We test three ML models on 48K congressional bills to see how well they can predict policy areas from bill text. Results show logistic regression performs best, with a certified weighted-F1 of ~0.88 within-Congress (0.877) and ~0.87 out-of-Congress (0.871).
Only 2% of congressional bills become law. We analyze 15K bills from 2021-2023 to understand what drives legislative success and failure.
A 47,000-bill dataset from Congress.gov with a co-sponsorship graph and TF-IDF baselines for 33-class policy-area classification, at 0.88 weighted F1. On Hugging Face.
Bachelor’s thesis introducing PyConversations, an open-source library that normalizes over 308 million posts from Twitter, Reddit, Facebook, and 4chan into a unified data model for cross-platform social media research.
We introduce NewsTweet, a dataset and pipeline for studying embedded tweets in digital journalism, revealing that 13% of Google News articles incorporate tweets and providing insights into how social media becomes newsworthy.
We developed high-frequency monitoring tools to detect coordinated manipulation on Twitter, documenting anomalous follower patterns including sub-second spikes, sawtooth waves, circulating accounts, and dormant pre-2010 accounts resurfacing in bulk on high-profile political accounts.