Bachelor of Arts, Wittenberg University, 2021, Political Science

The 2019 Mueller Report revealed a campaign by the Russian Internet Research Agency to influence the outcome of the 2016 U.S. presidential election and insert systemic distrust in Western democracy. The campaign engaged in “information warfare” using false accounts, or bots, to prey on inherent social vulnerabilities that are amplified by the novelty and anonymity of social media, such as echo chambers and the rapid spread of fake news. This thesis explores the aims, methods, effects, and behavioral patterns of bots. It also proposes BotWise, a model designed to distill average behavior on the social media platform Twitter from a set of real users and compare that data against novel input.

Committee: Tyler Highlander (Advisor); Staci Rhine (Advisor); Alyssa Hoofnagle (Committee Member); Yu Bin (Committee Member) Subjects: Computer Science; Political Science; Sociology; Technology

Master of Applied Communication Theory and Methodology, Cleveland State University, 2018, College of Liberal Arts and Social Sciences

With the rise of technology it becomes important to measure and analyze the communication patterns that are emerging from these changes. Technologies open up different communication patterns for individuals to use (Tomas & Carlson 2015; Walther, 1996; Wei & Leung, 1999). Thus, this study develops the “ASOHIO” perspective, which incorporates a range of new and old communication patterns, online communication, offline communication, synchronous communication, asynchronous communication, interpersonal communication, and hyperpersonal communication. This work also looks to extend the hyperpersonal model greatly by developing an actual multi-item scale to measure the construct at the individual level. Walther's (1996) basic description of hyperpersonal communication breaks down that there are a lack of non-verbal cues, a sense of strategic communication, and computer-mediated communication. This study takes things a step further, with a breakdown of the components of hyperpersonal taking into account current technologies, as well as using Goffman's “presentation of everyday self“ and “interaction ritual” to help define what hyperpersonal could really mean in the current hybrid communication environment.

Committee: Kimberly Neuendorf Dr. (Committee Chair); Guowei Jian Dr. (Committee Member); Leo Jeffres Dr. (Committee Member) Subjects: Communication

PHD, Kent State University, 2018, College of Arts and Sciences / Department of Computer Science

Social Routing in Online Social Networks (OSNs) is very challenging, as it must handle privacy and performance. This study proposes a Social Online Routing (SOR) protocol for OSNs that satisfies Stratified Privacy Model (SPM) core requirements and minimizes end-to-end routing delays corresponding to the social routing information elements exchanged under the SPM. SOR uses five messages (I-need Message, I-have Message, I-thank Message, I-like/dislike message, and the I-Ack Message) for carrying routing information. Forwarding models (I-need Module, I-have Module, I-thank Module, and I-ack Module) and routing algorithms (Topology aware Shortest-Path-Based routing algorithm, Social-Priority-Based routing algorithm, and Queue-aware Social-Priority-Based routing algorithm) are introduced. Four anonymization techniques are also utilized for stratified privacy. To evaluate the study's proposed protocol, an Online Social Networks Simulator is designed and implemented. Using real datasets from Google Plus, the simulator is used to evaluate end-to-end routing delays corresponding to the social routing information elements exchanged under the SPM.

Committee: Javed Khan Prof. (Advisor) Subjects: Computer Science

Master of Arts (MA), Ohio University, 2010, Sociology (Arts and Sciences)

Family SES has the potential to shape the opportunities, resources and life trajectory of college students. This study examines the effects of SES on college students‟ social capital through an online survey and innovative Facebook application measuring students‟ social networks. Participants were recruited through class visits and emails. Regression analyses measured the effects of SES on three measures of students‟ social capital, operationalized using online network data: general social capital (network size), bridging social capital (number of clusters), and bonding social capital (average degree). Students that had higher SES had larger networks with more ties per actor within the individual‟s network (average degree). Students from lower SES backgrounds had smaller networks with fewer ties per actor within ego‟s network. The effects of SES on social capital have never been studied in an online setting, and this study provides good evidence that more substantial research in the online environment can and should take place in the future.

Committee: Howard T. Welser PhD (Committee Chair); Robert Shelly PhD (Committee Member); Joseph De Angelis PhD (Committee Member); Scott Titsworth PhD (Committee Member) Subjects: Sociology

Doctor of Philosophy, The Ohio State University, 2013, Computer Science and Engineering

Recent years have witnessed the success of a number of online social networks (OSNs) and explosive increasing of social media. These social networking and social media sites have attracted a significant number of participants that contribute various types of contents on the Internet, which are generally referred as user generated content (UGC). A well designed UGC network can utilize the wisdom of crowds to collect, organize, and vote user contributed content to generate high quality knowledge with a relatively low cost. However, the open environment of UGC system also makes it easy to be polluted and attacked by spammers and malicious users. How users participate in UGC networks, especially how users contribute content and share content with their friends and other users, is fundamental to spam detection and high quality knowledge discovery. In this dissertation, we investigate two important research issues: (1) discovering user content generation patterns in OSNs, focusing on publicly available content (knowledge sharing), and (2) detecting spam in user generated content based on our discovered patterns. With the access to three large OSN user activity logs, including Yahoo! Blogs, Yahoo! Answers, and Yahoo! Del.icio.us, for a duration of up to 4.5 years, we are able to well analyze the patterns of content generation patterns of social network users in detail. Our analysis consistently shows that users' posting behavior in these networks exhibits strong daily and weekly patterns, but the user active time in these OSNs does not follow commonly assumed exponential distributions. We also show that the user posting behavior in these OSNs follows stretched exponential distributions instead of widely accepted power law distributions. Our discovery lays a foundation for user behavior analysis in social networks, and serves as a ground truth for anomaly detection and anti-spam. Applying the user posting behavior distribution pattern, w (open full item for complete abstract)

Committee: Xiaodong Zhang (Advisor); Feng Qin (Committee Member); Ten H. (Steve) Lai (Committee Member) Subjects: Computer Engineering; Computer Science

Doctor of Philosophy (PhD), Wright State University, 2017, Computer Science and Engineering PhD

As the Internet has grown increasingly popular as a communication and information sharing platform, it has given rise to two major types of Internet security threats related to two primary entities: end-users and network services. First, information leakages from networks can reveal sensitive information about end-users. Second, end-users systems can be compromised through attacks on network services, such as scanning-and-exploit attacks, spamming, drive-by downloads, and fake anti-virus software. Designing threat assessments to detect these threats is, therefore, of great importance, and a number of the detection systems have been proposed. However, these existing threat assessment systems face significant challenges in terms of i) behavioral diversity, ii) data heterogeneity, and iii) large data volume. To address the challenges of the two major threat types, this dissertation offers three unique contributions. First, we built a new system to identify network users via Domain Name System (DNS) traffic, which is one of the most important behavior-based tracking methods for addressing privacy threats. The goal of our system is to boost the effectiveness of existing user identification systems by designing effective fingerprint patterns based on semantically limited DNS queries that are missed by existing tracking efforts. Second, we built a novel system to detect fake anti-virus (AV) attacks, which represent an active trend in the distribution of Internet-based malware. Our system aims to boost the effectiveness of existing fake AV attack detection by detecting fake AV attacks in three challenging scenarios: i) fake AV webpages that require user interaction to install malware, instead of using malicious content to run automatic exploitation without users consent (e.g., shellcode); ii) fake AV webpages designed to impersonate real webpages using a few representative elements, such as the names and icons of anti-virus products from authentic anti-virus webpages (open full item for complete abstract)

Committee: Junjie Zhang Ph.D. (Advisor); Adam Robert Bryant Ph.D. (Committee Member); Bin Wang Ph.D. (Committee Member); Xuetao Wei Ph.D. (Committee Member) Subjects: Computer Science

Master of Science in Computer Engineering (MSCE), Wright State University, 2016, Computer Engineering

Online social systems have emerged as a popular medium for people in society to communicate with each other. Among the most important reasons why people communicate is to share emotional problems, but most online social systems are uncomfortable or unsafe spaces for this purpose. This has led to the rise of online emotional support systems, where users needing to speak to someone can anonymously connect to a crowd of trained listeners for a one-on-one conversation. To better understand who, how and when users utilize these systems, and to evaluate their safety, this thesis offers a comprehensive examination of the characteristics of users and their interactions from a massive, leading emotional support platform. From a big data set of millions of conversations across hundreds of thousands of users, the study employs statistical measurement techniques and predictive analytics to shed light about the ways these platforms are utilized, and the extent to which users behave in un-wanting ways. The analysis leads to recommendations on promoting positive system utilization and an understanding of the effectiveness of protections in place to thwart emotional attacks. This work is likely the first to measure the activities and interactions in an online social system for emotional support.

Committee: Derek Doran Ph.D. (Advisor); Junjie Zhang Ph.D. (Committee Member); Tanvi Banerjee Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science

Doctor of Philosophy, The Ohio State University, 2016, Sociology

The concept that social networks impact individual and organizational choices is as old as Sociology itself. Theorist from Durkheim to Simmel, and Weber to Parsons have all struggled with how to quantify and measure the real or imagined influence of social structures on individual choice. Network analysis proceeds from a similar framework as structuralism in that is assumes that the choices of one individual will be constrained by their place within the broader interconnectivity of the other actors. Interpretation then is based on the assumption that one's networks can be categorized, and that the meaning derived from that network are the same for all individuals within it. Several theoretical papers call these assumptions into question, but researchers examining online networks, especially from an international social movement perspective, have yet to examine their methods to verify that they capture the full extent of online networks, and if everyone associated with the network understands their place in it the same way. Our ability to gather and analyze data far outstrips our theorizing. In this dissertation I will examine the assumptions about what constitutes a network based on current collection techniques and show the current methods produce a systemic bias that cannot account for the entire issue based network, leading to errors in interpretation and the false identification of movement leaders. The new method, called Query Driven Sampling (QDS), uses webmaster tools to accurately record the missing inbound links and more fully complete the network compared to the outbound/co-link method. The second chapter will examine online Kurdish activism using the QDS method. With this method I demonstrate how online social activists within diasporic settings react differently to perceived risk even within the same ethnic community. In the final chapter I ask Kurdish activists to explain from their perspective, how they conceptualize, interact and grow their activist (open full item for complete abstract)

Committee: Craig Jenkins Ph.D. (Committee Chair); Andrew Martin Ph.D. (Committee Member); Vincent Roscigno Ph.D. (Committee Member) Subjects: Sociology

Doctor of Philosophy, The Ohio State University, 2015, Computer Science and Engineering

Online social networks (OSNs) allow Internet users all over the globe to share information, exchange thoughts, and work collaboratively. Not only do OSNs provide a channel of broadcasting real-world events as they unfold, they also enable a convenient way for users to exchange experience and opinions. Understanding the relation among network topology, users, content, and their dynamics can have a significant impact both from a theoretical standpoint as well as from a practical one, for instance, to understand online user behaviors and predict future online activities. In this dissertation, I study the interplay of three important factors that encode most of the OSN dynamics: network structure, user-generated content, and user characteristics. We first present our broader contribution to computer science: the development of two novel graph algorithms for community detection and structural role detection, which are scalable to handle networks containing millions of nodes and edges. Both community and role assignments of nodes generate novel clusterings of OSN users and provide valuable insights into OSN activities, but they are often implicit or even unknown to OSN analysts. We bridge this chasm by designing algorithms that can automatically infer community and role information in large-scale OSN data. Our algorithms are (1) robust in the presence of noise in real-world data, and (2) efficient in processing large network datasets. A key element to both of these contributions is a practical approach for network sparsification which enables efficient processing. Evaluated on various social networks containing hundreds of millions of edges, our algorithms outperform state-of-the-art approaches in terms of the ability of recovering ground truth communities and roles of OSN users. By augmenting the network structure with content information and performing joint inference, our algorithms are able to combat the impact of noise. At the same time, careful design and optim (open full item for complete abstract)

Committee: Srinivasan Parthasarathy (Advisor); P Sadayappan (Committee Member); Arnab Nandi (Committee Member); Robert Garrett (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Ohio University, 2008, Mass Communication (Communication)

The aim of this research was to better understand virtual online communities (VOCs), that is, communities that are formed and maintained through the Internet. This research was guided by four research questions: What do participants in VOCs actually seek? How does a participant critically evaluate information produced in VOCs? What differences do VOC members perceive between their online community experiences compared to their experiences in real-life face-to-face communities? In what ways might a VOC shape its members' views toward political and social change? The methodology employed was participant observation of 20 informants within their online and offline realms plus in-depth interviews with each informant. Interviews and observations were conducted from 2005 - 2007. This research identified two different types of VOCs: dependent and self-contained VOCs. Dependent VOCs act as extensions to already existent face-to-face communities while self-sustained VOCs are communities where relationships between members are formed, developed, and nurtured purely through virtual encounters on the Internet based on shared interests. Four functions were identified in this study: information exchange, social support exchange, friendship, recreation. Information exchange is a function where the VOC main purpose was to provide information for members. Social support refers to the degree to which a person's basic social needs are gratified through interaction with others. Friendship are formed within the VOC not only for social support, but also provide deeper, more meaningful relationships. Recreation within a VOC occurs when the community's main purpose is purely entertainment. This study also identified six motivations: accessibility/convenience, escapism, alternate identities, social recognition, voyeurism, written communication as a medium. Three issues in VOCs also emerged in this study: trust, evaluation of online material, and marginalized communities. VOCs exist in a (open full item for complete abstract)

Committee: Drew McDaniel Ph.D. (Committee Chair); Elizabeth Collins Ph.D. (Committee Member); Robert Stewart Ph.D. (Committee Member); Don Flournoy Ph.D. (Committee Member) Subjects: Communication

Master of Arts, Miami University, 2008, Mass Communication

This paper examines the online social networks, and the negative implications that have surfaced as a result of misunderstanding the purpose of the website. Using the concepts of identity, community, and disclosure, three real-life Facebook situations are described, discussed, and analyzed. The film, The Net is incorporated as a foundational template in discussing the similarities and warnings about the potential risks of online social networks. In addition, this paper examines how Facebook is redefining the areas of communication, identity and community.

Committee: Bruce Drushel PhD (Committee Chair); Kathy German PhD (Committee Member); Ron Scott PhD (Committee Member) Subjects: Communication; Mass Media