Narrative generation is an important field due to the high demand for stories in video game design and also in stories used in learning tools in the classroom. As these stories should contain depth, it is desired for these stories to ideally be more descriptive. There are tools that help with the creation of these stories, such as planning, which requires a domain as input, or GPT-3, which requires an input prompt to generate the stories. However, other aspects to consider are the coherence and variation of stories. To save time and effort and create multiple possible stories, we combined both planning and the Large Language Model (LLM) GPT-3 similar to how they were used in TattleTale to generate such stories while examining whether descriptive input prompts to GPT-3 affect the outputted stories. The stories generated are readable to the general public and overall, the prompts do not consistently affect descriptiveness of outputs across all stories tested. For this work, three stories with three variants each were created and tested for descriptiveness. To do so, adjectives, adverbs, prepositional phrases, and suboordinating conjunctions were counted using Natural Language Processing (NLP) tool spaCy for Part Of Speech (POS) tagging. This work has shown that descriptiveness is highly correlated with the amount of words in the story in general, so running GPT-3 to obtain longer stories is a feasible option to consider in order to obtain more descriptive stories. The limitations of GPT-3 have an impact on the descriptiveness of resulting stories due to GPT-3’s inconsistency and transformer architecture, and other methods of narrative generation such as simple planning could be more useful.

The growth in online job boards has made it easier than ever to find and apply for roles online. Unfortunately, since said job boards are, mainly, designed for hiring companies and not job applicants, the applicant interface is high friction and frustrating. With each company (and often each job) that a job-seeker applies for, they need to fill out an application form asking for the same information they have already provided countless times. This thesis explores the effectiveness of FuseApply, a web application and accompanying Chrome extension that reduces the friction involved in filling out these forms by automatically filling out a portion of job applications for users. Results from user experience testing with eleven Arizona State University (ASU) School of Computing and Augmented Intelligence students on real-world job applications demonstrated significant time savings and thus added value for users. On average, FuseApply saved users 33.09 seconds in time completing online job application forms, compared with manually filling them out. A one-tail T-test confirmed that this difference is statistically significant. Users also showed noticeable reduction in frustration with FuseApply. 72.7% of applicants said that they would use FuseApply in the future when applying for jobs, and comments were also positive. Business viability is less clear, as 63.6% of applicants said they would not pay for the software. Results demonstrate that FuseApply is useful and valuable software, but cast doubt on monetization plans.

Studies on underground forums can significantly advance the understanding of cybercrime workflow and underground economies. However, research on underground forums has concentrated on public information with little attention paid to users’ private interactions. Since detailed information will be discussed privately, the failure to investigate private interactions may miss critical intelligence and even misunderstand the entire underground economy. Furthermore, underground forums have evolved into criminal freelance markets where criminals trade illicit products and cybercrime services, allowing unsophisticated people to launch sophisticated cyber attacks. However, current research rarely examines and explores how criminals interact with each other, which makes researchers miss the opportunities to detect new cybercrime patterns proactively. Moreover, in clearnet, criminals are active in exploiting human vulnerabilities to conduct various attacks, and the phishing attack is one of the most prevalent types of cybercrime. Phishing awareness training has been proven to decrease the rate of clicking phishing emails. However, the rate of reporting phishing attacks is unexpectedly low based on recent studies, leaving phishing websites with hours of additional active time before being detected. In this dissertation, I first present an analysis of private interactions in underground forums and introduce machine learning-based approaches to detect hidden connections between users. Secondly, I analyze how criminals collaborate with each other in an emerging scam service in underground forums that exploits the return policies of merchants to get a refund or a replacement without returning the purchased products. Finally, I conduct a comprehensive evaluation of the phishing reporting ecosystem to identify the critical challenges while reporting phishing attacks to enable people to fight against phishers proactively.

Sports analytics is a growing field that attempts to showcase interesting aspects of a sport with the use of modern technology and machine learning techniques. This thesis will demonstrate how the NBA has progressed in the past decade by comparing the performance have five teams (SAS, OKC, PHO, MIN, and SAC). It will also provide key insight on what an NBA team should focus on to build an optimized NBA team composition, which will better their performance in the league, which will improve their chances of making into the playoffs. These teams were chosen after conducting extensive analysis on all NBA teams. These five teams were chosen because of the variability in performance (two successful and three less successful teams). Two successful teams, SAS and OKC, and three less successful teams, PHO, MIN, and SAC, were chosen to exemplify the different approaches of teams in the NBA and to distinguish what an NBA team should consider build an optimized team composition to better their performance in the league stage.

Digital media refers to any form of media which depends on electronic devices for its creation, distribution, view, and storage. Digital media analytics involves qualitative and quantitative analysis from the business to understand users’ behaviors. This technique brings disruptive changes to many industries and its path of economic disruption is getting wider and wider. Under the context of the increasingly popular digital media market, this dissertation investigates what are the best content delivery strategy and the new cultural phenomenon: Internet Water Army. The first essay proposes a theory-guided computational approach that consolidates distinct data sources spanning unstructured text, image, and video data, systematically measures modes of persuasion, and unveils the multimedia content design strategies for crowdfunding projects. The second essay studies whether using the Internet Water Army helps sales and under what conditions it helps. This study finds that the Internet water army helps product sales at both post-level and fans-level. The effect is largely reflected by changing the number of emotional fans. Furthermore, the earlier to purchase the water armies, more haters, likers, and neutral fans it can attract. The last essay builds a game model to study the trade- off between honestly promoting the product according to their evaluation and catering to the consumer’s prior belief on the product quality to stay on the market as long as possible. It provides insights on the optimum usage of promotion on social media and demonstrate how conventional wisdom about negative reviews will hurt business may be misleading in the presence of social media. These three studies jointly contribute to the crowdfunding and social media studies literature by elucidating the content delivery strategy, and the impact and purchasing strategy of the Internet Water Army.

The use of Artificial Intelligence in assistive systems is growing in application and efficiency. From self-driving cars, to medical and surgical robots and industrial tasked unsupervised co-robots; the use of AI and robotics to eliminate human error in high-stress environments and perform automated tasks is something that is advancing society’s status quo. Not only has the understanding of co-robotics exploded in the industrial world, but in research as well. The National Science Foundation (NSF) defines co-robots as the following: “...a robot whose main purpose is to work with people or other robots to accomplish a goal” (NSF, 1). The latest iteration of their National Robotics Initiative, NRI-2.0, focuses on efforts of creating co-robots optimized for ‘scalability, customizability, lowering barriers to entry, and societal impact’(NSF, 1). While many avenues have been explored for the implementation of co-robotics to create more efficient processes and sustainable lifestyles, this project’s focus was on societal impact co-robotics in the field of human safety and well-being. Introducing a co-robotics and computer vision AI solution for first responder assistance would help bring awareness and efficiency to public safety. The use of real-time identification techniques would create a greater range of awareness for first responders in high-stress situations. A combination of environmental features collected through sensors (camera and radar) could be used to identify people and objects within certain environments where visual impairments and obstructions are high (eg. burning buildings, smoke-filled rooms, ect.). Information about situational conditions (environmental readings, locations of other occupants, etc.) could be transmitted to first responders in emergency situations, maximizing situational awareness. This would not only aid first responders in the evaluation of emergency situations, but it would provide useful data for the first responder that would help materialize the most effective course of action for said situation.

The purpose of this thesis is to imagine and predict the ways in which humans will utilize technology to feed the world population in the 21st century, in spite of significant challenges we have not faced before. This project will first thoroughly identify and explain the most pressing challenges the future will bring in climate change and population growth; both projected to worsen as time goes on. To guide the prediction of how technology will impact the 21st century, a theoretical framework will be established, based upon the green revolution of the 20th century. The theoretical framework will summarize this important historical event, and analyze current thought concerning the socio-economic impacts of the agricultural technologies introduced during this time. Special attention will be paid to the unequal disbursement of benefits of this green revolution, and particularly how it affected small rural farmers. Analysis of the technologies introduced during the green revolution will be used to predict how 21st century technologies will further shape the agricultural sector. Then, the world’s current food crisis will be compared to the crisis that preceded the green revolution. A “second green revolution” is predicted, and the agricultural/economic impact of these advances is theorized based upon analysis of farming advances in the 20th century.

Bots and networks of bots (known as a botnet) are a powerful tool in the world of misinformation. However, there are methods being developed to counter these tools. One such method is the use of Artificial Intelligence and machine learning to automatically filter, block, and identify bot accounts and bot posts. Since the influx of bot posts and videos is too much for hired people to handle in any way that is financially reasonable for a company, AI can be the key to preventing the spread of information.

YouTube video bots have been constantly generating bot videos and posting them on the YouTube platform. While these bot-generated videos negatively influence the YouTube audience, they cost YouTube extra resources to host. The goal for this project is to build a classifier that identifies bot-generated channels based on a deep learning-based framework. We designed the framework to take text, audio, and video features into account. For the purpose of this thesis project, we will be focusing on text classification work.

Cryptocurrencies are notorious for its volatility. But with its incredible rise in price, Bitcoin keep being on the top among the trending topics on social media. Although doubts continue to rise with price, Bloomberg even make critics on Bitcoin as ‘the biggest bubble in the history’, some investors still hold strong enthusiasm and confidence towards Bitcoin. As contradicting opinions increase, it is worthy to dive into discussions on social media and use a scientific method to evaluate public’s non-negligible role in crypto price fluctuation.

Sentiment analysis, which is a notably method in text mining, can be used to extract the sentiment from people’s opinion. It then provides us with valuable perception on a topic from the public’s attitude, which create more opportunities for deeper analysis and prediction.

The thesis aims to investigate public’s sentiment towards Bitcoin through analyzing 10 million Bitcoin related tweets and assigning sentiment points on tweets, then using sentiment fluctuation as a factor to predict future crypto fluctuation. Price prediction is achieved by using a machine learning model called Recurrent Neural Network which automatically learns the pattern and generate following results with memory. The analysis revels slight connection between sentiment and crypto currency and the Neural Network model showed a strong connection between sentiment score and future price prediction.