Data Science & AI Ethics in Society

Partner: Media Cloud DS4CG 2023. With the current surge in multimodal media shared online, particularly from platforms like YouTube and Instagram, the need for multimodal hate speech detection systems has grown. We created an evaluation dataset of YouTube videos from specific types of content creators and compared how features from the audio and transcribed text in a video can be used to flag extreme speech using machine learning.

Partner: Media Cloud DS4CG 2023. In an increasingly digital landscape for media across the globe, the need for content moderation, misinformation detection, and bias analysis has risen. In partnership with Media Cloud, we evaluated existing tools for extracting author names in news articles across 10 languages, which allow researchers to analyze media based on the author. We designed a pipeline to execute byline extraction from the evaluated tools and determined that a multistep approach of heuristic and machine learning models will lead to the best byline extraction tool.

Partner: Co-Insights DS4CG 2023. This project takes a longitudinal approach to analyzing the #StopAsianHate hashtag on Twitter in order to understand changes in hashtag usage. We developed a model that converts text into embeddings, clusters the embeddings into groups, and links the similar groups to reveal data about context surrounding #StopAsianHate. By analyzing the main accounts driving conversation and identifying transitions in hashtags to discussions, we can better understand social media discussion mechanisms. 

As part of the Data Science for the Common Good summer program, students investigated potential issues in data-driven systems that contribute to algorithmic fairness, in order to help data practitioners understand and identify these issues in their data and the related machine learning tasks.