State of the art propaganda and hyperpartisan news detection with PyTorch.
Attention plots: blue indicates sentence-wise attention, and red token-wise.
| Example Hyperpartisan Article | Example Mainstream Article |
|---|---|
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 |
On a new Python3.7 virtualenv, run:
pip install -r requirements.txt
Code used in the following papers:
- On Document Representations for Detection of Biased News Articles
- On Sentence Representations for Propaganda Detection: From Handcrafted Features to Word Embeddings
- Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection
Bibtex (for latest paper):
@inproceedings{10.1145/3341105.3374025,
author = {Cruz, Andr\'{e} Ferreira and Rocha, Gil and Cardoso, Henrique Lopes},
title = {On Document Representations for Detection of Biased News Articles},
year = {2020},
isbn = {9781450368667},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3341105.3374025},
doi = {10.1145/3341105.3374025},
booktitle = {Proceedings of the 35th Annual ACM Symposium on Applied Computing},
pages = {892–899},
numpages = {8},
keywords = {deep learning, bias detection, hyperpartisan news, document representation, natural language processing},
location = {Brno, Czech Republic},
series = {SAC ’20}
}
- train_nn.py
usage: train_nn.py [-h] [--news-dir DIR_PATH] [--ground-truth-dir DIR_PATH]
[--val-news-dir DIR_PATH] [--val-ground-truth-dir DIR_PATH]
[--test-news-dir DIR_PATH]
[--test-ground-truth-dir DIR_PATH] [--tensor-dataset PATH]
[--granularity {token,sentence,document,tokens_grouped_by_sentence}]
[--max-seq-len N] [--max-sent-len N]
[--embeddings-matrix-path EMBEDDINGS_MATRIX_PATH]
[--word2index-path WORD2INDEX_PATH]
[--embeddings-path EMBEDDINGS_PATH]
[--dataloader-workers N] [--epochs N] [--batch-size N]
[--learning-rate LR] [--seed N] [--resume PATH]
[--hidden-dim N] [--test-percentage TEST_PERCENTAGE]
[--reduce-lr-patience REDUCE_LR_PATIENCE] [--freeze FREEZE]
[--k-fold K] [--checkpoint-dir CHECKPOINT_DIR]
[--print-freq N] [--validate-every N]
[--checkpoint-every N]
name
positional arguments:
name Model's name
optional arguments:
-h, --help show this help message and exit
--news-dir DIR_PATH, --train-news-dir DIR_PATH
Hyperpartisan news XML directory (TRAIN)
--ground-truth-dir DIR_PATH, --train-ground-truth-dir DIR_PATH
Ground truth XML directory (TRAIN)
--val-news-dir DIR_PATH
Hyperpartisan news XML directory (VALIDATION)
--val-ground-truth-dir DIR_PATH
Ground truth XML directory (VALIDATION)
--test-news-dir DIR_PATH
Hyperpartisan news XML directory (TEST)
--test-ground-truth-dir DIR_PATH
Ground truth XML directory (TEST)
--tensor-dataset PATH
Path to a previously serialized dataset
--granularity {token,sentence,document,tokens_grouped_by_sentence}
Granularity of embeddings in dataset
--max-seq-len N Maximum tokens to use for training (cutoff)
--max-sent-len N Maximum number of tokens in each sentence (cutoff)
--embeddings-matrix-path EMBEDDINGS_MATRIX_PATH
Path to pre-generated embeddings matrix (needs
word2index)
--word2index-path WORD2INDEX_PATH
Path to word-to-index mapping (corresponding to the
given embeddings matrix)
--embeddings-path EMBEDDINGS_PATH
Path to pre-trained embeddings
--dataloader-workers N
Number of workers to use for pytorch's Dataloader (0
means using main thread)
--epochs N Number of epochs to train for
--batch-size N Number of samples per batch (for training)
--learning-rate LR Number of epochs to train for
--seed N Random seed for initializing training
--resume PATH Resume training from the given checkpoint (default:
None)
--hidden-dim N Value for miscellaneous hidden dimensions
--test-percentage TEST_PERCENTAGE
Percentage of samples to use for testing
--reduce-lr-patience REDUCE_LR_PATIENCE
Number of non-improving epochs before reducing LR
--freeze FREEZE Indices of layers/sub-modules to freeze
--k-fold K Number of fold to use if using k-fold cross-validation
--checkpoint-dir CHECKPOINT_DIR
Directory for saving model checkpoints
--print-freq N Frequency of epochs for printing train statistics
--validate-every N Frequency of evaluation on validation dataset (in
epochs)
--checkpoint-every N Checkpoint frequency (in epochs)
- serialize_pytorch_dataset.py
usage: serialize_pytorch_dataset.py [-h] --news-dir DIR_PATH
[--ground-truth-dir DIR_PATH] --save-path
PATH [--batch-size N]
[--granularity {token,sentence,document,tokens_grouped_by_sentence}]
[--max-seq-len N] [--max-sent-len N]
[--embeddings-matrix-path EMBEDDINGS_MATRIX_PATH]
[--word2index-path WORD2INDEX_PATH]
[--embeddings-path EMBEDDINGS_PATH]
[--dataloader-workers N]
optional arguments:
-h, --help show this help message and exit
--news-dir DIR_PATH Hyperpartisan news XML directory
--ground-truth-dir DIR_PATH
Ground truth XML directory
--save-path PATH File path to save dataset on
--batch-size N Number of samples per batch (for training)
--granularity {token,sentence,document,tokens_grouped_by_sentence}
Granularity of embeddings in dataset
--max-seq-len N Maximum tokens to use for training (cutoff)
--max-sent-len N Maximum number of tokens in each sentence (cutoff)
--embeddings-matrix-path EMBEDDINGS_MATRIX_PATH
Path to pre-generated embeddings matrix (needs
word2index)
--word2index-path WORD2INDEX_PATH
Path to word-to-index mapping (corresponding to the
given embeddings matrix)
--embeddings-path EMBEDDINGS_PATH
Path to pre-trained embeddings
--dataloader-workers N
Number of workers to use for pytorch's Dataloader (0
means using main thread)
- generate_token_embeddings.py
usage: generate_token_embeddings.py [-h] --articles-dir ARTICLES_DIR
--embeddings-path EMBEDDINGS_PATH
[--save-dir SAVE_DIR]
[--base-name BASE_NAME]
generate_token_embeddings.py: error: the following arguments are required: --articles-dir, --embeddings-path
- evaluate_nn.py
usage: evaluate_nn.py [-h] --input-dir DIR_PATH [--output-dir DIR_PATH]
[--granularity {token,sentence,document,tokens_grouped_by_sentence}]
[--max-seq-len N] [--max-sent-len N]
[--embeddings-matrix-path EMBEDDINGS_MATRIX_PATH]
[--word2index-path WORD2INDEX_PATH]
[--embeddings-path EMBEDDINGS_PATH]
[--dataloader-workers N] [--model-path PATH]
[--batch-size N]
evaluate_nn.py: error: the following arguments are required: --input-dir
- train_kfold.py
- generate_dataset.py
- grid_search.py
- evaluate.py
- train.py