metadata

license: apache-2.0
language:
  - en
metrics:
  - accuracy
pipeline_tag: text-classification
widget:
  - text: >-
      On Unifying Misinformation Detection. In this paper, we introduce
      UNIFIEDM2, a general-purpose misinformation model that jointly models
      multiple domains of misinformation with a single, unified setup. The model
      is trained to handle four tasks: detecting news bias, clickbait, fake news
      and verifying rumors. By grouping these tasks together, UNIFIEDM2 learns a
      richer representation of misinformation, which leads to stateof-the-art or
      comparable performance across all tasks. Furthermore, we demonstrate that
      UNIFIEDM2's learned representation is helpful for few-shot learning of
      unseen misinformation tasks/datasets and model's generalizability to
      unseen events.
    example_title: Misinformation Detection

SciBERT NLP4SG

SciBERT NLP4SG is a SciBERT model fine-tuned to detect NLP4SG papers based on their title and abstract.

We present the details in the paper:

The training corpus is a combination of the NLP4SGPapers training set which is manually annotated, and some papers identified by keywords.

For more details about the training data and the model, visit the original repo here.

Please cite the following paper:

@misc{gonzalez2023good,
      title={Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good}, 
      author={Fernando Gonzalez and Zhijing Jin and Jad Beydoun and Bernhard Schölkopf and Tom Hope and Mrinmaya Sachan and Rada Mihalcea},
      year={2023},
      eprint={2305.05471},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}