Current Search: social influence (x)
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(1 - 7 of 7)
- Title
- WHAT DO YOU THINK OF OTHERS WHO PURSUE COSMETIC SURGERY? INFLUENCES ASSOCIATED WITH PERCEPTIONS OF COSMETIC SURGERY.
- Creator
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Vergara, Angela, Negy, Charles, University of Central Florida
- Abstract / Description
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In the current climate in which it seems like popular media determines normality, it is not surprising to find that reality television, especially programs geared towards elective cosmetic surgery, are correlated with the decision making processes associated with actually pursuing cosmetic surgery. Research suggests that attitudes towards cosmetic surgery have changed dramatically due to the public's exposure to reality makeover shows; these shows have increased the popularity of such...
Show moreIn the current climate in which it seems like popular media determines normality, it is not surprising to find that reality television, especially programs geared towards elective cosmetic surgery, are correlated with the decision making processes associated with actually pursuing cosmetic surgery. Research suggests that attitudes towards cosmetic surgery have changed dramatically due to the public's exposure to reality makeover shows; these shows have increased the popularity of such procedures and have highlighted and implied that cosmetic surgery is associated with little pain and risk. In this study, I sought to determine if attitudes toward cosmetic surgery vary as a function of ethnicity and gender, as well as examine the influence of the media on openness to pursuing cosmetic surgery. Examining how others view those who pursue elective cosmetic surgery and the variables associated with those who obtain cosmetic surgery will shed light on the processes associated with the decision to pursue the procedures.
Show less - Date Issued
- 2012
- Identifier
- CFH0004137, ucf:44887
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004137
- Title
- SPARSIFICATION OF SOCIAL NETWORKS USING RANDOM WALKS.
- Creator
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Wilder, Bryan, Sukthankar, Gita, University of Central Florida
- Abstract / Description
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Analysis of large network datasets has become increasingly important. Algorithms have been designed to find many kinds of structure, with numerous applications across the social and biological sciences. However, a tradeoff is always present between accuracy and scalability; otherwise promising techniques can be computationally infeasible when applied to networks with huge numbers of nodes and edges. One way of extending the reach of network analysis is to sparsify the graph by retaining only...
Show moreAnalysis of large network datasets has become increasingly important. Algorithms have been designed to find many kinds of structure, with numerous applications across the social and biological sciences. However, a tradeoff is always present between accuracy and scalability; otherwise promising techniques can be computationally infeasible when applied to networks with huge numbers of nodes and edges. One way of extending the reach of network analysis is to sparsify the graph by retaining only a subset of its edges. The reduced network could prove much more tractable. For this thesis, I propose a new sparsification algorithm that preserves the properties of a random walk on the network. Specifically, the algorithm finds a subset of edges that best preserves the stationary distribution of a random walk by minimizing the Kullback-Leibler divergence between a walk on the original and sparsified graphs. A highly efficient greedy search strategy is developed to optimize this objective. Experimental results are presented that test the performance of the algorithm on the influence maximization task. These results demonstrate that sparsification allows near-optimal solutions to be found in a small fraction of the runtime that would required using the full network. Two cases are shown where sparsification allows an influence maximization algorithm to be applied to a dataset that previous work had considered intractable.
Show less - Date Issued
- 2015
- Identifier
- CFH0004732, ucf:45387
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004732
- Title
- Identifying Influential Agents in Social Systems.
- Creator
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Maghami, Mahsa, Sukthankar, Gita, Turgut, Damla, Wu, Annie, Boloni, Ladislau, Garibay, Ivan, University of Central Florida
- Abstract / Description
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This dissertation addresses the problem of influence maximization in social networks. Influence maximization is applicable to many types of real-world problems, including modeling contagion, technology adoption, and viral marketing. Here we examine an advertisement domain in which the overarching goal is to find the influential nodes in a social network, based on the network structure and the interactions, as targets of advertisement. The assumption is that advertisement budget limits prevent...
Show moreThis dissertation addresses the problem of influence maximization in social networks. Influence maximization is applicable to many types of real-world problems, including modeling contagion, technology adoption, and viral marketing. Here we examine an advertisement domain in which the overarching goal is to find the influential nodes in a social network, based on the network structure and the interactions, as targets of advertisement. The assumption is that advertisement budget limits prevent us from sending the advertisement to everybody in the network. Therefore, a wise selection of the people can be beneficial in increasing the product adoption. To model these social systems, agent-based modeling, a powerful tool for the study of phenomena that are difficult to observe within the confines of the laboratory, is used.To analyze marketing scenarios, this dissertation proposes a new method for propagating information through a social system and demonstrates how it can be used to develop a product advertisement strategy in a simulated market. We consider the desire of agents toward purchasing an item as a random variable and solve the influence maximization problem in steady state using an optimization method to assign the advertisement of available products to appropriate messenger agents. Our market simulation 1) accounts for the effects of group membership on agent attitudes 2) has a network structure that is similar to realistic human systems 3) models inter-product preference correlations that can be learned from market data. The results on synthetic data show that this method is significantly better than network analysis methods based on centrality measures.The optimized influence maximization (OIM) described above, has some limitations. For instance, it relies on a global estimation of the interaction among agents in the network, rendering it incapable of handling large networks. Although OIM is capable of finding the influential nodes in the social network in an optimized way and targeting them for advertising, in large networks, performing the matrix operations required to find the optimized solution is intractable.To overcome this limitation, we then propose a hierarchical influence maximization (HIM) algorithm for scaling influence maximization to larger networks. In the hierarchical method the network is partitioned into multiple smaller networks that can be solved exactly with optimization techniques, assuming a generalized IC model, to identify a candidate set of seed nodes. The candidate nodes are used to create a distance-preserving abstract version of the network that maintains an aggregate influence model between partitions. The budget limitation for the advertising dictates the algorithm's stopping point. On synthetic datasets, we show that our method comes close to the optimal node selection, at substantially lower runtime costs.We present results from applying the HIM algorithm to real-world datasets collected from social media sites with large numbers of users (Epinions, SlashDot, and WikiVote) and compare it with two benchmarks, PMIA and DegreeDiscount, to examine the scalability and performance.Our experimental results reveal that HIM scales to larger networks but is outperformed by degree-based algorithms in highly-connected networks. However, HIM performs well in modular networks where the communities are clearly separable with small number of cross-community edges. This finding suggests that for practical applications it is useful to account for network properties when selecting an influence maximization method.
Show less - Date Issued
- 2014
- Identifier
- CFE0005205, ucf:50647
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005205
- Title
- TRUST ON THE WEB: THE IMPACT OF SOCIAL CONSENSUS ON INFORMATION CREDIBILITY.
- Creator
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Del Giudice, Katherine, Hancock, Peter, University of Central Florida
- Abstract / Description
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Models of the need-driven information search and the information appraisal process were formed from a comprehensive literature review of factors affecting perceived credibility and trust in online information. The social component of online credibility has not, to date, been thoroughly researched. This componentÂÂ's impact on the development of the perceived credibility of online information was examined in two experiments. In the first experiment, the impact of...
Show moreModels of the need-driven information search and the information appraisal process were formed from a comprehensive literature review of factors affecting perceived credibility and trust in online information. The social component of online credibility has not, to date, been thoroughly researched. This componentÂÂ's impact on the development of the perceived credibility of online information was examined in two experiments. In the first experiment, the impact of positive, mixed, and negative social feedback on the development of the perceived credibility of a web page was evaluated. In the second experiment, the effect of social feedback on credibility was examined under two levels of motivation for information use to investigate whether social feedback becomes less important as motivation to obtain quality information increases. The results of Experiment 1 suggest that type of feedback can influence perceived web page credibility. Pages with negative audience feedback received the lowest credibility ratings, while pages with positive audience feedback received the highest credibility ratings. Pages with mixed or no audience feedback received higher credibility ratings than pages with negative feedback, but lower credibility ratings than pages with positive feedback. In Experiment 2, high motivation did not impact the number of web page elements participants reported that they used to determine credibility. High motivation for information use also did not reduce the impact of audience feedback on perceived credibility.
Show less - Date Issued
- 2010
- Identifier
- CFE0003240, ucf:48540
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003240
- Title
- Domestic Violence in Rural (&) Non-Rural Areas: A Study on the Influence of Population Density on Arrest Rates in the State of Florida.
- Creator
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Gonzalez Cruz, Kiara, Huff-Corzine, Lin, Reckdenwald, Amy, Corzine, Harold, University of Central Florida
- Abstract / Description
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Domestic violence (DV) is a global issue that can affect anyone regardless of what role they play in a family household. It does not discriminate by education, age, religion, etc. DV includes any type of violence or abuse that occurs within a domestic setting. For the purposes of this study, this content primarily focuses on intimate partner violence (IPV) as the main form of DV and is used interchangeably throughout the text. This study examines the influence of population density on arrest...
Show moreDomestic violence (DV) is a global issue that can affect anyone regardless of what role they play in a family household. It does not discriminate by education, age, religion, etc. DV includes any type of violence or abuse that occurs within a domestic setting. For the purposes of this study, this content primarily focuses on intimate partner violence (IPV) as the main form of DV and is used interchangeably throughout the text. This study examines the influence of population density on arrest rates for DV and some factors behind the likelihood of arrests in urban and rural areas. The literature between both of these societies has demonstrated a clear difference in social behaviors that shape the response to DV (Websdale and Johnson 1998). Normative social influence theory suggests that people's influence may lead someone to conform in order to be liked or accepted by a group (Izuma 2017). This theory hypothesizes that the proportion of people living in rural per county will have fewer arrests for DV than the proportion of people living in non-rural areas because of the need for positive relationships that can lead to conformity (Izuma 2017). Furthermore, it is predicted that there are less arrests in rural areas because of the effects of informal social controls in these areas. Informal social controls can take place between police and citizens that may interact more personally through socialization. An example is when citizens take matters into their own hands, therefore prolonging the reporting of crimes to police. This study uses secondary data provided by sources such as the Florida Department of Law Enforcement (FDLE) website and Social Explorer. Broader implications of this research are that it could shed some light on the social dynamics that impact the outcome of crime in both densely populated and sparsely populated areas.
Show less - Date Issued
- 2019
- Identifier
- CFE0007808, ucf:52366
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007808
- Title
- Environmental Physical(-)Virtual Interaction to Improve Social Presence with a Virtual Human in Mixed Reality.
- Creator
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Kim, Kangsoo, Welch, Gregory, Gonzalez, Avelino, Sukthankar, Gita, Bruder, Gerd, Fiore, Stephen, University of Central Florida
- Abstract / Description
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Interactive Virtual Humans (VHs) are increasingly used to replace or assist real humans in various applications, e.g., military and medical training, education, or entertainment. In most VH research, the perceived social presence with a VH, which denotes the user's sense of being socially connected or co-located with the VH, is the decisive factor in evaluating the social influence of the VH(-)a phenomenon where human users' emotions, opinions, or behaviors are affected by the VH. The purpose...
Show moreInteractive Virtual Humans (VHs) are increasingly used to replace or assist real humans in various applications, e.g., military and medical training, education, or entertainment. In most VH research, the perceived social presence with a VH, which denotes the user's sense of being socially connected or co-located with the VH, is the decisive factor in evaluating the social influence of the VH(-)a phenomenon where human users' emotions, opinions, or behaviors are affected by the VH. The purpose of this dissertation is to develop new knowledge about how characteristics and behaviors of a VH in a Mixed Reality (MR) environment can affect the perception of and resulting behavior with the VH, and to find effective and efficient ways to improve the quality and performance of social interactions with VHs. Important issues and challenges in real(-)virtual human interactions in MR, e.g., lack of physical(-)virtual interaction, are identified and discussed through several user studies incorporating interactions with VH systems. In the studies, different features of VHs are prototyped and evaluated, such as a VH's ability to be aware of and influence the surrounding physical environment, while measuring objective behavioral data as well as collecting subjective responses from the participants. The results from the studies support the idea that the VH's awareness and influence of the physical environment can improve not only the perceived social presence with the VH, but also the trustworthiness of the VH within a social context. The findings will contribute towards designing more influential VHs that can benefit a wide range of simulation and training applications for which a high level of social realism is important, and that can be more easily incorporated into our daily lives as social companions, providing reliable relationships and convenience in assisting with daily tasks.
Show less - Date Issued
- 2018
- Identifier
- CFE0007340, ucf:52115
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007340
- Title
- Examining presence and influence of linguistic characteristics in the Twitter discourse surrounding the women's right to drive movement in Saudi Arabia.
- Creator
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Sahly, Abdulsamad, Kinnally, William, Neuberger, Lindsay, Miller, Ann, University of Central Florida
- Abstract / Description
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Social media platforms like Facebook and Twitter have been popular tools for social and political movements in non-democratic societies in which traditional media outlets are under government control. Activists in Saudi Arabia, particularly women, have launched several campaigns through social media to demand the right to drive for women. This study used framing theory as the foundation for looking at the degree to which cognitive, emotion, and religious or moral language has been used to...
Show moreSocial media platforms like Facebook and Twitter have been popular tools for social and political movements in non-democratic societies in which traditional media outlets are under government control. Activists in Saudi Arabia, particularly women, have launched several campaigns through social media to demand the right to drive for women. This study used framing theory as the foundation for looking at the degree to which cognitive, emotion, and religious or moral language has been used to frame discussion of this issue on Twitter. Additionally, this study observed the relationship between these linguistic attributes in Twitter and retweeting behavior to understand the characteristics of the discourse that relate to the potential influence of the message. The results suggested that, within the sociopolitical discussion in social media, cognitive language was expressed the most often, particularly insight and causation language. The results also suggested that tweets containing cognitive language are more likely to be retweeted than those with emotion language. However, among the components of cognitive and emotion language, anger was the strongest specific predictor of retweeting behavior. The implications of the presence of linguistic attributes and their relationship to retweeting behavior and suggestions for future communication research within the context of social and political movements are discussed.
Show less - Date Issued
- 2016
- Identifier
- CFE0006386, ucf:51535
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006386
