Learning and Forecasting Opinion Dynamics in Social Networks
Authors: Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez Rodriguez
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on data gathered from Twitter show that our model provides a good fit to the data and our formulas achieve more accurate forecasting than alternatives. |
| Researcher Affiliation | Academia | IIT Kharagpur MPI for Software Systems {abir.de,niloy,sourangshu}@cse.iitkgp.ernet.in {ivalera,manuelgr}@mpi-sws.org |
| Pseudocode | Yes | Appendix H summarizes the overall estimation algorithm. Appendix I summarizes the overall simulation algorithm. |
| Open Source Code | No | The paper does not provide explicit statements or links for open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using 'five Twitter datasets about current real-world events (Politics, Movie, Fight, Bollywood and US)' which were gathered from Twitter, but does not provide specific access information (e.g., URL, DOI, specific citation to a publicly available dataset) for these collected datasets. |
| Dataset Splits | No | The paper mentions 'cross-validation' for setting decay parameters but does not provide specific percentages or sample counts for a distinct validation split. |
| Hardware Specification | Yes | The experiments are carried out in a single machine with 24 cores and 64 GB of main memory. |
| Software Dependencies | No | The paper mentions using 'a popular sentiment analysis toolbox, specially designed for Twitter [13]' but does not provide specific version numbers for this or any other software dependency. |
| Experiment Setup | Yes | Here, we set the decay parameters of the exponential triggering kernels κ(t) and g(t) by cross-validation. |