Scalable Misinformation Mitigation in Social Networks Using Reverse Sampling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We consider misinformation propagating through a social network and study the problem of its prevention. The goal is to identify a set of $k$ users that need to be convinced to adopt a limiting campaign so as to minimize the number of people that end up adopting the misinformation. This work presents Reverse Prevention Sampling (RPS), an algorithm that provides a scalable solution to the misinformation mitigation problem. Our theoretical analysis shows that RPS runs in $O((k + l)(n + m)(\frac{1}{1 - \gamma }) \log n / \epsilon ^2 )$ expected time and returns a $(1 - 1/e - \epsilon )$-approximate solution with at least $1 - n^{-l}$ probability (where $\gamma $ is a typically small network parameter and $l$ is a confidence parameter). The time complexity of RPS substantially improves upon the previously best-known algorithms that run in time $\Omega (m n k \cdot POLY(\epsilon ^{-1}))$. We experimentally evaluate RPS on large datasets and show that it outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation mitigation can be made practical while still offering strong theoretical guarantees.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it