A Probabilistic Model for Information Diffusion in Social Networks: Insights From Twitter Data
Bibliographic record
Abstract
Social networks have become a part of the daily lives of most people and are a significant influence in a variety of fields including economics, culture, and politics. This has motivated research on social networks. One aspect is evaluating the impact of messages on society which is a function of the information spread. Thus, a probabilistic model is proposed for the information spread in a social network. In this model, the probability of a user retweeting is based on metrics such as the trending degree, the importance of users retweeting the message, message freshness, and the influence of users on each other. The message viewing time is also considered as it is a critical spread factor. We propose three algorithms based on the proposed model. The first is based on a Kalman Filter that simply estimates the number of retweeting users in the near future. The second considers in what order and at what times users retweet the message. The third employs some simplifications to reduce the complexity of the second algorithm. A real Twitter dataset is used to evaluate the performance. The results obtained show that the proposed model accurately predicts the number of users who spread a message
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".