Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification
Authors: Karish Grover, S M Phaneendra Angara, Md Shad Akhtar, Tanmoy Chakraborty
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 22,898 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,068 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four different social-text classification tasks, namely detecting fake news, hate speech, rumour, and sarcasm, show that Hyphen generalises well, and achieves state-of-the-art results on ten benchmark datasets. |
| Researcher Affiliation | Collaboration | Karish Grover IIIT Delhi India EMAIL S.M. Phaneendra Angara Linked In India EMAIL Md. Shad Akhtar IIIT Delhi India EMAIL Tanmoy Chakraborty IIT Delhi India EMAIL |
| Pseudocode | No | The paper describes the architecture and processes in text and diagrams but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/LCS2-IIIIITD/Hyphen. |
| Open Datasets | Yes | We evaluate the performance of Hyphen on four different social-text classification tasks across ten datasets (c.f. Table 1) (i) fake news detection (Politifact [43], Gossipcop [43], Anti Vax [44]), (ii) hate speech detection (HASOC [45]), (iii) rumour detection (Pheme [46], Twitter15 [47], Twitter16 [47], Rumour Eval [48]), and (iv) sarcasm detection (Fig Lang-Twitter[49], Fig Lang-Reddit [49]). |
| Dataset Splits | No | The paper mentions 'early stopping patience of 10 epochs', which implies the use of a validation set, but it does not provide specific details on the training, validation, or test data splits (e.g., percentages or sample counts) in the main text. |
| Hardware Specification | Yes | We run all experiments for 100 epochs with early stopping patience of 10 epochs, on a NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | The paper mentions using 'Riemannian Adam from Geoopt [61]' for optimization, but it does not specify version numbers for Geoopt or any other software dependencies like Python, PyTorch, etc. |
| Experiment Setup | Yes | To find the optimal k (latent dimension, see Equation 5) for hyperbolic co-attention, we run grid search over k = 50, 80, 128, 256, and finally use k = 128. For HGCN, we use two layers with curvatures K1 = K2 = -1. We run all experiments for 100 epochs with early stopping patience of 10 epochs. |