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Social media offers a powerful platform for the independent digital content producer community to develop, disperse, and maintain their brands. In terms of information systems research, the broad majority of the work has not examined hedonic consumption on Social Media Sites (SMS). The focus has mostly been on the organizational

Social media offers a powerful platform for the independent digital content producer community to develop, disperse, and maintain their brands. In terms of information systems research, the broad majority of the work has not examined hedonic consumption on Social Media Sites (SMS). The focus has mostly been on the organizational perspectives and utilitarian gains from these services. Unlike through traditional commerce channels, including e-commerce retailers, consumption enhancing hedonic utility is experienced differently in the context of a social media site; consequently, the dynamic of the decision-making process shifts when it is made in a social context. Previous research assumed a limited influence of a small, immediate group of peers. But the rules change when the network of peers expands exponentially. The assertion is that, while there are individual differences in the level of susceptibility to influence coming from others, these are not the most important pieces of the analysis--unlike research centered completely on influence. Rather, the context of the consumption can play an important role in the way social influence factors affect consumer behavior on Social Media Sites. Over the course of three studies, this dissertation will examine factors that influence consumer decision-making and the brand personalities created and interpreted in these SMS. Study one examines the role of different types of peer influence on consumer decision-making on Facebook. Study two observes the impact of different types of producer message posts with the different types of influence on decision-making on Twitter. Study three will conclude this work with an exploratory empirical investigation of actual twitter postings of a set of musicians. These studies contribute to the body of IS literature by evaluating the specific behavioral changes related to consumption in the context of digital social media: (a) the power of social influencers in contrast to personal preferences on SMS, (b) the effect on consumers of producer message types and content on SMS at both the profile level and the individual message level.
ContributorsSopha, Matthew (Author) / Santanam, Raghu T (Thesis advisor) / Goul, Kenneth M (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Law enforcement, schools and universities, health service agencies, as well as social service agencies, each acquire information from individuals that receive their services. That information gets recorded into the respective application system of each organization. The information, however, gets recorded only in the context of each service rendered and within

Law enforcement, schools and universities, health service agencies, as well as social service agencies, each acquire information from individuals that receive their services. That information gets recorded into the respective application system of each organization. The information, however, gets recorded only in the context of each service rendered and within each system used to record it. Information that is recorded by the police department for one individual is entirely different from the information that is recorded by the hospital for that same individual. What if all the organizations used the same system to record information? What if all the organizations followed the same protocols to record information as well as access it? The goal of this research was to analyze a system that allows for all organizations within a community to share information with each other. Technically, this system is feasible. However, public opinion says sharing personal information is unethical, and Federal regulation says it is unlawful. To accomplish an information-sharing system of this type, both regulation and public opinion need to be addressed.
ContributorsPullin, Britton Scott (Author) / Schildgen, Thomas (Thesis advisor) / Prewitt, Deborah (Committee member) / Ralston, Laurel (Committee member) / Arizona State University (Publisher)
Created2012
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Description
With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus

With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables.
ContributorsKoh, Derek (Author) / Runger, George C. (Thesis advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like

Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Data imbalance and data noise often coexist in real world datasets. Data imbalance affects the learning classifier by degrading the recognition power of the classifier on the minority class, while data noise affects the learning classifier by providing inaccurate information and thus misleads the classifier. Because of these differences, data

Data imbalance and data noise often coexist in real world datasets. Data imbalance affects the learning classifier by degrading the recognition power of the classifier on the minority class, while data noise affects the learning classifier by providing inaccurate information and thus misleads the classifier. Because of these differences, data imbalance and data noise have been treated separately in the data mining field. Yet, such approach ignores the mutual effects and as a result may lead to new problems. A desirable solution is to tackle these two issues jointly. Noting the complementary nature of generative and discriminative models, this research proposes a unified model fusion based framework to handle the imbalanced classification with noisy dataset.

The phase I study focuses on the imbalanced classification problem. A generative classifier, Gaussian Mixture Model (GMM) is studied which can learn the distribution of the imbalance data to improve the discrimination power on imbalanced classes. By fusing this knowledge into cost SVM (cSVM), a CSG method is proposed. Experimental results show the effectiveness of CSG in dealing with imbalanced classification problems.

The phase II study expands the research scope to include the noisy dataset into the imbalanced classification problem. A model fusion based framework, K Nearest Gaussian (KNG) is proposed. KNG employs a generative modeling method, GMM, to model the training data as Gaussian mixtures and form adjustable confidence regions which are less sensitive to data imbalance and noise. Motivated by the K-nearest neighbor algorithm, the neighboring Gaussians are used to classify the testing instances. Experimental results show KNG method greatly outperforms traditional classification methods in dealing with imbalanced classification problems with noisy dataset.

The phase III study addresses the issues of feature selection and parameter tuning of KNG algorithm. To further improve the performance of KNG algorithm, a Particle Swarm Optimization based method (PSO-KNG) is proposed. PSO-KNG formulates model parameters and data features into the same particle vector and thus can search the best feature and parameter combination jointly. The experimental results show that PSO can greatly improve the performance of KNG with better accuracy and much lower computational cost.
ContributorsHe, Miao (Author) / Wu, Teresa (Thesis advisor) / Li, Jing (Committee member) / Silva, Alvin (Committee member) / Borror, Connie (Committee member) / Arizona State University (Publisher)
Created2014
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Description
The current study examines the role that context plays in hackers' perceptions of the risks and payoffs characterizing a hacktivist attack. Hacktivism (i.e., hacking to convey a moral, ethical, or social justice message) is examined through a general game theoretic framework as a product of costs and benefits, as well

The current study examines the role that context plays in hackers' perceptions of the risks and payoffs characterizing a hacktivist attack. Hacktivism (i.e., hacking to convey a moral, ethical, or social justice message) is examined through a general game theoretic framework as a product of costs and benefits, as well as the contextual cues that may sway hackers' estimations of each. In two pilot studies, a bottom-up approach is utilized to identify the key motives underlying (1) past attacks affiliated with a major hacktivist group, Anonymous, and (2) popular slogans utilized by Anonymous in its communication with members, targets, and broader society. Three themes emerge from these analyses, namely: (1) the prevalence of first-person plural pronouns (i.e., we, our) in Anonymous slogans; (2) the prevalence of language inducing status or power; and (3) the importance of social injustice in triggering Anonymous activity. The present research therefore examines whether these three contextual factors activate participants' (1) sense of deindividuation, or the loss of an individual's personal self in the context of a group or collective; and (2) motive for self-serving power or society-serving social justice. Results suggest that participants' estimations of attack likelihood stemmed solely from expected payoffs, rather than their interplay with subjective risks. As expected, the use of we language led to a decrease in subjective risks, possibly due to primed effects of deindividuation. In line with game theory, the joint appearance of both power and justice motives resulted in (1) lower subjective risks, (2) higher payoffs, and (3) higher attack likelihood overall. Implications for policymakers and the understanding and prevention of hacktivism are discussed, as are the possible ramifications of deindividuation and power for the broader population of Internet users around the world.
ContributorsBodford, Jessica (Author) / Kwan, Virginia S. Y. (Thesis advisor) / Shakarian, Paulo (Committee member) / Adame, Bradley J. (Committee member) / Arizona State University (Publisher)
Created2015
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Description
A statement appearing in social media provides a very significant challenge for determining the provenance of the statement. Provenance describes the origin, custody, and ownership of something. Most statements appearing in social media are not published with corresponding provenance data. However, the same characteristics that make the social media environment

A statement appearing in social media provides a very significant challenge for determining the provenance of the statement. Provenance describes the origin, custody, and ownership of something. Most statements appearing in social media are not published with corresponding provenance data. However, the same characteristics that make the social media environment challenging, including the massive amounts of data available, large numbers of users, and a highly dynamic environment, provide unique and untapped opportunities for solving the provenance problem for social media. Current approaches for tracking provenance data do not scale for online social media and consequently there is a gap in provenance methodologies and technologies providing exciting research opportunities. The guiding vision is the use of social media information itself to realize a useful amount of provenance data for information in social media. This departs from traditional approaches for data provenance which rely on a central store of provenance information. The contemporary online social media environment is an enormous and constantly updated "central store" that can be mined for provenance information that is not readily made available to the average social media user. This research introduces an approach and builds a foundation aimed at realizing a provenance data capability for social media users that is not accessible today.
ContributorsBarbier, Geoffrey P (Author) / Liu, Huan (Thesis advisor) / Bell, Herbert (Committee member) / Li, Baoxin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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Description
This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of

This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of the current implementation at the Southern Nevada Health District (SNHD). The result of the system deployment at SNHD was considered as a basis for projecting the practical application and benefits of an enterprise architecture. This approach has resulted in a sustainable platform to enhance the practice of public health by improving the quality and timeliness of data, effectiveness of an investigation, and reporting across the continuum.
ContributorsKriseman, Jeffrey Michael (Author) / Dinu, Valentin (Thesis advisor) / Greenes, Robert (Committee member) / Johnson, William (Committee member) / Arizona State University (Publisher)
Created2012
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Description
The Arizona State University Herbarium began in 1896 when Professor Fredrick Irish collected the first recorded Arizona specimen for what was then called the Tempe Normal School - a Parkinsonia microphylla. Since then, the collection has grown to approximately 400,000 specimens of vascular plants and lichens. The most recent project

The Arizona State University Herbarium began in 1896 when Professor Fredrick Irish collected the first recorded Arizona specimen for what was then called the Tempe Normal School - a Parkinsonia microphylla. Since then, the collection has grown to approximately 400,000 specimens of vascular plants and lichens. The most recent project includes the digitization - both the imaging and databasing - of approximately 55,000 vascular plant specimens from Latin America. To accomplish this efficiently, possibilities in non-traditional methods, including both new and existing technologies, were explored. SALIX (semi-automatic label information extraction) was developed as the central tool to handle automatic parsing, along with BarcodeRenamer (BCR) to automate image file renaming by barcode. These two developments, combined with existing technologies, make up the SALIX Method. The SALIX Method provides a way to digitize herbarium specimens more efficiently than the traditional approach of entering data solely through keystroking. Using digital imaging, optical character recognition, and automatic parsing, I found that the SALIX Method processes data at an average rate that is 30% faster than typing. Data entry speed is dependent on user proficiency, label quality, and to a lesser degree, label length. This method is used to capture full specimen records, including close-up images where applicable. Access to biodiversity data is limited by the time and resources required to digitize, but I have found that it is possible to do so at a rate that is faster than typing. Finally, I experiment with the use of digital field guides in advancing access to biodiversity data, to stimulate public engagement in natural history collections.
ContributorsBarber, Anne Christine (Author) / Landrum, Leslie R. (Thesis advisor) / Wojciechowski, Martin F. (Thesis advisor) / Gilbert, Edward (Committee member) / Lafferty, Daryl (Committee member) / Arizona State University (Publisher)
Created2012
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Description
This thesis examines the role of the media and popular culture in defining the shape and scope of what we think of today as "science." As a source of cognitive authority the scientific establishment is virtually beyond dispute. The intellectual clout of science seemingly elevates it to a position outside

This thesis examines the role of the media and popular culture in defining the shape and scope of what we think of today as "science." As a source of cognitive authority the scientific establishment is virtually beyond dispute. The intellectual clout of science seemingly elevates it to a position outside the influence of the general population. Yet in reality the emergence and evolution of the public sphere, including popular culture, has had a profound impact on the definition and application of science. What science is and how it relates to the life of the ordinary person are hardly static concepts; the public perception of science has been molding its boundaries since at least the 18th century. During the Enlightenment "natural philosophy" was broadly accessible and integrated nicely with other forms of knowledge. As the years passed into the 19th century, however, science became increasingly professionalized and distinct, until the "Two Cultures" had fully developed. The established scientific institution distanced itself from the nonscientific community, leaving the task of communicating scientific knowledge to various popularizers, who typically operated through the media and often used the mantle of science to further their own social or political agendas. Such isolation from orthodox science forced the public to create an alternate form of science for popular consumption, a form consisting mainly of decontextualized facts, often used in contrast to other forms of thought (i.e. religion, art, or pseudoscience). However, with the recent advent of "Web 2.0" and the increasing prominence of convergence culture, the role of the public sphere is undergoing a dramatic revolution. Concepts such as "collective intelligence" are changing consumers of information into simultaneous producers, establishing vast peer networks of collaboration and enabling the public to bypass traditional sources of authority. This new hypermobility of information and empowerment of the public sphere are just now beginning to break down science's monolithic status. In many ways, it seems, we are entering a new Enlightenment.
ContributorsSmith, Robert Scott (Author) / Lussier, Mark (Thesis advisor) / Broglio, Ronald (Committee member) / Bivona, Daniel (Committee member) / Arizona State University (Publisher)
Created2012