay 1 showed that COPD patients have health information needs and that a clinically monitored social health online community, that is available 24/7 to answer questions that arise at the time of need, provides much needed support. The research in Essay 2 illustrates the need for healthcare workers to be aware of unmoderated sites and promote these sites for the purpose of socialization only, and not for medical information. Building on the knowledge gained through the data analysis in Essays 1 and 2 and based on the theoretical frameworks established in the health belief model, social exchange theory, and the technology acceptance model, Essay 3 generated a new integrated model that seeks to understand information seeking effectiveness in online health communities was proposed. This model identifies the relationships between the types of disease specific information sought by members of 65 COPD Facebook groups, and member success in acquiring credible and clinically accurate health information to use in making health decisions related to disease management and the development of effective health management behaviors. Structural equation modeling was utilized to analyze survey responses and test the proposed model for statistical significance This study has important implications for health educators and medical professionals that will enhance their understanding of the benefits of online peer health communities and will guide them in providing their patients with an "information prescription" guiding them to clinically accurate and understandable, disease specific health information between office visits and at the patient's time of need.

With the expansion of online communities, extant research in multiple disciplines has attempted to investigate its adoption and use among individuals. However, the biggest challenge encountered by managers of these communities is supplying knowledge, particularly, the willingness to share knowledge among the members. It is extremely important to maintain committed members in terms of active participation. Yet their level of participation might vary based on some social, behavioral and environmental factors that eventually affect their intentions on whether to participate actively or not, in fact some users choose to discontinue participating totally in the community. Cancers figure among the leading causes of morbidity and mortality worldwide, with approximately 14 million new cases and 8.2 million cancer related deaths in 2012. The number of new cases is expected to rise by about 70% over the next 2 decades. Among men, the 5 most common sites of cancer diagnosed in 2012 were lung, prostate, colorectal, stomach, and liver cancer. According to the world cancer report, among women the 5 most common sites diagnosed were breast, colorectal, lung, cervix, and stomach cancer. For this reason, there is an ever-increasing need to establish communities to offer empathic support to patients. Though peer support groups have been known to offer adequate support to patients with cancer and are considered to be an important complement to the formal health care system, however, practical barriers such as time, mobility and geography limit their use, this is where the online communities serve an advantage, as they have the potential to overcome barriers posed by regular offline communities. To achieve its objectives, this study mainly adopts the Social cognitive theory and two components of the social influence theory. According to the SCT, user behaviour is influenced by two factors: personal cognition and environment. Social influence model postulates that individual behaviour in a community can be affected by the social environment and three factors constitute this, they are compliance, identification and internalization. The study aims to provide insights on how and why patients diagnosed with cancer (and their relatives) seek social support using the Internet and social media. In particular, we seek to understand the motivation for joining these groups and the values derived from the community for the users both active and non-active.

The current dissertation provides an examination of health information technology (HIT) by analyzing big datasets. It contains two separate essays focused on: (1) the evolving intellectual structure of the healthcare informatics (HI) and healthcare IT (HIT) scholarly communities, and (2) the impact of social support exchange embedded in social interactions on health promotion outcomes associated with online health community use. Overall, this dissertation extends current theories by applying a unique combination of methods (natural language processing, machine learning, social network analysis, and structural equation modeling etc.) to the analyses of primary datasets. The goal of the first study is to obtain a full understanding of the underlying dynamics of the intellectual structures of HI and its sub-discipline HIT. Using multiple statistical methods including citation and co-citation analysis, social network analysis (SNA), and latent semantic analysis (LSA), this essay shows how HIT research has emerged in IS journals and distinguished itself from the larger HI context. The research themes, intellectual leadership, cohesion of these themes and networks of researchers, and journal presence revealed in our longitudinal intellectual structure analyses foretell how, in particular, these HI and HIT fields have evolved to date and also how they could evolve in the future. Our findings identify which research streams are central (versus peripheral) and which are cohesive (as opposed to disparate). Suggestions for vibrant areas of future research emerge from our analysis. The second part of the dissertation focuses on comprehensively understanding the effect of social support exchange in online health communities on individual members’ health promotion outcomes. This study examines the effectiveness of online consumer-to-consumer social support exchange on health promotion outcomes via analyses of big health data. Based on previous research, we propose a conceptual framework which integrates social capital theory and social support theory in the context of online health communities and test it through a quantitative field study and multiple analyses of a big online health community dataset. Specifically, natural language processing and machine learning techniques are utilized to automate content analysis of digital trace data. This research not only extends current theories of social support exchange in online health communities, but also sheds light on the design and management of such communities.

Online Health Communities (OHCs) have positively disrupted the modern global healthcare system as patients and caregivers are interacting online with similar peers to improve quality of their life. Social support is the pillar of OHCs and, hence, analyzing the different types of social support activities contributes to a better understanding and prediction of future user engagement in OHCs. This thesis used data from a popular OHC, called Breastcancer.org, to first classify user posts in the community into the different categories of social support using Word2Vec for language processing and six different classifiers were explored, resulting in the conclusion that Random Forest was the best approach for classification of the user posts. This exercise helped identify the different types of social support activities that users participate in and also detect the most common type of social support activity among users in the community. Thereafter, three trajectory-based methods were proposed and implemented to predict user churn (attrition) from the OHC. Comparison of the proposed trajectory-based methods with two non-trajectory-based benchmark methods helped establish that user trajectories, which represent the month-to-month change in the type of social support activity of users are effective pointers for user churn from the community. The results and findings from this thesis could help OHC managers better understand the needs of users in the community and take necessary steps to improve user retention and community management.

Online health communities (OHCs) have become a medium for patients to share their personal experiences and interact with peers on topics related to a disease, medication, side effects, and therapeutic processes. Many studies show that using OHCs regularly decreases mortality and improves patients mental health. As a result of their benefits, OHCs are a popular place for patients to refer to, especially patients with a severe disease, and to receive emotional and informational support. The main reasons for developing OHCs are to present valid and high-quality information and to understand the mechanism of social support in changing patients' mental health. Given the purpose of OHC moderators for developing OHCs applications and the purpose of patients for using OHCs, there is no facility, feature, or sub-application in OHCs to satisfy patient and moderator goals. OHCs are only equipped with a primary search engine that is a keyword-based search tool. In other words, if a patient wants to obtain information about a side-effect, he/she needs to browse many threads in the hope that he/she can find several related comments. In the same way, OHC moderators cannot browse all information which is exchanged among patients to validate their accuracy. Thus, it is critical for OHCs to be equipped with computational tools which are supported by several sophisticated computational models that provide moderators and patients with the collection of messages that they need for making decisions or predictions. We present multiple computational models to alleviate the problem of OHCs in providing specific types of messages in response to the specific moderator and patient needs. Specifically, we focused on proposing computational models for the following tasks: identifying emotional support, which presents OHCs moderators, psychologists, and sociologists with insightful views on the emotional states of individuals and groups, and identifying informational support, which provides patients with an efficient and effective tool for accessing the best-fit messages from a huge amount of patient posts to satisfy their information needs, as well as provides OHC moderators, health-practitioners, nurses, and doctors with an insightful view about the current discussion under the topics of side-effects and therapeutic processes, giving them an opportunity to monitor and validate the exchange of information in OHCs. We proposed hybrid models that combine high-level, abstract features extracted from convolutional neural networks with lexicon-based features and features extracted from long short-term memory networks to capture the semantics of the data. We show that our models, with and without lexicon-based features, outperform strong baselines.

People are increasingly turning to online communities for entertainment, information, and social support, among other uses and gratifications. Online communities include traditional online social networks (OSNs) such as Facebook but also specialized online health communities (OHCs) where people go specifically to seek social support for various health conditions. OHCs have obvious health ramifications but the use of OSNs can also influence people's mental health and health behaviors. The use of online communities has been widely studied but in the health context their exploration has been more limited. Not only are online communities being extensively used for health purposes, but there is also increasing concern that the use of online communities can itself affect health. Therefore, there is a need to better understand how such technologies influence people's health and health behaviors. The research in this dissertation centers on examining how online community use influences health and health behaviors. There are three studies in this dissertation. The first study develops a conceptual model to explain the process whereby the characteristics of a request from an OHC user for social support is answered by a wounded healer, who is a person leveraging their own experiences with health challenges to help others. The second study investigates how algorithmic fairness, accountability, and transparency of an OSN newsfeed algorithm influence the users' attitudes and beliefs about childhood vaccines and ultimately their vaccine hesitancy. The third study examines how OSN social overload, through OSN use, can lead to psychological distress and received social support. The research contributes theoretical and practical insights to the literature on the use of online communities in the health context. / Doctor of Philosophy / People use online communities to socialize and to seek out information and help. Online social networks (OSNs) such as Facebook are large communities on which people segregate into smaller groups to discuss joint interests. Some online communities cater to specific needs, such as online health communities (OHCs), which provide platforms for people to talk about the health challenges they or their loved ones are facing. Online communities do not intentionally seek controversy, but because they welcome all perspectives, they have contributed to phenomena such as vaccine hesitancy. Moreover, social overload from the use of OSNs can have both positive and negative psychological effects on users. This dissertation examines the intersection of online communities and health. The first study explains how the interaction of the characteristics of a request for social support made by an OHC user and the characteristics of the wounded healer drive the provision of social support. The model that is developed shows the paths through which the empathy of the wounded healer and the characteristics of the request lead to motivation to provide help to those in need on an OHC. In the second study, the role of characteristics of a newsfeed algorithm, specifically fairness, accountability, and transparency (FAT), in the development of childhood vaccine hesitancy is examined. The findings show that people's perceptions of the newsfeed algorithm's FAT increase their negative attitudes toward vaccination and their perceived behavioral control over vaccination. The third study examines how different uses of OSNs can influence the relationships between social overload and psychological distress and received social support. The findings show how OSN use can be tailored to decrease negative and increase positive psychological consequences without discontinuing use.

This research has examined how Information and Communication Technologies (ICTs) can promote healthy eating habits amongst African Americans in low-income neighborhoods, a population that faces disproportionately high rates of diet-related health problems. In this dissertation, I describe the formative research I conducted to obtain system design guidelines and how I used those guidelines to develop two applications: EatWell and Community Mosaic. I also describe the results of the in-depth field studies I conducted to evaluate each application. Both EatWell and Community Mosaic incorporate the cultural construct of collectivism, a social orientation in which interdependence and communal responsibility are valued over individual goals and independence. As researchers have generally characterized the African American culture as collectivistic and argued for the value of designing collectivistic health interventions for this population, I examined the implications of taking such an approach to designing health promotion technologies. EatWell and Community Mosaic are collectivistic because they empower users to care for the health of their local community by helping others learn practical, locally-relevant healthy eating strategies. I discuss the results of my formative fieldwork and system evaluations, which characterize the value, challenge and nuances of developing community-based health information sharing systems for specific cultural contexts. By focusing on health disparities issues and the community social unit, I extend previous health technology research within Human-Computer Interaction (HCI). In particular, my results describe 1) a set of characteristics that help make shared material useful and engaging, 2) how accessing this information affects how people view the feasibility of eating well in their local context, 3) the way in which sharing information actually benefits the contributor by catalyzing personal behavior reflection, analysis and modification and 4) how sharing information and seeing that information's impact on others can help to build individuals' capacity to be a community health advocate. In addition, my work shows how examining cultural generalizations such as collectivism is not a straightforward process but one that requires careful investigation and appreciation for the way in which such generalizations are (or are not) manifested in the lives of individual people. I further contribute to HCI by presenting a set of important considerations that researchers should make when designing and evaluating community-based health systems. I conclude this dissertation by outlining directions for future HCI research that incorporates an understanding of the relationship between culture and health and that attempts to address health disparities in the developed world.

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