Bibliographic record
Abstract
Information propagation in a microblog network aims to identify a set of seed users for propagating a target message to as many interested users as possible. This problem differs from the traditional influence maximization in two major ways: it has a content-rich target message for propagation and it treats each link in the network as communication on certain topics and emphasizes the topic relevance of such communication in propagating the target message. In realistic situations, however, the topics associated with a link are not explicitly expressed but are hidden in the microblogs previously exchanged through the link. In this paper, we present a topic-aware solution to information propagation in a microblog network. We first model the latent topic structure of the network using observed microblog messages published in the network. We then present two methods for estimating the propagation probability based on the topic relevance between a link and the target message. Once the propagation probability is estimated, we adopt the standard greedy algorithm for influence maximization to find seed users. This approach is topic-aware in that the target message finds its way of propagation according to its topic relevance to the latent topic structure in the network. Experiments conducted on real Twitter datasets suggest that the proposed methods are able to select right seed users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".