Pinpointing Fine-Grained Relationships between Hateful Tweets and Replies
Authors: Abdullah Albanyan, Eduardo Blanco10418-10426
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |