Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Pinpointing Fine-Grained Relationships between Hateful Tweets and Replies
Authors: Abdullah Albanyan, Eduardo Blanco10418-10426
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show improvements (a) taking into account the hateful tweet in addition to the reply and (b) pretraining with related tasks. |
| Researcher Affiliation | Academia | Abdullah Albanyan1, Eduardo Blanco2 1 University of North Texas 2 Arizona State University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Corpus and implementation available at https://github.com/albanyan/hateful-tweets-replies |
| Open Datasets | Yes | The main contribution of this paper are:4 (a) a corpus of 5,652 replies to hateful tweets published by real users and annotated with finegrained relationship information... Corpus and implementation available at https://github.com/albanyan/hateful-tweets-replies |
| Dataset Splits | Yes | we split the dataset as follows: 70% for training, 10% for validation, and 20% for testing. |
| Hardware Specification | No | The paper mentions using BERT-based transformers and deep learning libraries but does not specify any hardware details such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like the Transformers library by Hugging Face, TensorFlow, and PyTorch, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | No | The paper states, 'We report hyperparameters and other implementation details in the supplementary materials,' indicating these details are not provided within the main text of the paper. |