Predicting Opinions in Social Networks Using Recurrent Neural Networks
Bibliographic record
Abstract
This paper studies the spread of opinions in social media networks through the lens of opinion dynamics. As more human interactions and public discourse move online, understanding opinion formation and evolution in social media is crucial for issues such as virtual marketing, information dissemination, and social security. We introduce a novel approach using recurrent neural networks (RNN) to monitor and predict interactions in these networks. Our method uses two configurations of RNN algorithms to predict the opinions of agents in an online social network, with results showing its effectiveness in predicting diverse opinions. The first configuration uses a sigmoid activation function to predict the binary opinions output (agree, disagree), while the second configuration uses the softmax function to predict more detailed opinions. For the simulation results, we considered a group of five agents interacting in the Twitter network on the subject of COVID-19. The social interaction for a 30-day period was captured and opinion dynamics prediction using the RNN was verified.
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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 | 0.001 |
| 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".