Rumours can spread quickly through social media, and malicious ones can bring about significant economical and social impact. Motivated by this, our paper focuses on the task of rumour detection; particularly, we are interested in understanding how early we can detect them. To address this, we present a novel methodology for early rumour detection.Here is the code based on our approach.
Python 3.6
TensorFlow 1.13
Two DataSets can be used to evaluate our model.
Weibo DataSet: http://alt.qcri.org/~wgao/data/rumdect.zip
Twitter DataSet: https://figshare.com/articles/PHEME_dataset_of_rumours_and_non-rumours/4010619
Download Twitter DataSet and extract, set the DataSet path to the data_file_path in config.py.
Download glove word vectors: http://nlp.stanford.edu/data/glove.840B.300d.zip, and set the w2v_file_path in config.py.
Run python main.py to train and evaluate the model.
If there are problems with the codes, you can try the newly uploaded torch codes by Menglong Lu.