Agent-based simulation of twitter for building effective recommender system
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
This paper presents a framework to extract real-time information found in tweets using a multi-agent system for simulation of both a Twitter user and its attached agent. Agents communicate with each other and finally the framework is used to provide recommendation to its users. This process consists of the following tasks: extraction of distributed data using multi-agents from a social network website (i.e. Twitter), data cleansing, tweet analysis using Term Frequency/Inverse Document Frequency (TF/IDF), and providing recommendations. The aim of the proposed framework is to simulate Twitter users and analyze different information retrieval methods in a real-time distributed environment. Therefore, this simulator is capable of evaluating the performance metrics of the information retrieval methods together with scalability and distributed processing effectiveness. Simulation of Twitter users and reporting the scalability of information retrieval methods for processing the tweets are the new ideas presented in this paper.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it