Modeling and analysis of interest-tag-driven information cocoons formation mechanisms
Bibliographic record
Abstract
The information cocoon phenomenon refers to the situation in social networks where, due to the combined effects of individual interest preferences and recommendation algorithms, information spreading becomes confined to specific interest groups, creating a closed information environment. To elucidate the formation mechanism of this phenomenon, this paper constructs a spreading dynamics model based on interest tags. We employ a unique encoding scheme to model user interest tags and introduce a tag-matching-based spreading mechanism to simulate interest-driven information spreading processes. Through simulation experiments, the study systematically analyzed the impact of different interest tags, spreading thresholds, and network types on the speed and coverage of information spreading. The results indicate that messages bearing prominent interest tags are more likely to exhibit local spreading within specific interest groups, while messages with weaker interest associations tend to exhibit greater spreading across groups. This mechanism-based discovery reveals the structural role of interest tags in the formation of information cocoons. Although the research is primarily based on simulation experiments, the findings align closely with the phenomenon of topic spreading on real-world social platforms. This provides a new theoretical perspective for understanding the evolutionary mechanisms of information cocoons and offers potential insights for optimizing recommendation systems and enhancing information diversity.
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 | 0.001 | 0.000 |
| 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".