Predicting Amazon’s Choice of HQ2 From Social Media: Evidence From the Tweets of Informed Sources
Bibliographic record
Abstract
Social media chatter, and in particular, Twitter, is increasingly gaining popularity to generate forecasts in a wide variety of domains. We build on this body of work and set out to predict Amazon’s HQ2 choice by analyzing the tweets of officials at the 20 finalist cities. Consistent with the affect infusion model (AIM) from the psychology literature, we conceptualize that the positive affect generated in successful ongoing negotiations will lead to a congruent positive spill over even in unrelated tweets. Analyzing tweet series that include a corpus of 50,238 tweets and incorporating dynamic time warping measures, our forecasting method correctly predicts Northern Virginia, favors it over two proximal cities, Washington D.C. and Baltimore, and ranks New York City 11 th out of 20 cities. These forecasts match those of the betting markets. Our research thus offers an alternate and novel approach to extracting the signal from the noise in social media.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".