Papers by Felix Drinkall
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing studies have shown that social media can help predict rises in infectious disease caseloads. |
| Approach: | They propose to use transformer-based language models to integrate infectious disease modelling into reddit embedding features in reddits in specific US states. |
| Outcome: | The proposed model outperforms other features at predicting upward trend signals in areas where epidemiological data is unreliable. |
Time Machine GPT (2024.findings-naacl)
Copied to clipboard
| Challenge: | Large language models are often trained on extensive, temporally indiscriminate text corpora . conventional methods for creating temporal adapted models depend on pre-training static models on time-specific data. |
| Approach: | They propose a series of point-in-time LLMs called TimeMachineGPT to be nonprognosticative . time-series forecasting and event prediction aim to infer a future state from past data . authors propose linguistically-based models that can be used to predict future events . |
| Outcome: | The proposed model is nonprognosticative and ensures it remains uninformed about future factual information and linguistic changes. |
Stories that (are) Move(d by) Markets: A Causal Exploration of Market Shocks and Semantic Shifts across Different Partisan Groups (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing attempts to model the relationship between the real world and written or spoken text have focused on more interpretable and simplistic text representations. |
| Approach: | They propose to link shifts in semantic embedding space to real-world market shocks and partisanship to shape predictions of market fluctuations. |
| Outcome: | The proposed model demonstrates that partisanship can influence the predictive power of text for market fluctuations and shape reactions to those same shocks. |