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Description
Social media refers computer-based technology that allows the sharing of information and building the virtual networks and communities. With the development of internet based services and applications, user can engage with social media via computer and smart mobile devices. In recent years, social media has taken the form

Social media refers computer-based technology that allows the sharing of information and building the virtual networks and communities. With the development of internet based services and applications, user can engage with social media via computer and smart mobile devices. In recent years, social media has taken the form of different activities such as social network, business network, text sharing, photo sharing, blogging, etc. With the increasing popularity of social media, it has accumulated a large amount of data which enables understanding the human behavior possible. Compared with traditional survey based methods, the analysis of social media provides us a golden opportunity to understand individuals at scale and in turn allows us to design better services that can tailor to individuals’ needs. From this perspective, we can view social media as sensors, which provides online signals from a virtual world that has no geographical boundaries for the real world individual's activity.

One of the key features for social media is social, where social media users actively interact to each via generating content and expressing the opinions, such as post and comment in Facebook. As a result, sentiment analysis, which refers a computational model to identify, extract or characterize subjective information expressed in a given piece of text, has successfully employs user signals and brings many real world applications in different domains such as e-commerce, politics, marketing, etc. The goal of sentiment analysis is to classify a user’s attitude towards various topics into positive, negative or neutral categories based on textual data in social media. However, recently, there is an increasing number of people start to use photos to express their daily life on social media platforms like Flickr and Instagram. Therefore, analyzing the sentiment from visual data is poise to have great improvement for user understanding.

In this dissertation, I study the problem of understanding human sentiments from large scale collection of social images based on both image features and contextual social network features. We show that neither

visual features nor the textual features are by themselves sufficient for accurate sentiment prediction. Therefore, we provide a way of using both of them, and formulate sentiment prediction problem in two scenarios: supervised and unsupervised. We first show that the proposed framework has flexibility to incorporate multiple modalities of information and has the capability to learn from heterogeneous features jointly with sufficient training data. Secondly, we observe that negative sentiment may related to human mental health issues. Based on this observation, we aim to understand the negative social media posts, especially the post related to depression e.g., self-harm content. Our analysis, the first of its kind, reveals a number of important findings. Thirdly, we extend the proposed sentiment prediction task to a general multi-label visual recognition task to demonstrate the methodology flexibility behind our sentiment analysis model.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Liu, Huan (Committee member) / Tong, Hanghang (Committee member) / Chang, Yi (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Due to vast resources brought by social media services, social data mining has

received increasing attention in recent years. The availability of sheer amounts of

user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information

in social networks could provide another rich source

Due to vast resources brought by social media services, social data mining has

received increasing attention in recent years. The availability of sheer amounts of

user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information

in social networks could provide another rich source in deriving implicit information

for social data mining. However, the vast majority of existing studies overwhelmingly

focus on positive links between users while negative links are also prevailing in real-

world social networks such as distrust relations in Epinions and foe links in Slashdot.

Though recent studies show that negative links have some added value over positive

links, it is dicult to directly employ them because of its distinct characteristics from

positive interactions. Another challenge is that label information is rather limited

in social media as the labeling process requires human attention and may be very

expensive. Hence, alternative criteria are needed to guide the learning process for

many tasks such as feature selection and sentiment analysis.

To address above-mentioned issues, I study two novel problems for signed social

networks mining, (1) unsupervised feature selection in signed social networks; and

(2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In

particular, I model positive and negative links simultaneously for user preference

learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and

implicit sentiment signals from signed social networks into a coherent model Signed-

Senti. Empirical experiments on real-world datasets corroborate the effectiveness of

these two frameworks on the tasks of feature selection and sentiment analysis.
ContributorsCheng, Kewei (Author) / Liu, Huan (Thesis advisor) / Tong, Hanghang (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2017
Description
Self-Driving cars are a long-lasting ambition for many AI scientists and engineers. In the last decade alone, many self-driving cars like Google Waymo, Tesla Autopilot, Uber, etc. have been roaming the streets of many cities. As a rapidly expanding field, researchers all over the world are attempting to develop more

Self-Driving cars are a long-lasting ambition for many AI scientists and engineers. In the last decade alone, many self-driving cars like Google Waymo, Tesla Autopilot, Uber, etc. have been roaming the streets of many cities. As a rapidly expanding field, researchers all over the world are attempting to develop more safe and efficient AI agents that can navigate through our cities. However, driving is a very complex task to master even for a human, let alone the challenges in developing robots to do the same. It requires attention and inputs from the surroundings of the car, and it is nearly impossible for us to program all the possible factors affecting this complex task. As a solution, imitation learning was introduced, wherein the agents learn a policy, mapping the observations to the actions through demonstrations given by humans. Through imitation learning, one could easily teach self-driving cars the expected behavior in many scenarios. Despite their autonomous nature, it is undeniable that humans play a vital role in the development and execution of safe and trustworthy self-driving cars and hence form the strongest link in this application of Human-Robot Interaction. Several approaches were taken to incorporate this link between humans and self-driving cars, one of which involves the communication of human's navigational instruction to self-driving cars. The communicative channel provides humans with control over the agent’s decisions as well as the ability to guide them in real-time. In this work, the abilities of imitation learning in creating a self-driving agent that can follow natural language instructions given by humans based on environmental objects’ descriptions were explored. The proposed model architecture is capable of handling latent temporal context in these instructions thus making the agent capable of taking multiple decisions along its course. The work shows promising results that push the boundaries of natural language instructions and their complexities in navigating self-driving cars through towns.
ContributorsMoudhgalya, Nithish B (Author) / Amor, Hani Ben (Thesis advisor) / Baral, Chitta (Committee member) / Yang, Yezhou (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to

Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Increasing misinformation in social media channels has become more prevalent since the beginning of the COVID-19 pandemic as countless myths and rumors have circulated over the internet. This misinformation has potentially lethal consequences as many people make important health decisions based on what they read online, thus creating an urgent

Increasing misinformation in social media channels has become more prevalent since the beginning of the COVID-19 pandemic as countless myths and rumors have circulated over the internet. This misinformation has potentially lethal consequences as many people make important health decisions based on what they read online, thus creating an urgent need to combat it. Although many Natural Language Processing (NLP) techniques have been used to identify misinformation in text, prompt-based methods are under-studied for this task. This work explores prompt learning to classify COVID-19 related misinformation. To this extent, I analyze the effectiveness of this proposed approach on four datasets. Experimental results show that prompt-based classification achieves on average ~13% and ~6% improvement compared to a single-task and multi-task model, respectively. Moreover, analysis shows that prompt-based models can achieve competitive results compared to baselines in a few-shot learning scenario.
ContributorsBrown, Clinton (Author) / Baral, Chitta (Thesis director) / Walker, Shawn (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05