Maximizing Disagreement and Polarization in Social Media Networks using Double Deep Q-Learning
Bibliographic record
Abstract
In this paper, we consider reinforcement learning (RL) techniques to systematically analyze and enhance the levels of disagreement and polarization within social media ecosystems. The proposed methodology employs a Double Deep Q- Learning algorithm to strategically identify individuals within the network. This identification process is aimed at selecting agents for takeover and control, thereby orchestrating a scenario that culminates in the maximization of disagreement and polarization within the network. The social media network is modeled by an asynchronous and synchronous expressed and private opinion dynamics model. The model incorporates a dual-state update mechanism: a synchronous update process for the state representing an individual's private opinion and an asynchronous update process for the state that reflects the individual's publicly expressed opinion. The RL agent's observational capacity is limited to the expressed opinions of individuals and the quantifiable metric of their followers or connections. The proposed model is analyzed for varying topologies and convergence conditions. Simulations are provided to illustrate the results.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".