Doctor of Philosophy, The Ohio State University, 2010, ED Physical Activities and Educational Services

The purpose of the study was to examine the relationships among perceived effectiveness of NBTAs, extent of advice networks, and perceived job outcomes in a semiconductor manufacturing company in Korea, using a mixed method. The data for the quantitative study were collected from an online survey questionnaire. The population consisted of all employees (N=15,000) who were working in production facilities of the company or branch offices in Korea. The total number of respondents was 188 out of 375 employees randomly selected, with an overall response rate of 50.13%. The data for the qualitative study were gathered from semi-structured interviews with eight employees who responded to the online survey. Canonical correlation analysis and hierarchical regression analysis were utilized to analyze the survey data. Additionally, content analysis was employed to analyzed and interpret the interview data. The results showed that on-the-job training approaches and training approaches within a business unit were perceived to be more helpful than common training approaches to develop advice relations. Yet, no relationships were found between advice networks and the perceived effectiveness of NBTAs. The results also indicated that no mediation occurred between the perceived effectiveness of NBTAs and perceived job outcomes. Although the study failed to reveal the mediation between the perceived effectiveness of NBTAs and perceived job outcomes, the findings from the quantitative and qualitative studies provided evidences that NBTAs helped individuals develop advice networks, and the development of advice networks through NBTAs had an impact on individual job performance and job satisfaction. In addition, the results of this study identified four processes which create advice networks through training approaches: 1) developing advice networks based on job-relatedness, 2) sharing a common interest among others, 3) spending time doing group activities with others, and 4) spending (open full item for complete abstract)

Committee: Ronald Jacobs PhD (Advisor); Joshua Hawley EdD (Committee Member); Larry Miller PhD (Committee Member) Subjects: Adult Education

Doctor of Philosophy, The Ohio State University, 2024, Environment and Natural Resources

Departing from literature on social-ecological fitness and social-ecological network analysis, this dissertation explores the degree to which social-ecological theory reflects underlying social processes of issue engagement and partnership evaluation and identifies pathways for future research to engage practitioners with social-ecological network data. In total, the research presented in this dissertation shows that social-ecological network analysis and theory can both be strengthened by participant engagement and qualitative analyses and can be translated into actionable information that practitioners can use to inform their management decisions. This research – which includes three consecutive empirical studies (chapters 2 through 4) – presents one of the first comprehensive accounts of confirming social-ecological network theory with participant populations. Each of the three chapters seeks to determine how practitioners navigate social-ecological interdependence by assessing whether practitioners' strategies align with social-ecological motifs that are commonly used in empirical network analyses (i.e., small-scale network structures that impart theoretically important processes). Further, all three empirical chapters analyze separate components of a dataset pertaining to climate change adaptation governance in Columbus, Ohio, which is a system comprised of over one hundred unique stakeholder organizations, 19 climate adaptation-related issues, and their interconnections. In the first chapter, I explore how community-engaged network tools can help to overcome fragmentation in environmental governance systems. I helped to develop a network tool that offers personalized partnership recommendations to practitioners that would close “collaborative gaps,” which are instances where two actors who manage the same issue(s) fail to collaborate with one another. Results from focus group conversations with practitioners suggest that engaged network tools can be 1) hampere (open full item for complete abstract)

Committee: Ramiro Berardo (Advisor); Matthew Hamilton (Advisor); Alia Dietsch (Committee Member); Cynthia Tyson (Committee Member) Subjects: Climate Change; Conservation; Environmental Management; Environmental Studies; Natural Resource Management; Public Administration

PHD, Kent State University, 2013, College of Education, Health and Human Services / School of Teaching, Learning and Curriculum Studies

This study sought to uncover teacher perceptions of social capital within a high school mathematics department utilizing a research design that acknowledged the complex environment faced by high school teachers and their subsequent interpretations of how and from whom they sought access to professional resources. Through an analysis of narratives captured as teachers interviewed one another in strong-tie pairs, the study identified the elements of social capital which were central to the professional lives of high school mathematics teachers. Narrative analysis revealed that the group, situated in an urban setting, was able to define issues around trust and structure within their network. There was significantly less discussion or acknowledgement of the remaining dimensions of social capital: level of professional expertise within their group, and the depth or content of their professional interactions. Teachers had no vocabulary, interactional routines, norms or other tools to assist in the analysis of these key social capital resources. The study found that there is a need for an additional dimension to be included in existing social capital models. Defined as “Group Self-Knowledge”, I describe this construct as a way for teacher networks to detect, define and assess their own capacity for change and innovation. The ability of a network to assess its social capital is described as necessary in determining specific needs for professional development resources, and in aligning those needs with the resources (physical, human or social capital) that are most likely to lead to conditions in which a network could learn, adapt, grow and change. Social capital models offer constructs which can assist in social capital analysis, and which could lead to significant impacts on educational organizations: a “systems” view that privileges the knowledge of the group and disrupts teachers' tendency towards individualism, presentism and conserv (open full item for complete abstract)

Committee: Joanne Arhar (Committee Chair); Tricia Niesz (Committee Member); Michael Mikusa (Committee Member); Joanne Caniglia (Committee Member) Subjects: Education; Mathematics Education; Social Research; Teaching

Master of Science (MS), Wright State University, 2022, Physics

There is much research about how actors and events in social networks affect each other. In this research, three network models were created for discussion forums in three semesters of undergraduate general physics courses. This study seeks to understand what social network measures are most telling of a online forum classroom dynamic. That is, I wanted to understand more about things like what students are most central to the networks and whether this is consistent across different network models. I also wanted to better understand how students may or may not group together. What relationships (student to student, student to instructor, etc.) are formed, centralization, various clustering and correlation coefficients, and how participation in a forum unfolds were all things that were examined in this data set. Network model construction and measuring how these constructions may affect student interactions was another focus of this study. These attributes are analyzed among individual semesters, but also compared/contrasted across all three, to see if they maintain across different network models. It was found that in general as models increase in connectivity, a rise in network measures like centralization and average degree was observed. A drop in network measure values such as average vertex-vertex distance and diameter was also seen. Finally, it was discovered that changing a model from undirected to directed made an appreciable change in average degree outcomes. Overall, this research gave an appreciation of different network model construction and how different network measures may help describe social networks. It was discovered that centralization metrics may be more telling of social networks than what was anticipated. Average degree, average vertex to vertex distance and diameter followed trends we would expect to see. Other measures looked into were transitivity, average Barrat coefficient and degree correlation coefficient.

Committee: Adrienne Traxler Ph.D. (Advisor); Ivan Medvedev Ph.D. (Committee Member); Jason Deibel Ph.D. (Committee Member) Subjects: Physics

Master of Science, Miami University, 2022, Kinesiology, Nutrition, and Health

Drunkorexia is disordered eating behaviors during an alcohol consumption event, or disordered eating behaviors occurring to compensate for excessive alcohol use. Women in Greek life are at risk for participating in drunkorexia behaviors. They are also more likely to be influenced by their social network, such as their sorority. Using social network analysis as a statistical tool allows for drunkorexia in a network to be examined quantitatively. The present study assessed whether drunkorexia behaviors could be identified within a sorority's drinking buddy network and how those behaviors may impact the overall network and subgroups within the network. It was hypothesized that members with high levels of centrality in the network would report more alcohol consumption and drunkorexia behaviors. The second hypothesis was that there would be cliques in the drinking buddy network and membership in these cliques would correspond to varying levels of drunkorexia. The findings showed that members who were more central in the network tended to drink more and also participate in compensatory drunkorexia behaviors. No cliques were found in the present study. The future implications for these results contribute to the field, because more efficient drunkorexia interventions may be created and placed strategically within networks.

Committee: Rose Marie Ward (Committee Chair); Paul Branscum (Committee Member); Aaron Luebbe (Committee Member) Subjects: Public Health

PhD, University of Cincinnati, 2021, Education, Criminal Justice, and Human Services: Educational Studies

Mixed methods (MM) research is an emergent methodology that involves collecting, analyzing, and intentionally integrating qualitative and quantitative data in a study. MM is increasingly used by graduate students and scholars across contexts. These researchers' practices of MM research, however, have been found to be limited in the literature, and it is unclear whether there is a relationship between how individuals learn about MM research and the extent to which they use MM practices. The purpose of this study was to explain how recent doctorates, who adopted and used MM research in their dissertations, learned about MM research. This study was informed by three theories (i.e., diffusion of innovations theory, social network theory, and ecological systems theory) and framed by an overarching philosophy of dialectical pluralism. The study used a mixed methods social network analysis (MMSNA) design, which combined MM research and social network analysis (SNA) with fully integrated analyses. The intent of employing an MMSNA design involved three rationales: (a) context, (b) diversity of views, and (c) complementarity. MMSNA design was implemented with a convergent design logic where online survey (N=81), secondary data analysis (N=81), and qualitative interviews (N=10) were used concurrently. Equal priority was given to MMSNA and qualitative data collection and analyses. Constant comparison technique was employed to compare qualitative thematic results with MMSNA pattern results. Then, the resulting themes and patterns were merged. The overall results were interpreted using MM integrative analysis through SNA maps, joint displays, and narrative. The results of the descriptive statistics indicated that recent doctorates identified 16 types of sources for learning about MM research. The SNA results revealed that books, courses, articles, advisors, and peers/colleagues were the top five sources for learning about MM research among recent doctorates. Six differen (open full item for complete abstract)

Committee: Vicki Plano Clark Ph.D (Committee Chair); Lisa Vaughn Ph.D (Committee Member); Amy Farley Ph.D (Committee Member); Jess Kropczynski Ph.D (Committee Member) Subjects: Education

Doctor of Philosophy, The Ohio State University, 2021, Communication

Political homophily, or the tendency for relationships or discussion ties to form more frequently between like-partisans than between people with differing political identities, is a well-studied phenomenon in the political communication and social networks literatures. Such political similarity has been observed in a variety of contexts, including in work environments, church congregations, universities, romantic relationships, neighborhoods, and online social media. Homophilous network structures have profound effects on normative democratic outcomes, such as participation and exposure to diverse sources of information. However, comparatively little attention has been paid to the antecedent processes which give rise to political homophily. This dissertation advances the concept of political selectivity, or the degree to which one's decisions about the status of ties in a political discussion network favor discussion partners who one perceives to be similar (i.e., shared party identity) to themselves. The culmination of networked theories of homophily, along with interpersonal theories of relational uncertainty and topic avoidance, together provide a holistic view of how the dyadic and network structures co-evolve over time. The goals of this dissertation are threefold: to isolate selection from other generative mechanisms, to explain individual variances in selectivity, and to provide a framework with which interpersonal processes, like topic avoidance, affect selection in dynamic discussion networks. To these ends, a five-phase, two-condition quasi-experiment was conducted in which participants shared political opinions with one another and made decisions regarding who they would like to continue discussing political matters with. Subjects (n = 366) were recruited from Amazon's Mechanical Turk participant pool into 24 cohorts. In condition 1, participants shared their opinions with each of their alters; in condition 2, participants were permitted to decide from (open full item for complete abstract)

Committee: Robert Bond (Advisor); Skyler Cranmer (Committee Member); William Eveland Jr. (Committee Member); Hillary Shulman (Committee Member) Subjects: Communication; Political Science

PHD, Kent State University, 2021, College of Arts and Sciences / Department of English

COVID, coupled with a flurry of black deaths at the hands of policemen, has spawned a new era of social movements. As online environments have multiplied, so have people's options for civic engagement. As a result, the field of writing studies and rhetoric is full of new research that questions what a social movement is and what it can do. However, it has yielded little empirical data that details the behind-the-scenes activity of a social movement organization. How can we understand what constitutes a social movement today if we rely only on what we see happening in the streets or on the internet? Such a front stage view only allows us access to the final product of activism, and thus, obscures the complex circumstances that catalyze and shape civic engagement. This research is an attempt to understand such circumstances, especially those related to writing as a tool to gain a more powerful position within a social movement network. In addition to there being little empirical research on social movements within writing studies and social movement rhetoric, there is a scarce body of literature that addresses how conservative social movements work. For many, the election of Donald Trump on the heels of our first black president has revealed surprising facts about our culture as fears about immigration, gun control, and abortion have been inflamed. Political debates about race, climate change, voter suppression, and reproductive rights restrictions make the study of conservative rhetorical tools even more critical. Using one prominent pro-life lobbying and social movement organization as my specifying site, this dissertation study aims to understand what motivates, influences, and facilitates a social movement. What entanglement of ideology, circumstances, and personal attachments exist within an activist organization, and how do these factors influence the language and delivery methods of such defining documents as mission statements, donor letters, legislation, a (open full item for complete abstract)

Committee: Derek Van Ittersum (Committee Chair); Sara Newman (Committee Member); Pamela Takayoshi (Committee Member); Daniel Skinner (Committee Member) Subjects: Organization Theory; Rhetoric; Social Research

Master of Science, The Ohio State University, 2021, Environment and Natural Resources

Modern environmental problems pose unique management challenges since they are usually interdependent in myriad, complex ways. Climate change is the ultimate example of a problem that forces environmental managers to confront highly interdependent challenges, such as invasive species, rising temperatures, and habitat loss. A growing area of interest in understanding complex, polycentric governance systems has been to analyze the engagement of stakeholders in policy issues and the participation of stakeholders in policy forums. In this thesis, I focus on climate change adaptation governance in Ohio, USA as a model study system to evaluate conditions and incentives that drive actors to manage for interdependent issues or to participate in forums in ways that are collectively beneficial. To answer questions about actor management strategies in complex, polycentric governance arrangements, I analyze climate change governance as a three-mode network of interrelations among actors, forums, and policy issues related to climate change adaption in Ohio. I draw upon the Ecology of Games Theory (EGT) and an Institutional Fitness framework to formulate hypotheses that uncover the conditions, incentive structures, and attributes that prompt actors to engage with issues and participate in forums in ways that promote adaptive capacity. Chapter 2 tests whether actors are likely to simultaneously manage environmental policy issues that are highly interdependent (such as nutrient management and water quality, which are connected through the process of eutrophication). Then, Chapter 3 tests for how different types of theorized closure structures (i.e., unique situations of actor benefits) – lead actors to participate in decision-making forums. To tackle the questions at hand, both chapters utilize Exponential Random Graph Models (ERGMs), which is a tool for inferential network analysis. The results indicate that actors are more likely to manage for pairs of interdependent polic (open full item for complete abstract)

Committee: Ramiro Berardo Ph.D. (Advisor); Matthew Hamilton Ph.D. (Advisor); Jeremy Brooks Ph.D. (Committee Member) Subjects: Environmental Management; Environmental Studies; Natural Resource Management

MS, University of Cincinnati, 2020, Education, Criminal Justice, and Human Services: Information Technology

Literature has shown that social groups play an important role in the ways that individuals learn about and change behaviors related to privacy and security management on digital devices. The term tech caregiver has recently been used to describe individuals that oer direct support to friends and family in need of help managing digital devices. This thesis investigates the role of these tech caregivers to support privacy and security management in small groups. To do this, 112 individuals were surveyed across the United States of America. These 112 participants belonged to 20 small groups comprising of technology caregivers and the technology caregivees. The results show that technology caregivers tend to be younger adults (age 19-25). Technology caregivers reported significantly higher levels of self-ecacy for privacy and security and power usage than technology caregivees. Qualitative feedback shows that participants primarily used text messages and phone calls to communicate to receive support on the topics of troubleshooting and device setup and the explanation of a new device. This work helps to characterize the role of technology caregivers within small groups when it comes to social support for digital privacy and security and describes design implications for creating a mobile platform that supports the work of tech caregivers in their social groups.

Committee: Jess Kropczynski Ph.D. (Committee Chair); Shane Halse Ph.D. (Committee Member) Subjects: Information Technology

Master of Arts, The Ohio State University, 2021, Psychology

Past measurements of the self-concept fail to adequately capture the full extent of the construct. In an attempt to establish a novel measurement of the self-concept, participants generated a network of self-relevant aspects and described the extent to which they were connected. Researchers then used social network analysis methodologies to quantify these networks. We failed to find any evidence to support the validity of this new measure. These metrics do not correlate significantly with any previous measure that we used. Potential limitations and future directions are discussed.

Committee: Steven Spencer PhD (Advisor); Baldwin Way PhD (Committee Member); Russell Fazio PhD (Committee Member) Subjects: Social Psychology

Master of Science (MS), Ohio University, 2020, Clinical Psychology (Arts and Sciences)

Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common disorders among school-aged children, and has been shown to be strongly associated with marked academic and social impairment. Although interventions have been developed to improve social deficits in children with ADHD, few successfully change peer perceptions of these children, even when ADHD behaviors and symptoms improve. The Making Socially Accepting Inclusive Classrooms (MOSAIC) program, a teacher-implemented classroom intervention, was developed to specifically target peer perceptions of students with ADHD and social impairment. This pilot study aimed to explore whether teachers (N=12) who implemented the MOSAIC intervention over the course of a school year experienced change in the classroom social network, especially with regard to target students (N=43) with elevated ADHD symptoms and social impairment. In-degree centrality, out-degree centrality, alter-based centrality, and classroom density were calculated using Social Network Analysis (SNA). T-tests were conducted to evaluate whether social outcomes for target students differed from their typically-developing peers and to assess whether change occurred in classroom social networks from fall to spring. Results indicated that change in social outcomes for targets students did not differ significantly from change experienced by peers. Results for change in classroom networks were mixed. Correlational analyses that examined the relationship between teacher integrity to the MOSAIC strategies and change in SNA metrics were also mixed. Implications and future directions are discussed.

Committee: Julie Owens Dr. (Committee Chair) Subjects: Psychology

Doctor of Philosophy (PhD), Ohio University, 2020, Industrial and Systems Engineering (Engineering and Technology)

A supply chain is a network of suppliers, production, or manufacturing facilities, retailers, and transportation channels which are structured to acquire supplies, produce new products, and distribute the finished products to retailers and customers. Closed Loop Supply Chain (CLSC) networks incorporate the flow of the returned, used, or recycled products from the customers through the retailers to the manufacturing, recycling, or refurbishing facilities to support managing the full lifecycle of the products. Social Network Analysis (SNA) has been developed to identify and analyze the patterns in social networks. SNA is used as a theoretical framework for better understanding of social networks by characterizing the structure of a network in terms of nodes and links. SNA is applied to various types of networks including telecommunication networks, protein interaction networks, animal disease epidemics, and customer interaction and analysis. Although SNA is a powerful method to study networks in many areas, it has not been comprehensively applied to supply chain networks. Likewise, there is no application and interpretation of SNA metrics in CLSCs. In this study, SNA metrics are introduced and interpreted for components in CLSC networks and forward and reverse logistic activities. Correspondingly, a decision making tool is developed based on selected SNA metrics for comparing alternative network designs in terms of network reliability and balance of the flows.

Committee: Saeed Ghanbartehrani (Advisor); Gary Weckman (Committee Member); Tao Yuan (Committee Member); Vardges Melkonian (Committee Member); Benjamin Sperry (Committee Member) Subjects: Industrial Engineering; Information Technology; Management

Master of Science, Miami University, 2020, Computer Science and Software Engineering

Online discussion forums are increasingly used in large classrooms to enhance students' collaboration, promote student engagement and measure academic performances. Although many Computer-Supported Collaborative Learning (CSCL) tools are available, only a few researchers in the past have investigated the limitations of unstructured discussion forums. The linearly structured threads of discussion posts in these discussion forums make it difficult to represent and analyze the student interaction patterns. Modeling these discussion forums as collaboration networks enables the analysis of students' interaction and learning behavior. This thesis work demonstrates the need for and value of innovative network models that involve developing more structured, schema-based, and goal-oriented discussion forums constructed from active student collaborations. The schema-based structured network model enables identifying and measuring the influence that the student interactions have in dynamically evolving student collaboration networks as the course progresses. First, the unstructured discussion boards in Canvas are analyzed using graph mining and social network techniques. Then, the structured collaborative network model is used to study the impact that student collaborations have on knowledge acquisition, persistence and course outcomes using machine learning algorithms. These structured discussions enable the analysis of the changes in student collaboration patterns and belongingness for pedagogical benefits.

Committee: Vijayalakshmi Ramasamy (Advisor); James D. Kiper (Committee Member); Hakam W. Alomari (Committee Member) Subjects: Computer Science; Education

Master of Science, The Ohio State University, 2020, Environment and Natural Resources

Harmful algal blooms (HABs), have been a growing issue in Lake Erie since the 1990s. The blooms, composed of toxin-producing cyanobacteria, are primarily caused by nutrient runoff in the form of phosphorus and nitrogen from agricultural lands around the Lake. HABs in Lake Erie have become an especially salient issue after the August 2014 Toledo Water Crisis, in which 500,000 people in the Toledo Metropolitan Area were deprived of the use of their tap water due to a `do not drink' advisory prompted by toxins in the water originating from a HAB. In drawing an explosion of attention to HABs, the 2014 algal bloom functioned as a focusing event. A focusing event is an event, concentrated in a particular geographical area, that causes harm or reveals the potential for harm to human communities. To understand how the 2014 Toledo Water Crisis affected policy change processes implicated in managing Lake Erie, I investigate two issues pertaining to how this attention changed HAB management: first, the ways the crisis changed the social networks of the stakeholders involved in water quality management; and second, the ways that the crisis altered narratives about HABs. To address HABs, coordination across jurisdictions and the various levels of government is essential. Actors faced with these fragmentations of management built into government face a collective action problem and must coordinate their actions to compensate for this fragmentation. Understanding how the 2014 algal bloom as a focusing event altered social networks associated with water quality management in Lake Erie will help reveal the conditions under which coordination and collective action may arise. As a part of this process of addressing HABs, narrative will also be an important aspect. Policy debates are fought using narrative, and narrative affects the policy process. Understanding the narratives actors employ at a moment in which quickly mobilizing resources and people is essential can elucidate how acto (open full item for complete abstract)

Committee: Ramiro Berardo (Advisor); Saatvika Rai (Committee Member); Matthew Hamilton (Committee Member); Jeremy Brooks (Committee Member) Subjects: Environmental Studies; Water Resource Management

Master of Computer Science, Miami University, 2020, Computer Science and Software Engineering

Network analysis techniques help investigate the significance of nodes/actors that play central roles where the nodes represent people, and the links represent the communication between them. This thesis analyzes how collaboration helps students' learning process and proposes a tool that could be integrated with Canvas to analyze student discussion data. To begin, we analyzed data collected from online student discussions on Canvas, in a Level-1 Programming course. These discussion topics were classified into classroom experiences/learning, question/answers, opinions, and comments. Modeling of the patterns of discussion board interactions as networks and applying various node-based network measures helped to unravel the similarities of student interaction patterns, and gain insights into their progress in the course. The experimental analyses include finding the most challenging/debated topics in the course, analyzing the leadership and team-based qualities, and analyzing trends in student participation. The results of the study reveal that participation in online discussion forums has a positive impact on the students' grades. In summary, the inferences drawn from this research can help instructors understand the student learning behaviors/patterns and guide the development of better pedagogical approaches that benefit students to overcome the common misconceptions that they confront in the course concepts.

Committee: Vijayalakshmi Ramasamy (Advisor); James Kiper (Committee Member); Hakam Alomari (Committee Member) Subjects: Computer Science; Education

PHD, Kent State University, 2019, College of Communication and Information

The study explores the personal knowledge networks of professional librarians to understand how current career stage and individual career motivations may influence characteristics of personal knowledge networks. any observed differences. Various social network attributes including size, direction of contact, and boundary spanning relationships were explored to understand: (1) how social networks differ based on early, mid, and late career stages; (2) how differing career motivations impact the growth and development of social networks, and (3) whether gender plays a significant role in how social networks develop. Data about the social networks that support librarians in performing their work were collected from a sample of 280 librarians employed in different types of libraries within the U.S. Additionally, the short form Career Orientation Inventory (COI) developed by Igbaria and Baroudi (1993) based on the earlier work of Schein and DeLong was employed to measure career motivations. Multiple quantitative tests were run on the data, including non-parametric, ANOVA-equivalent tests and simultaneous linear regressions. Results demonstrate a tentative link between career stage and the network attributes direction of contact, as well as certain boundary spanning relationships. Social network analysis (SNA) shows promise as an untapped methodology for exploring career development within librarianship and knowledge network assessment via SNA was demonstrated to provide valuable insight to practicing librarians.

Committee: Miriam Matteson (Advisor) Subjects: Communication; Social Research

Doctor of Philosophy, The Ohio State University, 2019, Political Science

Social influence shapes the political opinions people form and the norms they adopt. I show that three key types of social networks drive the type of social influence people face: fully-connected deliberative networks, social hierarchies based on status, and star networks that group up around a central opinion leader. In chapter one, I lay the foundation for thinking of public opinion as emerging from people's social structures. I then apply the theory to international norm adoption and show that the type of network an individual is placed in has a direct effect on the norms they adopt, over and above partisanship. In chapter two, I use a custom-made Twitter-like environment to show experimentally that hierarchies foster the adoption of partisan-leaning norms as members pursue status. Stars, in contrast, inoculate their members against false claims due to the reputations costs opinion leaders face in misleading their followers. In chapter three, I expand on these findings by priming subjects in a survey experiment with an image of their social structure, and show that placing a political independent in a social hierarchy at the individual-level makes them favor U.S. isolationism, international competition in domains such as trade, and overall unilateralism on the world stage. The conclusion of these two studies is clear: social hierarchies prime competitive political thinking, often leading to the adoption of norms based on false premises, and star networks help their followers sift through the noise and misinformation that prevails in online fully-connected networks. These findings highlight the importance of viewing norm adoption and opinion formation as a social endeavor that is deeply influenced by one's reference network. As online social networks continue to expand, identifying the types of networks that characterize these social environments becomes imperative for students of public opinion and international relations that seek to understand why some norms an (open full item for complete abstract)

Committee: Skyler Cranmer (Committee Chair); Christopher Gelpi (Committee Member); Richard Herrmann (Committee Member); Jon Krosnick (Committee Member) Subjects: International Relations; Political Science

MS, University of Cincinnati, 2019, Engineering and Applied Science: Computer Science

Community detection in Social Networks has been a major research interest in recent years. In graphical community detection, the principal consideration is the connection between users in the network data. On the other hand, document clustering is a paradigm where text documents are clustered together based on their textual properties. In this thesis, we have used document clustering techniques on data collected from the social networking site, Twitter to cluster the users associated with them. We then compared the user clusters formed by the document clustering techniques and compared them with the communities detected in the graphical representation to investigate the possibilities of any correlation between these two methods. We utilized tools such as NodeXL and Gephi for collecting and visualizing the network data respectively. For user clustering based on their tweets, we used four different feature representation techniques and two clustering algorithms.

Committee: Anca Ralescu Ph.D. (Committee Chair); Kenneth Berman Ph.D. (Committee Member); Dan Ralescu Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2019, Educational Studies

One of the major challenges that students in the 21st century have faced is the need to reconcile various perspectives in the increasingly complex and interconnected world. Epistemic cognition—the process of thinking about what counts as knowledge and the process of knowing— plays an important role in enabling people to critically examine their understanding about the social world (i.e., social knowledge). Research has suggested that students lacking sophisticated social knowledge are vulnerable to negative social experiences, such as bullying or victimization, which can lead to long-term detrimental life-course outcomes. To this end, the major research gap in this field is the lack of scholarly understanding about the nature and development of epistemic cognition about social knowledge. The overarching aim of this study was to unpack the process by which early adolescents develop epistemic cognition about social knowledge. Based on 12 small groups' (63 fifth-grade students) discussions performed at three time points (a total of 36 discussions), this study investigated 1) the ways by which networks of epistemic cognition about social knowledge operate; 2) the impact of collaborative small group dialogic inquiry on the development of epistemic cognition about social knowledge; and 3) the associations between students' epistemic cognition about social knowledge and their social reasoning development. To examine how students' epistemic cognition worked and developed within the context of group discussions through the continued participation in collaborative small group dialogic inquiry, this study applied a network analysis approach called Epistemic Network Analysis, along with qualitative coding of discussions and quantitative analyses. The findings showed that 1) epistemic cognition about social knowledge constructed during small group discussions were connected epistemic networks; 2) the collaborative small group dialogic inquiry activity was effective in pro (open full item for complete abstract)

Committee: Tzu-Jung Lin (Committee Chair); George Newell (Committee Member); Michael Glassman (Committee Member) Subjects: Educational Psychology