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- All Subjects: deep learning
- All Subjects: Twitter
- Creators: Davulcu, Hasan
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Twitter has become a very popular social media site that is used daily by many people and organizations. This paper will focus on the financial aspect of Twitter, as a process will be shown to be able to mine data about specific companies' stock prices. This was done by writing a program to grab tweets about the stocks of the thirty companies in the Dow Jones.
ContributorsLarson, Grant Elliott (Author) / Davulcu, Hasan (Thesis director) / Ye, Jieping (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
In this paper I defend the argument that public reaction to news headlines correlates with the short-term price direction of Bitcoin. I collected a month's worth of Bitcoin data consisting of news headlines, tweets, and the price of the cryptocurrency. I fed this data into a Long Short-Term Memory Neural Network and built a model that predicted Bitcoin price for a new timeframe. The model correctly predicted 75% of test set price trends on 3.25 hour time intervals. This is higher than the 53.57% accuracy tested with a Bitcoin price model without sentiment data. I concluded public reaction to Bitcoin news headlines has an effect on the short-term price direction of the cryptocurrency. Investors can use my model to help them in their decision-making process when making short-term Bitcoin investment decisions.
ContributorsSteinberg, Sam (Author) / Boscovic, Dragan (Thesis director) / Davulcu, Hasan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Social media has become a direct and effective means of transmitting personal opinions into the cyberspace. The use of certain key-words and their connotations in tweets portray a meaning that goes beyond the screen and affects behavior. During terror attacks or worldwide crises, people turn to social media as a means of managing their anxiety, a mechanism of Terror Management Theory (TMT). These opinions have distinct impacts on the emotions that people express both online and offline through both positive and negative sentiments. This paper focuses on using sentiment analysis on twitter hash-tags during five major terrorist attacks that created a significant response on social media, which collectively show the effects that 140-character tweets have on perceptions in social media. The purpose of analyzing the sentiments of tweets after terror attacks allows for the visualization of the effect of key-words and the possibility of manipulation by the use of emotional contagion. Through sentiment analysis, positive, negative and neutral emotions were portrayed in the tweets. The keywords detected also portray characteristics about terror attacks which would allow for future analysis and predictions in regards to propagating a specific emotion on social media during future crisis.
ContributorsHarikumar, Swathikrishna (Author) / Davulcu, Hasan (Thesis director) / Bodford, Jessica (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Fraud is defined as the utilization of deception for illegal gain by hiding the true nature of the activity. While organizations lose around $3.7 trillion in revenue due to financial crimes and fraud worldwide, they can affect all levels of society significantly. In this dissertation, I focus on credit card fraud in online transactions. Every online transaction comes with a fraud risk and it is the merchant's liability to detect and stop fraudulent transactions. Merchants utilize various mechanisms to prevent and manage fraud such as automated fraud detection systems and manual transaction reviews by expert fraud analysts. Many proposed solutions mostly focus on fraud detection accuracy and ignore financial considerations. Also, the highly effective manual review process is overlooked. First, I propose Profit Optimizing Neural Risk Manager (PONRM), a selective classifier that (a) constitutes optimal collaboration between machine learning models and human expertise under industrial constraints, (b) is cost and profit sensitive. I suggest directions on how to characterize fraudulent behavior and assess the risk of a transaction. I show that my framework outperforms cost-sensitive and cost-insensitive baselines on three real-world merchant datasets. While PONRM is able to work with many supervised learners and obtain convincing results, utilizing probability outputs directly from the trained model itself can pose problems, especially in deep learning as softmax output is not a true uncertainty measure. This phenomenon, and the wide and rapid adoption of deep learning by practitioners brought unintended consequences in many situations such as in the infamous case of Google Photos' racist image recognition algorithm; thus, necessitated the utilization of the quantified uncertainty for each prediction. There have been recent efforts towards quantifying uncertainty in conventional deep learning methods (e.g., dropout as Bayesian approximation); however, their optimal use in decision making is often overlooked and understudied. Thus, I present a mixed-integer programming framework for selective classification called MIPSC, that investigates and combines model uncertainty and predictive mean to identify optimal classification and rejection regions. I also extend this framework to cost-sensitive settings (MIPCSC) and focus on the critical real-world problem, online fraud management and show that my approach outperforms industry standard methods significantly for online fraud management in real-world settings.
ContributorsYildirim, Mehmet Yigit (Author) / Davulcu, Hasan (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Huang, Dijiang (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Huge advancements have been made over the years in terms of modern image-sensing hardware and visual computing algorithms (e.g. computer vision, image processing, computational photography). However, to this day, there still exists a current gap between the hardware and software design in an imaging system, which silos one research domain from another. Bridging this gap is the key to unlocking new visual computing capabilities for end applications in commercial photography, industrial inspection, and robotics. This thesis explores avenues where hardware-software co-design of image sensors can be leveraged to replace conventional hardware components in an imaging system with software for enhanced reconfigurability. As a result, the user can program the image sensor in a way best suited to the end application. This is referred to as software-defined imaging (SDI), where image sensor behavior can be altered by the system software depending on the user's needs. The scope of this thesis covers the development and deployment of SDI algorithms for low-power computer vision. Strategies for sparse spatial sampling have been developed in this thesis for power optimization of the vision sensor. This dissertation shows how a hardware-compatible state-of-the-art object tracker can be coupled with a Kalman filter for energy gains at the sensor level. Extensive experiments reveal how adaptive spatial sampling of image frames with this hardware-friendly framework offers attractive energy-accuracy tradeoffs. Another thrust of this thesis is to demonstrate the benefits of reinforcement learning in this research avenue. A major finding reported in this dissertation shows how neural-network-based reinforcement learning can be exploited for the adaptive subsampling framework to achieve improved sampling performance, thereby optimizing the energy efficiency of the image sensor. The last thrust of this thesis is to leverage emerging event-based SDI technology for building a low-power navigation system. A homography estimation pipeline has been proposed in this thesis which couples the right data representation with a differential scale-invariant feature transform (SIFT) module to extract rich visual cues from event streams. Positional encoding is leveraged with a multilayer perceptron (MLP) network to get robust homography estimation from event data.
ContributorsIqbal, Odrika (Author) / Jayasuriya, Suren (Thesis advisor) / Spanias, Andreas (Thesis advisor) / LiKamWa, Robert (Committee member) / Owens, Chris (Committee member) / Arizona State University (Publisher)
Created2023
Description
Animal pose estimation (APE) is utilized in preclinical research settings for various neurological disorders such as Parkinson's disease (PD), Huntington's disease (HD) and multiple sclerosis. The technique includes real-time scoring of impairment in the animals during testing or video recording. This is a time-consuming operation prone to errors due to visual fatigue. To overcome these shortcomings, APE automation by deep learning has been studied. The field of APE has gone through significant development backed by improvements in deep learning techniques. These developments have improved 2D and 3D pose estimation, 3D mesh reconstruction and behavior prediction capabilities. As a result, there are numerous sophisticated tools and datasets available today. Despite these developments, APE still lags behind human observer scoring with respect to accuracy and flexibility under complex scenarios. In this project, two critical challenges are being addressed within the context of neurological research focusing on PD. The first challenge is about the lack of comprehensive diverse datasets necessary for accurate training as well as for fine-tuning deep learning models. This is compounded by the inherent difficulty in working with uncooperative rodent subjects, whose unpredictable behaviors often impede reliable data collection. The second challenge focuses on reduction in variation of scores that result from being scored by different evaluators. This will also involve tackling bias and reducing human error for the purpose of reliable and accurate assessments. In order to address these issues, systematic data collection and deep learning in APE have been utilized to automate manual scoring procedures. This project will contribute to neurological research, particularly in understanding and treating disorders like PD. The goal is to improve methods used in assessing rodent behavior which could aid in developing effective therapeutics. The successful implementation of an automated scoring mechanism could set a new standard in neurological research, offering insights and methodologies that are more accurate and reliable.
ContributorsJanapareddi, Ajay Kumar (Author) / Davulcu, Hasan (Thesis advisor) / Poste, George (Thesis advisor) / Kelley, Christy (Committee member) / Arizona State University (Publisher)
Created2024
Description
Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, a novel problem of ranking images with respect to their aesthetic quality is formulated in this work. A new data-set of image pairs with relative labels is constructed by carefully selecting images from the popular AVA data-set. Unlike in aesthetics classification, there is no single threshold which would determine the ranking order of the images across the entire data-set.
This problem is attempted using a deep neural network based approach that is trained on image pairs by incorporating principles from relative learning. Results show that such relative training procedure allows the network to rank the images with a higher accuracy than a state-of-art network trained on the same set of images using binary labels. Further analyzing the results show that training a model using the image pairs learnt better aesthetic features than training on same number of individual binary labelled images.
Additionally, an attempt is made at enhancing the performance of the system by incorporating saliency related information. Given an image, humans might fixate their vision on particular parts of the image, which they might be subconsciously intrigued to. I therefore tried to utilize the saliency information both stand-alone as well as in combination with the global and local aesthetic features by performing two separate sets of experiments. In both the cases, a standard saliency model is chosen and the generated saliency maps are convoluted with the images prior to passing them to the network, thus giving higher importance to the salient regions as compared to the remaining. Thus generated saliency-images are either used independently or along with the global and the local features to train the network. Empirical results show that the saliency related aesthetic features might already be learnt by the network as a sub-set of the global features from automatic feature extraction, thus proving the redundancy of the additional saliency module.
This problem is attempted using a deep neural network based approach that is trained on image pairs by incorporating principles from relative learning. Results show that such relative training procedure allows the network to rank the images with a higher accuracy than a state-of-art network trained on the same set of images using binary labels. Further analyzing the results show that training a model using the image pairs learnt better aesthetic features than training on same number of individual binary labelled images.
Additionally, an attempt is made at enhancing the performance of the system by incorporating saliency related information. Given an image, humans might fixate their vision on particular parts of the image, which they might be subconsciously intrigued to. I therefore tried to utilize the saliency information both stand-alone as well as in combination with the global and local aesthetic features by performing two separate sets of experiments. In both the cases, a standard saliency model is chosen and the generated saliency maps are convoluted with the images prior to passing them to the network, thus giving higher importance to the salient regions as compared to the remaining. Thus generated saliency-images are either used independently or along with the global and the local features to train the network. Empirical results show that the saliency related aesthetic features might already be learnt by the network as a sub-set of the global features from automatic feature extraction, thus proving the redundancy of the additional saliency module.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Davulcu, Hasan (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2016
Description
In trading, volume is a measure of how much stock has been exchanged in a given period of time. Since every stock is distinctive and has an alternate measure of shares, volume can be contrasted with historical volume inside a stock to spot changes. It is likewise used to affirm value patterns, breakouts, and spot potential reversals. In my thesis, I hypothesize that the concept of trading volume can be extrapolated to social media (Twitter).
The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock.
In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level.
The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock.
In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level.
ContributorsAwasthi, Piyush (Author) / Davulcu, Hasan (Thesis advisor) / Tong, Hanghang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2015
Description
With the advent of social media and micro-blogging sites, people have become active in sharing their thoughts, opinions, ideologies and furthermore enforcing them on others. Users have become the source for the production and dissemination of real time information. The content posted by the users can be used to understand them and track their behavior. Using this content of the user, data analysis can be performed to understand their social ideology and affinity towards Radical and Counter-Radical Movements. During the process of expressing their opinions people use hashtags in their messages in Twitter. These hashtags are a rich source of information in understanding the content based relationship between the online users apart from the existing context based follower and friend relationship.
An intelligent visual dash-board system is necessary which can track the activities of the users and diffusion of the online social movements, identify the hot-spots in the users' network, show the geographic foot print of the users and to understand the socio-cultural, economic and political drivers for the relationship among different groups of the users.
An intelligent visual dash-board system is necessary which can track the activities of the users and diffusion of the online social movements, identify the hot-spots in the users' network, show the geographic foot print of the users and to understand the socio-cultural, economic and political drivers for the relationship among different groups of the users.
ContributorsGaripalli, Sravan Kumar (Author) / Davulcu, Hasan (Thesis advisor) / Shakarian, Paulo (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2015