Other Computer Sciences Commons^™

The Locals Casino As A Social Network – Can An Interconnected Community Of Players Detect Differences In Hold?, Jason D. Fiege, Anastasia (Stasi) D. Baran

International Conference on Gambling & Risk Taking

Abstract

It is difficult for individual players to detect differences in theoretical hold between slot machines without playing an unrealistically large number of games. This difficulty occurs because the fractional loss incurred by a player converges only slowly to the theoretical hold in the presence of volatility designed into slot pay tables. Nevertheless, many operators believe that players can detect changes in hold or differences compared to competition, especially in a locals casino market, and therefore resist increasing holds. Instead of investigating whether individual players can detect differences in hold, we ask whether a population of casino regulars who share …

Influence Level Prediction On Social Media Through Multi-Task And Sociolinguistic User Characteristics Modeling, Denys Katerenchuk

Dissertations, Theses, and Capstone Projects

Prediction of a user’s influence level on social networks has attracted a lot of attention as human interactions move online. Influential users have the ability to influence others’ behavior to achieve their own agenda. As a result, predicting users’ level of influence online can help to understand social networks, forecast trends, prevent misinformation, etc. The research on user influence in social networks has attracted much attention across multiple disciplines, from social sciences to mathematics, yet it is still not well understood. One of the difficulties is that the definition of influence is specific to a particular problem or a domain, …

Jlootbox: An Agent-Based Model Of Social Influence And Gambling In Online Video Games, Lila Zayed

Honors Capstones

Loot boxes are digital treasure chests that players spend real money to purchase, wherein the contents are randomly generated. Since players spend money on the pretense they might receive something valuable, many comparisons have been drawn to gambling behavior as the reward is up to chance. To explore this phenomenon, agent-based modeling will be used to simulate this behavior. Agent-based modeling allows us to create heterogenous agents who follow simple rules so that we may observe emergent behavior in a population. An agent-based model was created using Repast Simphony for this end.

Parameters included the player’s internal decision strategy around …

Codis: Community Detection Via Distributed Seed-Set Expansion On Graph Streams, Austin Anderson

Master's Projects

Community detection has been and remains a very important topic in several fields. From marketing and social networking to biological studies, community detec- tion plays a key role in advancing research in many different fields. Research on this topic originally looked at classifying nodes into discrete communities, but eventually moved forward to placing nodes in multiple communities. Unfortunately, community detection has always been a time-inefficient process, and recent data sets have been simply to large to realistically process using traditional methods. Because of this, recent methods have turned to parallelism, but all these methods, while offering sig- nificant decrease in …

Overlapping Community Detection In Social Networks, Akshar Panchal

Master's Projects

Social networking sites are important to connect with the world virtually. As the number of users accessing these sites increase, the data and information keeps on increasing. There are communities and groups which are formed virtually based on different factors. We can visualize these communities as networks of users or nodes and the relationships or connections between them as edges. This helps in evaluating and analyzing different factors that influence community formation in such a dense network. Community detection helps in revealing certain characteristics which makes these groups in the network unique and different from one another. We can use …

Tsar : A System For Defending Hate Speech Detection Models Against Adversaries, Brian Tuan Khieu

Master's Projects

Although current state-of-the-art hate speech detection models achieve praiseworthy results, these models have shown themselves to be vulnerable to attack. Easy to execute lexical manipulations such as the removal of whitespace from a given text create significant issues for word-based hate speech detection models. In this paper, we reproduce the results of five cutting edge models as well as four significant evasion schemes from prior work. Only a limited amount of evasion schemes that also maintain readability exists, and this works to our advantage in the recreation of the original data. Furthermore, we demonstrate that each lexical attack or evasion …

Relationships Between Social Network Characteristics, Alcohol Use, And Alcohol-Related Consequences In A Large Network Of First-Year College Students: How Do Peer Drinking Norms Fit In?, Graham T. Diguiseppi, Matthew K. Meisel, Sara G. Balestrieri, Miles Q. Ott, Melissa A. Clark, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

A burgeoning area of research is using social network analysis to investigate college students' substance use behaviors. However, little research has incorporated students' perceived peer drinking norms into these analyses. The present study investigated the association between social network characteristics, alcohol use, and alcohol-related consequences among first-year college students (N 1,342; 81% of the first-year class) at one university. The moderating role of descriptive norms was also examined. Network characteristics and descriptive norms were derived from participants' nominations of up to 10 other students who were important to them; individual network characteristics included popularity (indegree), network expansiveness (outdegree), relationship reciprocity, …

Strategic Players For Identifying Optimal Social Network Intervention Subjects, Miles Q. Ott, John M. Light, Melissa A. Clark, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

We present a method whereby social network ties are used to identify behavioral leaders who are situated in the network such that these individuals are: 1) able to influence other individuals who are in need of and most receptive to intervention, thereby optimizing the impact of the intervention; and 2) not embedded with ties to individuals that are likely to be behaviorally antagonistic to the intervention or that would compromise the optimal impact of intervention. In this study we developed a method that we call Strategic Players, which is a solution for identifying a set of players who are close …

(Dis)Enchanted: (Re)Constructing Love And Creating Community In The, Shannon A. Suddeth

USF Tampa Graduate Theses and Dissertations

This thesis examines a queer fan community for the television show Once Upon a Time (OUAT) that utilizes the social networking site Tumblr as their primary base of fan activity. The Swan Queen fan community is comprised of individuals that collectively support and celebrate a non-canon romantic relationship between two of the female lead characters of the show rather than the canonic, heterocentric relationships that occur between the two women and their respective male love interests. I answer two research questions in this study: First, how are members of the Swan Queen fan community developing counter narratives of …

Recommender Systems Research: A Connection-Centric Survey, Saverio Perugini, Marcos André Gonçalves, Edward A. Fox

Saverio Perugini

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from …

A Partner-Matching Framework For Social Activity Communities, Chunyu Ai, Wei Zhong, Mingyuan Yan, Feng Gu

Publications and Research

A lot of daily activities require more than one person to participate and collaborate with each other; however, for many people, it is not easy to find good partners to engage in activities with one another. With the rapid growth of social network applications, more and more people get used to creating connections with people on the social network. Therefore, designing social network framework for partner-matching is significant in helping people to easily find good partners. In this paper, we proposed a framework which can match partners for an active community. In order to improve the matching performance, all users …

A Trust-Aware System For Personalized User Recommendations In Social Networks, Magdalini Eirinaki, Malamati Louta, Iraklis Varlamis

Faculty Publications

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networking applications, microblogging, or customer review sites. In this environment, trust is becoming an essential quality among user interactions and the recommendation for useful content and trustful users is crucial for all the members of the network. In this paper, we introduce a framework for handling trust in social networks, which is based on a reputation mechanism that captures the implicit and explicit connections between the network members, analyzes the semantics and dynamics of these …

Recommender Systems Research: A Connection-Centric Survey, Saverio Perugini, Marcos André Gonçalves, Edward A. Fox

Computer Science Faculty Publications

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from …