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A controversial bill aimed to stimulate investment and boost the economy in Indonesia, called the Omnibus Law Bill, is followed by both protest and support expressed in social media prior to its signatories in October 2020. During that time, the Twittersphere is packed with both the Opposition and Support movement of the bill, who both benefit from the use of hashtags. To distinguish an organic grass-roots movement from a propaganda that fits the agenda of the government and elite, a comparison study is conducted with a framework of top-down and bottom-up- mechanism of information virality (Nahon & Hemsley, 2013). The top-down mechanism combined with participatory propaganda theory is designated to explain the Support movement. Vice versa the bottom-up mechanism is combined with connective action theory designed to explain the Opposition movement as its character in line with a contemporary and digital protest movement (Bennett & Segerberg, 2012). As existing research only often studies both networks alone, this unique case provides an opportunity to compare both networks. A mixed-method of Social Network Analysis (SNA) and Topic Modelling used to differentiate the characteristics of both groups, based on both network structure and topics discussed. The finding in regards to the SNA is corresponding to the theoretical framework and previous studies. The loosely organized nature of connective action is reflected in several characteristics of the Opposition Network, in contrast to the element of coordination found in the Support Network. Findings from bi-term topic modeling, however, both contradict and support the hypothesis that suggests more variations in the topics within the Opposition Network as a result of the self-motivated participant and personalized messages (Leong et al., 2019).

<p>Indiana has one of the worst health rankings in the nation at 41. The Centers for Disease Control and Prevention and the Robert Wood Johnson Foundation, among others have recognized that comprehensive, multi-disciplinary approaches are necessary to improve health in communities. No single organization, large or small, has the capacity to solve chronic disease, and thus coalitions have been touted as a solution to engage communities to better health. Evaluation of coalitions are critical to provide evidence of efficacy and identify factors required to build successful and sustainable health coalitions. A novel partnership between the Indiana State Department of Health (ISDH) and the Indiana Clinical and Translational Sciences Institute (CTSI) called Connections IN Health (CINH), integrates three Indiana chronic disease coalitions to improve the health of Indiana residents. A unique mission of this partnership is to integrate work from the three different disease areas of the coalitions (obesity, cardiovascular disease, and asthma) to enhance community engagement in Indiana counties. Coalition leads for each disease area were hired, as well as a manager to oversee integration of the coalitions. The coalitions are being re-built by increasing and diversifying membership, working together with funders to engage Indiana communities, and providing strong and formalized leadership to coalition members. Assessment of CINH is crucial to provide evidence that this approach of integrated coalition leadership is an archetype for successful health department/clinical translational science award (CTSA) collaboration for other CTSAs, and could be a reproducible approach to improve the translation of research from bench-to-bedside. Traditional evaluations of coalitions focus on singular process and formative assessments, which fail to capture the dynamic and inherently relational aspects of coalition functioning. Thus, I evaluated CINH coalitions using a mixed-methods, multi-level evaluation framework that includes coalition functioning and effectiveness surveys and social network analysis. Using linear and logistic regression models, I found that after CINH was implemented, perceptions of coalition functioning and effectiveness significantly increased among coalition members one- and two-years after the partnership was implemented. I found from a comprehensive social network analysis that CINH was successful in growing and diversifying its coalition networks, that partnership networks became more centralized, and that the networks demonstrated traits of effectiveness based on other coalition network effectiveness studies. We suggest that coalition evaluation researchers move towards a unified evaluation approach that includes perception surveys, social network analysis, external community development, and health outcomes. In addition, an integral part of my work was to share findings back to the coalitions to enhance evaluation and help coalitions achieve their goals. In this thesis, I discuss: evidence that community coalitions can improve health, current evaluation methods for health coalitions, the Connections IN Health partnership, and implementation of a mixed-methods, multi-level evaluation framework. Finally, I present findings from my longitudinal network analysis of the CINH statewide chronic disease coalitions. </p>

The successful elimination of vaccine-preventable diseases is contingent on high-vaccine coverage rates in targeted populations. The proliferation of vaccine misinformation on social media has led to vaccine hesitancy in the past two decades. A highly contextual phenomenon, areas with an increased prevalence of vaccine hesitancy and vaccine exemption have been shown to correlate with decreased immunization coverage and intermittent vaccine-preventable disease outbreaks worldwide. Although the Canadian government has recommended the use of social media to increase public confidence in vaccines, little documentation exists regarding the perceptions of advisors and decision-makers in policy and communications for immunization towards vaccine hesitancy on social media, and the use of social media to increase public confidence in vaccines in the context of Ontario, Canada’s largest province. This thesis employed 3 unique mixed-methods studies to explore the role of social media in addressing the problem of vaccine hesitancy facilitated through misinformation about childhood vaccines in Ontario. The first study is a social network analysis that incorporates sentiment analysis to demonstrate that pro-vaccine and anti-vaccine communities operate in siloes with little interaction with one another. Those interactions that do occur are most commonly facilitated by sentiment and geographic location, rather than profession or affiliation of the social media user. The second study is a mixed methods content analysis illustrating significant differences in user attributes (emotion, medium shared in tweets, direction of information-sharing, and use of Twitter functions) among pro-, neutral, and anti-vaccine Twitter users, suggesting different motivations underlying Twitter use. Qualitative inquiry of links and reasons for negative vaccine sentiment illustrate the proliferation of pseudo-experts occupying social media, as well as concerns about vaccine safety and mistrust towards the government. The third study complements the first two studies, and uses documents and in-depth interviews with 23 advisers and decisionmakers in policy and communications to illustrate that although vaccine hesitancy is of concern, the use of social media to increase public confidence in vaccines is met with resistance due to a myriad of barriers at all levels of immunization policy and program delivery in the Province of Ontario. Implications for policy and practice of this study include the recognition that a multi-pronged approach is needed to increase the public’s confidence in vaccines. Elements of this multi-pronged approach could include: i) commitments to investing in understanding social media’s use in informing immunization at all levels of governance and decision-making; ii) the active surveillance of public sentiment and the public’s concerns about vaccines on social media using network analysis and content analysis; and iii) the fostering of interdisciplinary collaboration to design interventions that facilitate connectivity between siloes. The implications for future research include the need for continued commitment to the design, implementation, and evaluation of public health interventions on social media in the Ontario context. This study points to the need to pay attention to the behavioral attributes and affordances of social media in order to develop policies, communicative strategies, and programmatic designs that comprehensively address public concerns towards vaccines and, in turn, promote increased confidence in them. / Thesis / Doctor of Philosophy (PhD) / Immunization efforts are integral to maintaining herd immunity. Over the past two decades, it has been observed that vaccine hesitancy brought forth by vaccine misinformation has led to reduced confidence in vaccines, contributing to declining vaccination rates that have subsequently led to outbreaks of vaccine-preventable diseases. Vaccine misinformation on social media has played a crucial role in exacerbating vaccine hesitancy. Limited research has explored the use of social media in the Canadian context in relation to how vaccine information is communicated, what is being discussed and with whom. The extent to which decision-makers working in the immunization policy arena in Canada consider the role of social media as a tool for addressing vaccine hesitancy in order to increase vaccine uptake is also unclear. Using a mixed methods approach, this study, carried out in Ontario, Canada, illustrates that communities supporting and opposing vaccines operate in silos that do not necessarily communicate with each other through social media. Although decision-makers acknowledge the role of social media in the salience of vaccine hesitancy, they consider social media to be a less feasible method to increase vaccine confidence. By exploring the networks and conversations about vaccination on social media, and by understanding decision-makers’ perceptions towards vaccine hesitancy and social media, this study identified gaps between the recommendations for addressing vaccine hesitancy, provincial decision-makers’ preference for addressing immunization, and concerns of the vaccine hesitant on social media. These findings can inform the design of public health messaging to increase the public’s confidence in vaccines in Ontario.

Previous research has mainly focused on the social capital formation process on Facebook. In general, professionally-oriented social network sites (P-SNSs), such as LinkedIn, are under-researched in the Information Systems discipline. In addition, current studies do not include the effects of important elements of social network sites (SNS) such as one’s profile on social capital formation. As such, the main objective of this research is to propose and validate a model that explains the process by which individuals develop and accrue social capital through using P-SNSs. The theoretical framework of the proposed research draws upon Social Network Analysis, Social Media Analysis, and Social Capital Theory. Using an online survey of 377 LinkedIn users, this study finds that: (1) P-SNS users’ actions (perceived profile disclosure, active participation, and passive consumption) have significant positive effects on perceived social connectedness; (2) perceived social connectedness on P-SNSs has a significant positive effect on perceived networking value on these sites; (3) perceived profile disclosure and passive consumption have significant positive effects on network size; (4) active participation does not have any effect on network size; and (5) network size does not have a significant effect on perceived networking value. Overall, this investigation advances our understanding of how social capital is formed in P-SNSs. Additionally, by including the profile disclosure construct in the research model, this is the first study in the P-SNS context that investigates the role of the user profile in the social capital formation process, along with user actions such as active participation and passive consumption. From a practical perspective, this study has implications for different audiences such as job seekers, policy-makers, and P-SNS providers, assisting them in playing a more effective role in the social capital formation process on P-SNSs. / Thesis / Doctor of Business Administration (DBA) / In recent years, people increasingly spend their time on various social network sites (SNSs) such as Facebook and LinkedIn. This raises a serious question as to how people gain actual benefits from using these sites. This research examines this question from the lens of social capital. As such, the main objective of this research was to propose and validate a model that explains the process by which individuals develop social capital through professionally-oriented SNS such as LinkedIn. This study finds that to gain actual benefits from professionally-oriented SNS, such as networking value, people need to feel connected to their social networks on the site. This feeling of connection requires that people actively participate on the site (e.g., share a post) rather than just reading and following other people’s posts. Also, to connect with more people, individuals should disclose more information on the site.

no / While criminals may start their activities at individual level, the same is in general not true for terrorists who are mostly organized in well established networks. The effectiveness of a terror network could be realized by watching many factors, including the volume of activities accomplished by its members, the capabilities of its members to hide, and the ability of the network to grow and to maintain its influence even after the loss of some members, even leaders. Social network analysis, data mining and machine learning techniques could play important role in measuring the effectiveness of a network in general and in particular a terror network in support of the work presented in this chapter. We present a framework that employs clustering, frequent pattern mining and some social network analysis measures to determine the effectiveness of a network. The clustering and frequent pattern mining techniques start with the adjacency matrix of the network. For clustering, we utilize entries in the table by considering each row as an object and each column as a feature. Thus features of a network member are his/her direct neighbors. We maintain the weight of links in case of weighted network links. For frequent pattern mining, we consider each row of the adjacency matrix as a transaction and each column as an item. Further, we map entries into a 0/1 scale such that every entry whose value is greater than zero is assigned the value one; entries keep the value zero otherwise. This way we can apply frequent pattern mining algorithms to determine the most influential members in a network as well as the effect of removing some members or even links between members of a network. We also investigate the effect of adding some links between members. The target is to study how the various members in the network change role as the network evolves. This is measured by applying some social network analysis measures on the network at each stage during the development. We report some interesting results related to two benchmark networks: the first is 9/11 and the second is Madrid bombing.

In today's globalized and technology-driven landscape, engineers wield unprecedented influence. As a response to calls from engineering accrediting and professional organizations, engineering educators have begun to further emphasize the importance of ethical decision-making within the curriculum. However, despite numerous attempts to integrate ethics, there remains a lack of consensus on effective strategies, particularly for larger-scale initiatives. This research, utilizing Lattuca and Stark's (2009) Academic Plan model, explores the Pathways curriculum reform at Virginia Tech, a university-wide initiative aimed at integrating intercultural awareness and ethical reasoning across general education courses. Through a case study methodology, semi-structured interviews were conducted with 12 faculty in the College of Engineering. Participants shared insights on the barriers encountered, resources utilized, and perceptions of ethical culture within their various academic environments. Additionally, participants described their network interactions within and beyond the curriculum reform initiative. Findings suggest faculty leverage existing networks during curriculum reform, with identified barriers categorized as influence-driven and resource-driven. Integrating these insights into the Academic Plan model offers a nuanced, process-oriented understanding of curricular change. / Doctor of Philosophy / In today's globalized and technology-driven landscape, engineers wield unprecedented influence. As a response to calls from engineering accrediting and professional organizations, engineering educators have begun to further emphasize the importance of ethical decision-making within the curriculum. However, despite numerous attempts to integrate ethics, there remains a lack of consensus on effective strategies, particularly for larger-scale initiatives. This research explores the Pathways curriculum reform at Virginia Tech, a university-wide initiative aimed at integrating intercultural awareness and ethical reasoning across general education courses. To understand faculty experiences related to the curriculum reform, interviews were conducted with 12 faculty in the College of Engineering. Participants shared insights on the barriers encountered, resources utilized, and perceptions of ethical culture within their various academic environments. Additionally, participants described their personal collaborations within and beyond the curriculum reform initiative. Findings suggest faculty leverage existing networks during curriculum reform, with identified barriers categorized as influence-driven and resource-driven. By integrating these insights into one connected framework, we might be able to better understand and navigate the barriers associated with curriculum reforms.

Topic management is the task of gathering, evaluating, organizing, and sharing a set of web sites for a specific topic. Current web tools do not provide adequate support for this task. We created and continue to develop the TopicShop system to address this need. TopicShop includes (1) a web crawler/analyzer that discovers relevant web sites and builds site profiles, and (2) user interfaces for information workspaces. We conducted an empirical pilot study comparing user performance with TopicShop vs. Yahooï . Results from this study were used to improve the design of TopicShop. A number of key design changes were incorporated into a second version of TopicShop based on results and user comments of the pilot study including (1) the tasks of evaluation and organization are treated as integral instead of separable, (2) spatial organization is important to users and must be well supported in the interface, and (3) distinct user and global datasets help users deal with the large quantity of information available on the web. A full empirical study using the second iteration of TopicShop covered more areas of the World Wide Web and validated results from the pilot study. Across the two studies, TopicShop subjects found over 80% more high-quality sites (where quality was determined by independent expert judgements) while browsing only 81% as many sites and completing their task in 89% of the time. The site profile data that TopicShop provide -- in particular, the number of pages on a site and the number of other sites that link to it -- were the key to these results, as users exploited them to identify the most promising sites quickly and easily. We also evaluated a number of link- and content-based algorithms using a dataset of web documents rated for quality by human topic experts. Link-based metrics did a good job of picking out high-quality items. Precision at 5 (the common information retrieval metric indicating the percentage of high quality items selected that are actually high quality) is about 0.75, and precision at 10 is about 0.55; this is in a dataset where 32% of all documents were of high quality. Surprisingly, a simple content-based metric, which ranked documents by the total number of pages on their containing site, performed nearly as well. These studies give insight into users' needs for the task of topic management, and provide empirical evidence of the effectiveness of task-specific interfaces (such as TopicShop) for managing topical collections. / Ph. D.

As we develop intelligent systems to handle online interactions and digital stories, how do we address those stories that are unwritten and invisible? How do ensure that communities who value oral histories are not left behind, and their voices also inform the design of these systems? How do we determine that the technology we design respect the agency and ownership of the stories, without imposing our own biases? To answer these questions, I rely on accounts from different underrepresented communities, as avenues to examine how digital technology affect their stories, and the agency they have over them. From these stories, I elicit guidelines for the design of equitable and resilient tools and technologies. I sought wisdom from griots who are master storytellers and story-keepers on the craft of handling both written and unwritten stories, which instructed the development of the Respectful Space for technology typology, a framework that informs our understanding and interaction with underrepresented stories. The framework guided the approach to understand technology use by inhabitants of rural spaces in the United States--particularly long-distance hikers who traverse these spaces. I further discuss the framework's extensibility, by considering its use for community self-reflection, and for researchers to query the ethical implications of their research, the technology they develop, and the consideration for the voices that the technology amplifies or suppresses. The intention is to highlight the vast resources that exist in domains we do not consider, and the importance of the underrepresented voices to also inform the future of technology. / Doctor of Philosophy / Advances in technology do not always consider how they affect group interactions, and the resulting tensions for marginal and underrepresented groups and contexts. As more technological advances focus on these contexts and communities, it is important to consider, identify, and examine these tensions and their effect on communities. We use stories from different communities as avenues for understanding technological impact, and as guides for the design of equitable and resilient tools and technologies. Stories are accessible, universal, and powerful. They guide the design of the Respectful Space for technology typology that I describe in this dissertation. Stories also allow for a combination of different areas of research: we can use Human Computer Interaction (HCI) to understand the impact of technology on human behavior, parse human language with Natural Language Processing (NLP), understand patterns in storytelling with machine learning, and leverage theories from social sciences to understand how people think, how they organize themselves, and how this translates to online spaces. I present three studies in this dissertation whose broad aims are to elicit guidelines for designing respectful technologies, and to guide our design approach for underrepresented contexts based on stories from these spaces. Using the respectful approach as a scaffold, I then give context to other research domains: informing the design of tools to amplify other communities to tell their own stories offline and online, and, more broadly, in providing spaces to query how these techniques offer key opportunities to understand other emerging and growing areas in computer science including ethics, and fairness and accountability in algorithm design.

The penetration of social media today enables the rapid spread of breaking news and other developments to millions of people across the globe within hours. However, such pervasive use of social media by the general masses to receive and consume news is not without its attendant negative consequences as it also opens opportunities for nefarious elements to spread rumors or misinformation. A rumor generally refers to an interesting piece of information that is widely disseminated through a social network and whose credibility cannot be easily substantiated. A rumor can later turn out to be true or false or remain unverified. The spread of misinformation and fake news can lead to deleterious effects on users and society. The objective of the proposed research is to develop a range of machine learning methods that will effectively detect and characterize rumor veracity in social media. Since users are the primary protagonists on social media, analyzing the characteristics of information spread w.r.t. users can be effective for our purpose. For our first problem, we propose a method of computing user embeddings from underlying social networks. For our second problem, we propose a long short-term memory (LSTM) based model that can classify whether a story discussed in a thread can be categorized as a false, true, or unverified rumor. We demonstrate the utility of user features computed from the first problem to address the second problem. For our third problem, we propose a method that uses user profile information to detect rumor veracity. This method has the advantage of not requiring the underlying social network, which can be tedious to compute. For the last problem, we investigate a rumor mitigation technique that recommends fact-checking URLs to rumor debunkers, i.e., social network users who are very passionate about disseminating true news. Here, we incorporate the influence of other users on rumor debunkers in addition to their previous URL sharing history to recommend relevant fact-checking URLs. / Doctor of Philosophy / A rumor is generally defined as an interesting piece of a story that cannot be authenticated easily. On social networks, a user can generally find an interesting piece of news or story and may share (retweet) it. A story that initially appears plausible can later turn out to be false or remain unverified. The propagation of false rumors on social networks has a deteriorating effect on user experience. Therefore, rumor veracity detection is important, and drawing interest in social network research. In this thesis, we develop various machine learning models that detect rumor veracity. For this purpose, we exploit different types of information regarding users, such as profile details and connectivity with other users etc. Moreover, we propose a rumor mitigation technique that recommends fact-checking URLs to social network users who are passionate about debunking rumors. Here, we leverage similar techniques used in e-commerce sites for recommending products to solve this problem.