Ethics/Equity

While many stories involve the friendly and familiar, scary stories across cultures, from Hamlet to Yotsuya Kaidan to Siren Head involve beings that are almost—but not quite—human. Can these stories give us insight into the “nearly-human” uncanny valley? And are the most popular stories at the nadir of this valley? In this project we aim to explore the uncanny valley through analysis of several thousand stories posted on Reddit posted over a decade. These data contain stories that cross a range of topics, and include user comments and story scores. We will explore the prevalence of the monsters over time, and explore whether there is some optimal characteristics of these monsters that make them so scary. While some research has explored the uncanny valley for images, the research is limited and virtually unexplored in text format. The students will build on initial work by the advisor to apply NLP methods to these texts and improve upon existing initial results.

The broadband access market is increasingly dominated by a few large ISPs. However, small and medium-size operators (“microtelcos”) are critical to connectivity in low-income and rural communities in CA, serving markets of little interest to large operators. The primary goal of this project is to combine broadband infrastructure deployment data from the CPUC (California Public Utilities Commission) and socioeconomic data from the Census Bureau to understand the characteristics of the communities served by microtelcos, and to analyze whether the presence of a microtelco operator contributes to higher levels of connectivity in the community. The main technical challenge is to combine spatial data found in CPUC files with census block level data provided by the Census Bureau. This is part of an ongoing research program called Connected Communities and Inclusive Growth (CCIG).

This project will result in a visual analytics toolkit that will enable social scientists to understand the cultural groups and biases at play in a social dataset. News, books, and social media all contain biases that stem from the cultural background of the author(s). We have developed algorithms to identify the cultural groups at play in an arbitrary dataset, as well as natural language processing approaches that can discover the biases of each group. This project would help bring put these tools into the hands of social scientists by displaying the output of these algorithms in novel visualizations.

Giorgos and John are developing a machine learning system to automatically detect, prioritize, and alert journalists in the presence of abnormalities in crime data. Through this project, they want to assist journalists to identify interesting stories in data. The data is rich in features, and by using general feature engineering.

Spread of misinformation has become a significant problem, raising the importance of relevant detection methods. While there are different manifestations of misinformation, in this work we focus on detecting face manipulations in videos. This project will focus on detecting face manipulations in videos. We exploit the temporal dynamics of videos with recurrent networks.