README.md

---
license: mit
task_categories:
  - text-classification
language:
  - en
tags:
  - politics
  - tweets
pretty_name: Political Slant Tweets Dataset
size_categories:
  - 100K<n<1M
features:
  - name: text
    dtype: string
  - name: raw_text
    dtype: string
  - name: filename
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          "0": republican
          "1": democratic
configs:
  - config_name: style
    default: true
    data_files:
      - split: train
        path:
          - political_data/democratic_only.train.en.parquet
          - political_data/republican_only.train.en.parquet
      - split: eval
        path:
          - political_data/democratic_only.dev.en.parquet
          - political_data/republican_only.dev.en.parquet
      - split: test
        path:
          - political_data/democratic_only.test.en.parquet
          - political_data/republican_only.test.en.parquet
  - config_name: classifier
    data_files:
      - split: train
        path: political_data/classtrain.txt.parquet
      - split: eval
        path:
          - political_data/democratic_only.dev.en.parquet
          - political_data/republican_only.dev.en.parquet
      - split: test
        path:
          - political_data/democratic_only.test.en.parquet
          - political_data/republican_only.test.en.parquet
---

# Political Slant Tweets Dataset

Political slant transfer dataset. Contains two classes of political tweets between Democratic and Republican Politicans. This dataset can be used for classification tasks.

This is taken directly from the below:

    http://tts.speech.cs.cmu.edu/style_models/political_data.tar
    tar -xvf political_data.tar

I can't take credit for this dataset; this is largely the work of Prabhumoye et al. (See References below). I have detokenized (via MosesDetokenizer), and added relevant labels for creature comforts.

## Reference

    @inproceedings{style_transfer_acl18,
      title={Style Transfer Through Back-Translation},
      author={Prabhumoye, Shrimai and Tsvetkov, Yulia and Salakhutdinov, Ruslan and Black, Alan W},
      year={2018},
      booktitle={Proc. ACL}
      }
    
    @inproceedings{rtgender,
      title={{RtGender}: A Corpus for Studying Differential Responses to Gender},
      author={Voigt, Rob and Jurgens, David and Prabhakaran, Vinodkumar and Jurafsky, Dan and Tsvetkov, Yulia},
      year={2018},
      booktitle={Proc. LREC},
      }