Understanding user interaction patterns in health social media

Internet users are becoming increasingly social in their online information behavior, as shown by a growing trend of social media adoption in the past decade. Social interaction patterns in this new space are governed by the technological affordances in the infrastructure and membership to the community, yet at times also by individual emotional needs for seeking social support. In the consumer health domain, social networking technology and consumer health information needs combine to show that even within the same community, relationships are not expressed in the same manner across various computer mediated communication (CMC) formats. The motivation for this research comes from an increasing need to understand the patterns of social interactions online, especially of e-patients using the Internet as a health resource. Frequently, e-patients use social networking platforms to teach each other about conditions and treatments (Civan and Pratt, 2007; Wright and Bell, 2003). Social networking sites are predicted to increase in popularity as a way for people to socialize online as an extension of their physical environment. Online community tools include the popular text-based communication formats such as posting status updates, discussion boards and profile pages. While many previous studies of online support communities identified sociability factors such as types of social support (i.e. informational support, emotional support) exchanged in online support groups and health outcomes, there is a gap in research literature concerning the design of software interface architecture. The central focus of this research investigated the impact of software features on online supportive communication behavior across multiple computer-mediated communication (CMC) formats. This research contributes insights to opportunities for design and implementation of social media technologies for online health support communities; scholarly literature regarding online support communities, and inform policy makers who determine parameters for both design and management of online health support communities. The outcome of this study can contribute to improving online intervention programs by targeting specific functions of social network sites.Ph.D., Information studies -- Drexel University, 201

Identifying peer experts in online health forums

Abstract Background Online health forums have become increasingly popular over the past several years. They provide members with a platform to network with peers and share information, experiential advice, and support. Among the members of health forums, we define “peer experts” as a set of lay users who have gained expertise on the particular health topic through personal experience, and who demonstrate credibility in responding to questions from other members. This paper aims to motivate the need to identify peer experts in health forums and study their characteristics. Methods We analyze profiles and activity of members of a popular online health forum and characterize the interaction behavior of peer experts. We study the temporal patterns of comments posted by lay users and peer experts to uncover how peer expertise is developed. We further train a supervised classifier to identify peer experts based on their activity level, textual features, and temporal progression of posts. Result A support vector machine classifier with radial basis function kernel was found to be the most suitable model among those studied. Features capturing the key semantic word classes and higher mean user activity were found to be most significant features. Conclusion We define a new class of members of health forums called peer experts, and present preliminary, yet promising, approaches to distinguish peer experts from novice users. Identifying such peer expertise could potentially help improve the perceived reliability and trustworthiness of information in community health forums.https://deepblue.lib.umich.edu/bitstream/2027.42/148520/1/12911_2019_Article_782.pd

Social Media Analytics of Smoking Cessation Intervention: User Behavior Analysis, Classification, and Prediction

Tobacco use causes a large number of diseases and deaths in the United States. Traditional intervention programs are based on face-to-face consulting, and social support is offered to help smoking quitters control stress and achieve better intervention outcomes. However, the scalability of these traditional intervention programs is limited by time and location. With the development of Web 2.0, many intervention programs of smoking cessation are developed online to reach a wider population. QuitNet is a popular website for smoking cessation that provides different services to help users quit smoking. It builds communities on different social media for people to discuss issues of smoking cessation and provide social support for each other. In this dissertation, we develop a comprehensive study to understand user behavior and their discussion interactions in online communities of smoking cessation. We compare user features and behaviors on different social media channels, analyze user interactions from the perspective of social support exchange, and apply data mining techniques to analyze discussion content and recommend threads for users. Health communities are developed on different types of social media. For example, QuitNet has Web forums on its own Web site while it also has its appearance on Facebook. The user participation may vary on different social media platforms. Users may also behave differently depending on the functions and design of the social media platforms. So, as the first step in this dissertation, we carry out a preliminary study to compare smoking cessation communities on different social media channels. We analyze user characteristics and behaviors in QuitNet Forum and QuitNet Facebook with statistical analysis and social network analysis. It is found that most users of QuitNet Forum are early smoking quitters, and they participate in discussions more actively than users of QuitNet Facebook. However, users of QuitNet Facebook have a wider spectrum of quitting statuses and interaction behaviors. Second, we are interested in user behaviors and how they exchange social support in online communities. Social support is "an exchange of resources between two individuals perceived by the provider or the recipient to be intended to enhance the well-being of the recipient". As QuitNet Forum attracts much more active users than QuitNet Facebook, it provides a better platform for our research purpose. So, we focus on QuitNet Forum, developing a classification scheme through qualitative analysis to categorize discussion topics and types of social support on the forum. Patterns of user behaviors are defined and identified. Social networks are built to analyze user interactions of social support exchange. It is found that users at different quit stages have different behaviors to exchange social support, and different types of social support flow between users at different quit stages. Discussion topics, user behaviors and patterns of social support exchanges are thoroughly analyzed. However, due to a huge amount of information on QuitNet Forum, it is difficult for users to find proper topics or peers to discuss or interact with. It would be helpful if we could apply machine learning techniques to understand user generated information in online health communities, and recommend discussion topics to users to participate in. We develop classifiers to categorize posts and comments on QuitNet Forum in terms of user intentions and social support types. User behaviors and patterns are used to help developing various feature sets. Then, we develop recommendation techniques to recommend threads for users to participate in. Based on traditional Collaborative Filtering and content-based approaches, we integrate classification results and user quit stages to develop recommendation systems. The experiments show that integrating classification results or user health statuses can achieve the best recommendation results with different percentages of unknown data. In this dissertation, we implement all-sided studies for online smoking cessation communities, including comprehensive analytics and applications. The proposed frameworks and approaches could be applied to other health communities. In the future, we will apply more analytics and techniques to a larger data set, and develop user-end applications to serve and improve online health intervention programs and communities.Ph.D., Computer Science -- Drexel University, 201

Essays on Health Information Technology: Insights from Analyses of Big Datasets

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

Attributing Meaning to Online Social Network Analysis for Tailored Socio-Behavioral Support Systems

Ubiquitous online social networks provide us with a unique opportunity to deliver scalable interventions for the support of lifestyle modifications in order to change behaviors that predispose toward cancer and other diseases. At the same time these networks act as rich data sources to inform our understanding of end-user needs. Traditionally, social network analysis is based on communication frequency among members. In this work, I introduce communication content as a complementary frame for studying these networks. QuitNet, an online social network developed to provide smoking cessation support is considered for analysis. Qualitative coding, automated content analysis, and network analysis were used to construct QuitNet sub-networks based on both frequency and content attributes. This merging of qualitative, quantitative, and automated methods expands the depth and breadth of existing network analysis techniques thereby allowing us to characterize the nature of communication among network members. First, grounded-theory based qualitative analysis provides a granular view of the QuitNet messages. Using automated text analysis, the communication links between network members were divided based on the similarity of the content in the exchanged messages to the identified themes. This automated analysis allowed us to expand the otherwise prohibitively labor-intensive qualitative methods to a large data sample using minimal time and resources. The follow-up one-mode and two-mode network analysis allowed us to investigate the content-specific communication patterns of QuitNet members. Qualitative analysis of the QuitNet messages identified themes ranging from “Social support”, “Progress”, and “Traditions” to “Nicotine Replacement Therapy (NRT) entries” and “Craves”. Automated annotation of messages was achieved by using a distributional approach incorporating distributional information from an outside corpus into a model of the QuitNet corpus to generate vector representations of messages. A k- nearest neighbor approach was used to infer themes relating to each message. The recall and precision measures indicate that the performance of the automated classification system is 0.77 and 0.71 for high-level themes. The average agreement of the system with two human raters for high-level themes approached the agreement between these human coders for a subset of 100 messages suggesting that the system is a reasonable substitute for a human rater. Subsequent one-mode network analysis provided insights into different theme-based networks at population level revealing content-specific opinion leaders. Two-mode network analysis allowed us to investigate the content affiliation patterns of QuitNet users and understand the content-specific attributes of social influence on smoking abstinence. These studies provide insights into the nature of communication among members in a smoking cessation related online social network. Ability to identify critical nodes and content-specific network patterns of communication has implications for the development and maintenance of support networks for health behavior change. Analysis of the frequency and content of health-related social network data can inform the development of tailored behavioral interventions that provide persuasive and targeted support for initiating or adhering to a positive behavior change

Computational Thematic Analysis of Online Communities

Public health researchers can use thematic analysis to develop human understandings of health topics from the lived experiences discussed in online communities. However, thematic analyses of online communities are difficult to conduct because large data sets amplify the resource intensity and complexity of the common phases: Data Collection, Data Familiarization, Coding, and Theme Review. Researchers can manage this amplification by integrating computational techniques that facilitate scalable interaction with large data sets when they converge with tasks completed during a thematic analysis. My thesis’ research explored barriers to integrating computational techniques into thematic analysis through three research questions: RQ1. Could computational techniques be used within a thematic analysis to assist with the analysis of online communities’ data? RQ2. How might tools be developed to not require programming expertise when integrating computational techniques as part of thematic analysis tasks? RQ3. How does a computational thematic analysis that integrates computational techniques compare with a traditional manual thematic analysis? To address these questions, I used a three-staged approach where I first piloted integrating techniques in a thematic analysis of addiction recovery. I then designed artifacts based on my pilot experience that allow qualitative researchers without programming expertise to integrate techniques. Finally, I deployed my artifacts with public health researchers to explore integration’s impact on their real-world thematic analyses. During my Pilot Stage, I conducted a topic-guided thematic analysis of two Reddit addiction recovery communities. Performing this analysis contributed a demonstration of integrating Latent Dirichlet Allocation topic modelling, a computational technique, to guide my reflexive thematic analysis by sampling interesting places in online discussion data sets for coding. Additionally, I discussed how integration benefited my data familiarization by facilitating the identification of patterns while being limited due to balancing metric optimization with interpretive usefulness when creating topic models. In my Design Stage, I created my Computational Thematic Analysis Workflow and Computational Thematic Analysis Toolkit to build upon my pilot stage experiences and support qualitative researchers. My workflow provides researchers with guidance on planning a reflexive thematic analysis of online communities that integrates computational techniques. Similarly, my toolkit supports qualitative researchers by implementing computational techniques as reusable tools in a graphic user interface that integrates into thematic analyses without requiring programmer expertise. My Deploy Stage investigated the impact of integrating computational techniques by collaborating with public health researchers studying COVID-19 news article comments.The researchers independently performed two inductive thematic analyses, one of which used my Computational Thematic Analysis Toolkit. I then work with the researchers to compare their processes and results. From this comparison, I identified that integrating computational techniques to facilitate multiple data interactions aided the analysis by enabling different interpretations. Additionally, despite both analyses developing a convergent set of themes, computational technique integration had subtle influences leading to divergent analysis processes and coding approaches. The contributions from my three stages have collective implications for qualitative research, human-computer interaction, and public health. My work provides qualitative researchers with demonstrations and tools that support integrating computational techniques to research online communities. My research created a base workflow and toolkit that human-computer interaction practitioners can support and extend to facilitate the integration of computational techniques into qualitative methods. Additionally, I addressed calls in human-computer interaction research to include qualitative perspectives in work that impacts qualitative researchers. Finally, public health researchers can use my guidance and toolkit to manage the amplification of resource intensity and complexity to perform thematic analyses on the lived experiences discussed in online communities. As researchers identify online communities’ perspectives on new and existing health issues, they can de- velop health interventions that impact people represented by online communities

Disrupting surveillance: critical software design-led practice to obfuscate and reveal surveillance economies and knowledge monopolies

Big data collection, behavioural economics and targeted advertisement are changing the dynamics and notions of our individuality and societies. By mobilising critical design methods, I made a series of critical design works to reveal and disrupt surveillance and knowledge monopolies. The aim of this practice-led investigation is to challenge surveillance and knowledge practices within internet search and advertising industries and through this contribute to surveillance debates and critical design practice. The four critical design practices that I developed during the course of this investigation namely Zaytoun, Philodox, Maladox and Open Bubble all interrogate humans’ relations to technology and more specifically their transformations as objects and subjects of surveillance capitalism. Zaytoun challenges notions of data consumption, quantification and distancing. Philodox reveals and critiques some trust issues and algorithmic biases of internet search engines. Maladox, is an anatomical engine of fictional speculative cyborg dis-eases, creating a critical space to reconsider our relationship to technology. Finally, Open Bubble is a counter surveillance browser extension that obfuscates and challenges knowledge enclosures imposed by search engines. Based on a review of philosophy of technology and especially as it relates to Science and Technology Studies (STS), I reflect on some of the underlying conditions that made possible the existence of modern technology in its current form. I analyse the contextual background of this body of work and its take on technology as a central lever for governance and for shaping of human subjects. This thesis investigates the taken for granted ways our interactions with surveillance capitalism infrastructures are transforming our individual and collective beings and in turn the new cyborg ontologies that we are being integrated into. The four critical design works included in this investigation offer alternative possibilities for critical engagement with, and interpretation of, big data and the algorithmic manipulations we are subjected to. This thesis attempts to take the below contributions to the theoretical developments around governmentality, surveillance capitalism, but also to critical design and design informatics. I develop ideas aiming at moving from humans and subjectivity as the nexus for governance towards attention to the cyborg as the emerging central site for both governance and resistance. Furthermore, through my practices I illustrate the importance of non-visual relations to audiences be it through touch or hearing in opening up spaces for questioning and resistance. I believe attention to the sensory dynamics of the experience and resistance have strong potentials for contributing to the debates around resistance within governance regimes. Furthermore, this thesis brings attention to the micro processes & software codes and algorithms that enable surveillance capitalism and engages in exercises aiming at disrupting them. I believe such detailed work focused on the ways humans interact with internet-based regimes of surveillance is a much-needed complement to the already well-developed critiques of institutions and structures of surveillance capitalism. Concerning critical design, my works bring attention to the role of spatial configuration of the works in conditioning the users’ rhythm, intensity and span of engagement with the work. In addition, I believe my practices and my theoretical developments around them open possibilities for new reflections on different forms of satire and laughter and how they can be situated in users’ experiences with critical design work