Matching Items (33)
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- All Subjects: Machine Learning
- Creators: School of Mathematical and Statistical Sciences
Description
Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
The OMFIT (One Modeling Framework for Integrated Tasks) modeling environment and the BRAINFUSE module have been deployed on the PPPL (Princeton Plasma Physics Laboratory) computing cluster with modifications that have rendered the application of artificial neural networks (NNs) to the TRANSP databases for the JET (Joint European Torus), TFTR (Tokamak Fusion Test Reactor), and NSTX (National Spherical Torus Experiment) devices possible through their use. This development has facilitated the investigation of NNs for predicting heat transport profiles in JET, TFTR, and NSTX, and has promoted additional investigations to discover how else NNs may be of use to scientists at PPPL. In applying NNs to the aforementioned devices for predicting heat transport, the primary goal of this endeavor is to reproduce the success shown in Meneghini et al. in using NNs for heat transport prediction in DIII-D. Being able to reproduce the results from is important because this in turn would provide scientists at PPPL with a quick and efficient toolset for reliably predicting heat transport profiles much faster than any existing computational methods allow; the progress towards this goal is outlined in this report, and potential additional applications of the NN framework are presented.
ContributorsLuna, Christopher Joseph (Author) / Tang, Wenbo (Thesis director) / Treacy, Michael (Committee member) / Orso, Meneghini (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2015-05
Description
Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed in this work provides understanding into why par- ticular words trend on Twitter.
ContributorsMarshall, Grant A (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
A model has been developed to modify Euler-Bernoulli beam theory for wooden beams, using visible properties of wood knot-defects. Treating knots in a beam as a system of two ellipses that change the local bending stiffness has been shown to improve the fit of a theoretical beam displacement function to edge-line deflection data extracted from digital imagery of experimentally loaded beams. In addition, an Ellipse Logistic Model (ELM) has been proposed, using L1-regularized logistic regression, to predict the impact of a knot on the displacement of a beam. By classifying a knot as severely positive or negative, vs. mildly positive or negative, ELM can classify knots that lead to large changes to beam deflection, while not over-emphasizing knots that may not be a problem. Using ELM with a regression-fit Young's Modulus on three-point bending of Douglass Fir, it is possible estimate the effects a knot will have on the shape of the resulting displacement curve.
ContributorsSexton, Thurston Bryant (Author) / Takahashi, Timothy (Thesis director) / Jones, Donald (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2015-05
Description
The focus of this project is developing a business plan for faith-based counseling for college students. Renewed Living Counseling Center (RLCC) is a faith-based counseling center in the Tempe area serving Arizona State University students. RLCC strives to bring healing and wholeness to each student who comes through the doors, to empower them to realize and live out their potential, by providing them with the skills to accomplish their dreams and live full lives, through counseling, motivation, education, and treating studentʼs behaviors to become whole and successful. Research indicates that the proposed center, Renewed Living Counseling Center (RLCC), has great potential for success because:
1. Spirituality and faith are increasingly recognized as important aspects in a personʼs life. National research shows that 66% of people feel counseling should include spirituality. Research with ASU students found that students reflect this statistic, as they feel spirituality is an important part of counseling. Students also feel spirituality is appropriate to include as part of counseling services offered by centers referred to by ASU.
2. There is a need for counseling at ASU. Nationally,approximately1,100 college students commit suicide each year. At ASU, almost one-third of students reported feeling so depressed that it is difficult to function, and 0.9% report having attempted suicide within the past year.
3. Surveys of ASU students indicate that students who describe themselves as being religious are more desirous that counseling include a spiritual dimension. Surveys of campus pastors indicate that over 80% believe there is a need for faith-based counseling and would refer students to a local center.
4. Price is an issue. Indeed, a survey of campus pastors indicated that they believed cost of counseling to be one of the primary deterrents to students seeking help. One way to control costs is to use a mixture of residents and licensed counselors. As in medicine, students must complete coursework along with a period of residency or internship to obtain licensing. Both religious and secular masters programs in counseling exist in the greater Phoenix area. Thus, there is a potential supply of students who could work as residents, permitting RLCC to offer counseling services at reasonable prices.
1. Spirituality and faith are increasingly recognized as important aspects in a personʼs life. National research shows that 66% of people feel counseling should include spirituality. Research with ASU students found that students reflect this statistic, as they feel spirituality is an important part of counseling. Students also feel spirituality is appropriate to include as part of counseling services offered by centers referred to by ASU.
2. There is a need for counseling at ASU. Nationally,approximately1,100 college students commit suicide each year. At ASU, almost one-third of students reported feeling so depressed that it is difficult to function, and 0.9% report having attempted suicide within the past year.
3. Surveys of ASU students indicate that students who describe themselves as being religious are more desirous that counseling include a spiritual dimension. Surveys of campus pastors indicate that over 80% believe there is a need for faith-based counseling and would refer students to a local center.
4. Price is an issue. Indeed, a survey of campus pastors indicated that they believed cost of counseling to be one of the primary deterrents to students seeking help. One way to control costs is to use a mixture of residents and licensed counselors. As in medicine, students must complete coursework along with a period of residency or internship to obtain licensing. Both religious and secular masters programs in counseling exist in the greater Phoenix area. Thus, there is a potential supply of students who could work as residents, permitting RLCC to offer counseling services at reasonable prices.
ContributorsMatthews, Rachel Leigh (Author) / Steinbart, Paul (Thesis director) / Chung, Sally (Committee member) / Sanders, Ben (Committee member) / Barrett, The Honors College (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor)
Created2014-05
Description
Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using rule induction methods. The dataset is cleaned by removing entries with missing data. The new column is created to measure price difference to create a more accurate analysis on the change in price. Eight relevant variables are selected using cross validation: the total number of bitcoins, the total size of the blockchains, the hash rate, mining difficulty, revenue from mining, transaction fees, the cost of transactions and the estimated transaction volume. The in-sample data is modeled using a simple tree fit, first with one variable and then with eight. Using all eight variables, the in-sample model and data have a correlation of 0.6822657. The in-sample model is improved by first applying bootstrap aggregation (also known as bagging) to fit 400 decision trees to the in-sample data using one variable. Then the random forests technique is applied to the data using all eight variables. This results in a correlation between the model and data of 9.9443413. The random forests technique is then applied to an Ethereum dataset, resulting in a correlation of 9.6904798. Finally, an out-of-sample model is created for Bitcoin and Ethereum using random forests, with a benchmark correlation of 0.03 for financial data. The correlation between the training model and the testing data for Bitcoin was 0.06957639, while for Ethereum the correlation was -0.171125. In conclusion, it is confirmed that cryptocurrencies can have accurate in-sample models by applying the random forests method to a dataset. However, out-of-sample modeling is more difficult, but in some cases better than typical forms of financial data. It should also be noted that cryptocurrency data has similar properties to other related financial datasets, realizing future potential for system modeling for cryptocurrency within the financial world.
ContributorsBrowning, Jacob Christian (Author) / Meuth, Ryan (Thesis director) / Jones, Donald (Committee member) / McCulloch, Robert (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Machine learning for analytics has exponentially increased in the past few years due to its ability to identify hidden insights in data. It also has a plethora of applications in healthcare ranging from improving image recognition in CT scans to extracting semantic meaning from thousands of medical form PDFs. Currently in the BioElectrical Systems and Technology Lab, there is a biosensor in development that retrieves and analyzes data manually. In a proof of concept, this project uses the neural network architecture to automatically parse and classify a cardiac disease data set as well as explore health related factors impacting cardiac disease in patients of all ages.
ContributorsMurella, Akhila Sainagaki (Author) / Blain-Christen, Jennifer (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of the Arts, Media and Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In this paper, I will show that news headlines of global events can predict changes in stock price by using Machine Learning and eight years of data from r/WorldNews, a popular forum on Reddit.com. My data is confined to the top 25 daily posts on the forum, and due to the implicit filtering mechanism in the online community, these 25 posts are representative of the most popular news headlines and influential global events of the day. Hence, these posts shine a light on how large-scale social and political events affect the stock market. Using a Logistic Regression and a Naive Bayes classifier, I am able to predict with approximately 85% accuracy a binary change in stock price using term-feature vectors gathered from the news headlines. The accuracy, precision and recall results closely rival the best models in this field of research. In addition to the results, I will also describe the mathematical underpinnings of the two models; preceded by a general investigation of the intersection between the multiple academic disciplines related to this project. These range from social to computer science and from statistics to philosophy. The goal of this additional discussion is to further illustrate the interdisciplinary nature of the research and hopefully inspire a non-monolithic mindset when further investigations are pursued.
ContributorsPriniski, John Hunter (Author) / Haiyan, Wang (Thesis director) / Hazel, Kwon (Committee member) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Background: As the growth of social media platforms continues, the use of the constantly increasing amount of freely available, user-generated data they receive becomes of great importance. One apparent use of this content is public health surveillance; such as for increasing understanding of substance abuse. In this study, Facebook was used to monitor nicotine addiction through the public support groups users can join to aid their quitting process. Objective: The main objective of this project was to gain a better understanding of the mechanisms of nicotine addiction online and provide content analysis of Facebook posts obtained from "quit smoking" support groups. Methods: Using the Facebook Application Programming Interface (API) for Python, a sample of 9,970 posts were collected in October 2015. Information regarding the user's name and the number of likes and comments they received on their post were also included. The posts crawled were then manually classified by one annotator into one of three categories: positive, negative, and neutral. Where positive posts are those that describe current quits, negative posts are those that discuss relapsing, and neutral posts are those that were not be used to train the classifiers, which include posts where users have yet to attempt a quit, ads, random questions, etc. For this project, the performance of two machine learning algorithms on a corpus of manually labeled Facebook posts were compared. The classification goal was to test the plausibility of creating a natural language processing machine learning classifier which could be used to distinguish between relapse (labeled negative) and quitting success (labeled positive) posts from a set of smoking related posts. Results: From the corpus of 9,970 posts that were manually labeled: 6,254 (62.7%) were labeled positive, 1,249 (12.5%) were labeled negative, and 2467 (24.8%) were labeled neutral. Since the posts labeled neutral are those which are irrelevant to the classification task, 7,503 posts were used to train the classifiers: 83.4% positive and 16.6% negative. The SVM classifier was 84.1% accurate and 84.1% precise, had a recall of 1, and an F-score of 0.914. The MNB classifier was 82.8% accurate and 82.8% precise, had a recall of 1, and an F-score of 0.906. Conclusions: From the Facebook surveillance results, a small peak is given into the behavior of those looking to quit smoking. Ultimately, what makes Facebook a great tool for public health surveillance is that it has an extremely large and diverse user base with information that is easily obtainable. This, and the fact that so many people are actually willing to use Facebook support groups to aid their quitting processes demonstrates that it can be used to learn a lot about quitting and smoking behavior.
ContributorsMolina, Daniel Antonio (Author) / Li, Baoxin (Thesis director) / Tian, Qiongjie (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
College and university students are heavily influenced by their exposure to opportunities, individuals, and belief-systems during their time in school. More specifically, countless students are impacted by campus Christian ministries. There are 67 registered religious clubs and organizations across Arizona State University's four campuses, and 46 of them identify as Christian. Similar to most faith-based organizations, Christian campus ministries seek to impact the lives of students. This study will take a look at the influence of these ministries at ASU by researching their intersection with another key component of university life: wellness.
The primary research question is, “How does involvement in Christian ministries at ASU relate to the wellness of students?” The study will examine multiple dimensions of wellness: occupational, physical, social, intellectual, spiritual, and emotional. Each component is essential to understanding the health and well-being of an individual, which is why this study will measure wellness levels in each dimension among samples of students at ASU.
The methodology chosen was a short, anonymous survey that 148 ASU students participated in—73 involved in Christian ministries at ASU and 75 not involved. The quantitative component included a wellness assessment using questions from The National Wellness Institute. These wellness scale questions were broken up into 5 randomized sections, each with one question per dimension, for 30 questions total. Each question response was assigned a rating on a 1 to 5 scale, 1 associated with low wellness and 5 high wellness. The qualitative component, comprised of short answer questions, only applied to students who were involved in a Christian ministry. This portion allowed respondents to explain if and how the ministry impacts each dimension of wellness uniquely.
The quantitative results showed some evident differences between students involved in Christian ministries and students not involved. The social and spiritual dimensions concluded much higher levels of wellness for involved students, both statistically significant with p-values of 0.028 and 0.004. Although some of the wellness differences between involved and not involved participants were not statistically significant, there is also notable variation among questions within each dimension. For the qualitative data, most students in Christian ministries said they believe their involvement increases their wellness in all six dimensions. For each dimension, over 75% of participants said that the ministry impacted their well-being. For the social, spiritual, and emotional dimensions, at least 97% of respondents said their ministry involvement impacted their wellness.
In examining the conclusions of the study, one recommendations is to strengthen the partnership between the greater ASU community and Christian ministries by collaborating and combining resources for programming that relates to their common goals and shared values. Additionally, other faith-based organizations at ASU may benefit from replicating this study to observe their unique wellness impact.
The primary research question is, “How does involvement in Christian ministries at ASU relate to the wellness of students?” The study will examine multiple dimensions of wellness: occupational, physical, social, intellectual, spiritual, and emotional. Each component is essential to understanding the health and well-being of an individual, which is why this study will measure wellness levels in each dimension among samples of students at ASU.
The methodology chosen was a short, anonymous survey that 148 ASU students participated in—73 involved in Christian ministries at ASU and 75 not involved. The quantitative component included a wellness assessment using questions from The National Wellness Institute. These wellness scale questions were broken up into 5 randomized sections, each with one question per dimension, for 30 questions total. Each question response was assigned a rating on a 1 to 5 scale, 1 associated with low wellness and 5 high wellness. The qualitative component, comprised of short answer questions, only applied to students who were involved in a Christian ministry. This portion allowed respondents to explain if and how the ministry impacts each dimension of wellness uniquely.
The quantitative results showed some evident differences between students involved in Christian ministries and students not involved. The social and spiritual dimensions concluded much higher levels of wellness for involved students, both statistically significant with p-values of 0.028 and 0.004. Although some of the wellness differences between involved and not involved participants were not statistically significant, there is also notable variation among questions within each dimension. For the qualitative data, most students in Christian ministries said they believe their involvement increases their wellness in all six dimensions. For each dimension, over 75% of participants said that the ministry impacted their well-being. For the social, spiritual, and emotional dimensions, at least 97% of respondents said their ministry involvement impacted their wellness.
In examining the conclusions of the study, one recommendations is to strengthen the partnership between the greater ASU community and Christian ministries by collaborating and combining resources for programming that relates to their common goals and shared values. Additionally, other faith-based organizations at ASU may benefit from replicating this study to observe their unique wellness impact.
ContributorsSouza, Ann Christina (Author) / Golden, Amy (Thesis director) / Valiente, Carlos (Committee member) / Department of English (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05