Predicting Political Donations Using Twitter Hashtags and Character N-Grams
Bibliographic record
Abstract
We describe a novel approach for predicting politicaldonations and performing psychographic segmentation basedon social data linked to election donation records. The role ofmicroblogs in enterprise informatics, specifically in relation tocustomer relationship systems is highlighted. Algorithms trainedon social data can be used to interpret and detect prospects' psychographicinformation. Contrasted with past approaches whichfocused exclusively on a single source of social data, the methodbeing presented allows us to use an objective gold standard bylinking Twitter and election records. Two experiments were conductedusing data collected from 438 Twitter users, half of whichare linked with donation event records collected from the UnitedStates Federal Election Commission. Probabilistic, entropy andkernel approaches were tested for predictive accuracy, while theCNG technique is explored as an alternative. The CNG algorithmwas found to predict political affiliation 17 percentage pointsabove the majority classifier, exceeding benchmarks suggestedby the literature. A NaïveBayes word n-gram approach wasfound to outperform CNG at predicting donations by predictingpolitical donations. Insufficient performance and poor reliabilityof standard word n-gram techniques in opinion detection revealskepticism about past work on political affiliation analysis fromsocial data alone. This suggests that prospecting systems maybenefit from constructing algorithms using data linked to external sources.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".