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Auditing web content promoting eating disorders
The pandemic worsened eating disorders among adolescents, particularly girls. Eating disorders, a psychiatric illness where an adolescent tries to control their weight with severe food restriction (anorexia) or purging (bulimia), can be fatal. How much does web content and algorithms that power web search contribute to eating disorders? Imagine a girl who is unhappy about her weight and looks for dieting tips online. It does not take long for search algorithms to lead her to extreme content that promotes anorexia and extreme weight loss. The goal of this project is to audit the web for potentially harmful content and communities that promote eating disorders.
Auditing web content promoting eating disorders
Determinants of spatial variations in broadband quality and prices
There is anecdotal evidence that broadband service offers vary in quality and price along income and racial lines. This project seeks to validate these claims by collecting data about service speeds and prices for all known serviceable locations in Los Angeles County, and merging with sociodemographic variables from the Census Bureau and other sources. Other researchers have already developed prototype code for scraping data from ISP’s websites, but the study only covered a small percentage of addresses in LA County. The analysis will probe for evidence of a “poverty penalty’ whereby residents of poorer areas in Los Angeles are offered higher cost/lower quality broadband services.
Determinants of spatial variations in broadband quality and prices
Natural language processing of safety reports in nuclear plants and aviation
This project will focus on extracting concise structured information about incidents in nuclear plants and in aviation that are currently described in lengthy document reports. By structuring this information and mapping it to safety models and standards, we can help improve their operations.
Transition of Care
Analytics to determine what tertiary and quaternary care patients would have the best outcomes at Keck Medical Center. Analyze data from incoming transfer patients to look at data quality, evaluate clinical records and predict outcomes.
Transition of Care
Automatic Discovery of News Articles: Case of Policy Misconduct

The PMR (Police Misconduct Registry) is a database of officers who have been terminated or have resigned in lieu of being fired for misconduct. The objective of PMR is to increase the public trust and legitimacy of law enforcement officers serving the community while also helping departments hire the best possible candidates. The PMR is continually populated with all instances of police misconduct anywhere in the United States.

Currently, data entries are manually identified, discovered and registered using public, open-sourced information, mostly news articles on the web, which critically limits its data collection process. Thus, this project aims at automating the discovery of such data with an efficient identification mechanism. Working with the Price School of Policy, we will implement an automatic identification mechanism to effectively search police misconduct articles utilizing web crawling/scarping and natural language processing.

Characterizing Online Attitudes, Expectations, and Concerns about Novel Medical Treatments
Novel, or hypothesized medical treatments, such as COVID-19 vaccines and male contraception, are regularly discussed on social media. For example, on the AskReddit subreddit, questions of the form “”Would you take [x] if it existed?”” Aside from willingness to use these novel treatments, the answers to these questions contain important clues to peoples’ latent concerns and barriers to adoption of novel medications. Understanding them can provide crucial information about how to introduce, communicate, and counsel about new medications when they come to market. In this project you will use pre-collected Reddit data spanning 10 years to answer questions including: What concerns do individuals have about a novel medication? How do these concerns vary by demographics, such as cultural background? How have these concerns evolved over time? What has caused users to become more or less accepting of the treatment over time?
Identification and characterization of cross-platform misinformation diffusion
Fringe communities are often the sources of conspiracy theories and extreme ideas. Niche online platforms hosting such communities represent suitable incubators for reinforcing questionable stories and to ultimately push them into the mainstream. Over the years, information pathways from fringe to mainstream media have significantly increased, enabling the proliferation of harmful content. This project aims at developing novel network- and AI-based models for identifying and characterizing information pathways that enable the proliferation of potentially harmful content on online media channels. Mainstream social media (Twitter, Facebook, and Instagram), video streaming platforms (YouTube and Bitchute), niche platforms (Gab, 4chan, and Parler), and messaging apps (Telegram) will be considered to investigate how harmful narratives flow across diverse platforms and predict those that will gain traction on mainstream media.
Identification of Sustainability-Related Research at USC through Machine Learning and Keyword Mapping
Are you passionate about data science and sustainability? Then this interdisciplinary project is for you! Here, we will develop a machine learning program to identify USC research publications and grants as ‘sustainability-focused’, ‘sustainability-inclusive’ or ‘not-sustainability-related’ by using pre-categorized publication samples. In addition, we will use keyword lists that relate to the 17 UN Sustainable Development Goals (SDGs) to map all research groups at USC as they relate to these SDGs (https://sdgs.un.org/goals). Lastly, we will create an interactive dashboard in R Shiny that will act as a public directory of all research at USC with classification of the research by the SDGs and broader sustainability categorization. As an example, check our github for USC curriculum: https://github.com/USC-Office-of-Sustainability/USC-SDGmap . Your work on this project is critical in boosting sustainability-related research at USC and thereby achieving our Asgmt: Earth Research Goals."
Identification of Sustainability-Related Research at USC through Machine Learning and Keyword Mapping
Knowledge-powered understanding of diet’s water footprint
Food production and their supply chains are the main cause of water pollution, especially in emerging and developed countries. Understanding the connection between our diet and water pollution is extremely challenging because it requires combining knowledge about food production, supply chains, and transportation. As more and more people seek to live sustainably, there is a need to inform consumers about the planetary impacts of their choices. We propose to construct a knowledge graph and application to create a water footprint calculator for our dietary choices that computes the water footprint for each ingredient of a meal proposed by the user and suggest alternatives to reduce the water footprint (e.g., replacing beef by pork in a cheesesteak meal will save the planet 1,600 gallons of water).
Quantum Natural Language Processing for Fake News Identification
Advancements in artificial intelligence, especially neural networks, have enabled more intelligent models that can distinguish between fake and real information. However, these models suffer from over-fitting: a phenomenon where models memorize certain patterns in the dataset instead of understanding the actual underlying task.This prevents the models from generalizing well, especially across domains. Quantum Natural Language Processing (QNLP) is a very nascent field where quantum computers solve NLP problems. It has been shown that QNLP models have been able to solve many of the aforementioned tasks difficult for neural networks to solve. This is attributed to the fact that QNLP models naturally incorporate rich linguistic meanings and structure. In this project we will create neural network like models for QNLP. This will be done on fact verification datasets, with the goal of improving the quality of fake news identification.