fake_news_semantics
│   README.md
│   *.py
│   
└───data
    │   balancedtest.csv
    │   fulltrain.csv
    |   test.xsls

pytorch 1.0.0
pandas
tqdm
xlrd (pip install xlrd)
bert-pytorch (pip install pytorch-pretrained-bert)

python main.py --batch_size 1024 --config lstm --encoder 0 --mode 0

python main.py --batch_size 1024 --config cnn --encoder 1 --mode 0

python bert_classifier.py --batch_size 4 --max_epochs 10 --max_seq_length 500 --max_sent_length 70 --mode 0

python main.py --batch_size 32 --max_epochs 10 --config gcn --max_sent_len 50 --encoder 2 --mode 0

python main.py --batch_size 32 --max_epochs 10 --config gcn_attn --max_sent_len 50 --encoder 3 --mode 0

python main.py --batch_size 32 --max_epochs 10 --config gat --max_sent_len 50 --encoder 4 --mode 0

python main.py --batch_size 1024 --encoder 0 --model_file model_lstm.t7 --mode 1

python main.py --batch_size 1024 --encoder 1 --model_file model_cnn.t7 --mode 1

python bert_classifier.py --batch_size 4 --model_file model_bert.t7 --max_seq_length 500 --max_sent_length 70 --mode 1

python main.py --batch_size 32 --max_sent_len 50 --encoder 2 --model_file model_gcn.t7 --mode 1

python main.py --batch_size 32 --max_sent_len 50 --encoder 3 --model_file model_gcn_attn.t7 --mode 1

python main.py --batch_size 32 --max_sent_len 50 --encoder 4 --model_file model_gat_attn.t7 --mode 1

@inproceedings{vaibhav-etal-2019-sentence,
    title = "Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification",
    author = "Vaibhav, Vaibhav  and
      Mandyam, Raghuram  and
      Hovy, Eduard",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5316",
    doi = "10.18653/v1/D19-5316",
    pages = "134--139",
    abstract = "The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a document. We observe an interesting pattern in the way sentences interact with each other across different kind of news articles. To capture this kind of information for long news articles, we propose a graph neural network-based model which does away with the need of feature engineering for fine grained fake news classification. Through experiments, we show that our proposed method beats strong neural baselines and achieves state-of-the-art accuracy on existing datasets. Moreover, we establish the generalizability of our model by evaluating its performance in out-of-domain scenarios. Code is available at https://github.com/MysteryVaibhav/fake{\textbackslash}{\_}news{\textbackslash}{\_}semantics.",
}