Early Rumour Detection

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.

Requirement

Python 3.6

TensorFlow 1.13

DataSet

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

Usage

1. Download Twitter DataSet and extract, set the DataSet path to the data_file_path in config.py.

2. Download glove word vectors: http://nlp.stanford.edu/data/glove.840B.300d.zip, and set the w2v_file_path in config.py.

3. Run python main.py to train and evaluate the model.

Early Rumour Detection (Torch)

If there are problems with the codes, you can try the newly uploaded torch codes by Menglong Lu.