Analysing user retweeting behaviour on microblogs: prediction model and influencing features
Bibliographic record
Abstract
This paper explores the feasibility of predicting users' retweeting behaviour and ranks the influencing features affecting that behaviour. The four first-dimension features, namely author, text, recipient and relationship are extracted and split into 39 second-dimension features. This study then applies support vector machine (SVM) to build the prediction model. Data samples extracted from Sina Microblog platform are subsequently used to evaluate this prediction model and rank the 39 second-dimension features. The results show the recall rate of this model is 58.67%, the precision rate is 82.19%, and the F1 test value is 68.46%, which show that the performance of the prediction model is highly satisfactory. Moreover, results of ranking indicate four features affect retweeting behaviour of users: the active degree of microblog author, the similarity of interests between the author and the recipient, the active degree of microblog recipient and the similarity between the theme of microblog and the recipient's interest.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".