Modeling information spread in polarized communities: Transitioning from legacy media to a Facebook world
Bibliographic record
Abstract
Rumors have played an important role in social life for centuries, with early examples including their use to steer Roman politics. Today's world includes entire industries focused on digital misinformation, whose rumors can spread quickly via social networks such as Facebook not only because of their structure (e.g., clustering) but also because individuals can place an excessively high trust in information originating from their friends. Relaying information from our friends and ignoring or being unaware of other opinions leads to polarized groups, such as liberals or conservatives in a political context. While numerous models of rumor spreads have been proposed, their focus was more often on the conditions to stop/verify one rumor than in accounting for a polarized context. In this paper, we develop a new model of rumor spread with two different susceptibility rates, which can be used to investigate cases in which the population can be sub-divided with respect to one rumor (e.g. based on political opinions or socio-economic factors such as educational attainment). We describe the dynamics of the model using differential equations, and present numerical results regarding the model behavior with respect to key parameters such as the rate with which rumors are forgotten. While our work took into account network features (e.g., average degree), it is of particular interest for future work to examine the interplay between the network structure and the distribution of susceptibility rates.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".