Papers by Gregory Spell
An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets (2020.emnlp-main)
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| Challenge: | Legislator preferences are typically estimated as general ideology using roll call votes on legislation, but these measures fail to capture aspects of preferences not reflected in legislation, such as attitudes towards a sitting president. |
| Approach: | They propose an embedding-based method for measuring legislator attitudes using tweets . they model legislators' attitudes towards president Donald Trump as vector embeddables that interact with embeddibles for Trump himself constructed using a neural network from the text of his daily tweets. |
| Outcome: | The proposed model predicts the frequency and sentiment of tweets by comparing it to traditional measures of legislator preferences. |