Online Communication

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.

Top climate scientists post their findings and views regularly on social media. These very scientists are met with tweets from those with opposing views, often containing vitriolic and false information. It is important that we can identify and characterize these tweets to understand the counter-narratives of climate change. We will address topics including false information, bot campaigns, and harassment.

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.

What’s the next big idea, and who’s going to discover it? Our project is trying to understand how researchers make new discoveries and innovate new ideas. To do that we will apply deep learning techniques for temporal knowledge graph learning (RE-NET, CyGNet, HINGE, StarE) to a huge citation network dataset. We have assembled a KG with 260M research papers, 270M authors, 700K fields. To learn representations, our training tasks include citation prediction, author collaboration prediction, and field of study prediction.

This new project attempts to understand the interaction between anti-vaxxers (anti-vaccination groups) and alt-right groups on platforms such as Facebook. The goal of this project is to understand how do these two types of fringe groups interact over the years, and how do their interactions and discourse evolve during the COVID-19 pandemic. It would be interesting to explore the longitudinal patterns of network/discourse co-evolution and how such patterns may change in times of dramatic events.In terms of data, I have access to Facebook’s historical data archive and I have collected a dataset that contains a list of anti-vaxxer (n=158) and alt-right groups’ (n=183) Facebook posts over 10 years (2010-2021). The dataset can be further expanded with additional help.

“Open-source intelligence (“OSINT”) is a rapidly growing area of cybersecurity. This project seeks to explore OSINT information available on GitHub. Specifically, we will build and analyze a dataset comprised of users on GitHub who show a specific interest in GitHub repos related to hacking artifacts. This dataset and social network analysis could help us determine what attributes lead to “black hat” — or malicious — cyber actors.”

Open-source intelligence (“OSINT”) is a rapidly growing area of cybersecurity. This project seeks to explore OSINT information available on GitHub. Specifically, we will build and analyze a dataset comprised of users on GitHub who show a specific interest in GitHub repos related to hacking artifacts. This dataset and social network analysis could help us determine what attributes lead to “black hat” — or malicious — cyber actors.

Cyber ontologies such as STIX and ATT&CK can represent complex relationships between cyber threat actors, attacks, and infrastructure. While such representations are easily processed by computers, cyber analysts often prefer dealing with written text. Natural language ontologies like FrameNet represent language in a structured manner as well, but frame specifications are often not specific enough for a given domain (like cybersecurity). In this project, students will learn about cybersecurity threat ontologies and build a GUI web app tool that annotates provided cyber threat documents. No previous knowledge of cybersecurity necessary!

Top climate scientists post their findings and views regularly on social media. These very scientists are met with tweets from those with opposing views, often containing vitriolic and false information. It is important that we can identify and characterize these tweets to understand the counter-narratives of climate change. We will address topics including false information, bot campaigns, and harassment.

Top climate scientists post their findings and views regularly on social media. These very scientists are met with tweets from those with opposing views, often containing vitriolic and false information. It is important that we can identify and characterize these tweets to understand the counter-narratives of climate change. We will address topics including false information, bot compaigns, and harassment.