Awesome

Paper of the source codes released:

Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang. Rumor Detectionon Social Media with Bi-Directional Graph Convolutional Networks. AAAI 2020.

Datasets:

The datasets used in the experiments were based on the three publicly available Weibo and Twitter datasets released by Ma et al. (2016) and Ma et al. (2017):

Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J Jansen, Kam-Fai Wong, and Meeyoung Cha. Detecting rumors from microblogs with recurrent neural networks. In Proceedings of IJCAI 2016.

Jing Ma, Wei Gao, Kam-Fai Wong. Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. ACL 2017.

In the 'data' folder we provide the pre-processed data files used for our experiments. The raw datasets can be respectively downloaded from https://www.dropbox.com/s/46r50ctrfa0ur1o/rumdect.zip?dl=0. and https://www.dropbox.com/s/7ewzdrbelpmrnxu/rumdetect2017.zip?dl=0.

The Weibo datafile 'weibotree.txt' is in a tab-sepreted column format, where each row corresponds to a weibo. Consecutive columns correspond to the following pieces of information:
1: root-id -- an unique identifier describing the tree (weiboid of the root);
2: index-of-parent-weibo -- an index number of the parent weibo for the current weibo;
3: index-of-the-current-weibo -- an index number of the current weibo;
4: list-of-index-and-counts -- the rest of the line contains space separated index-count pairs, where a index-count pair is in format of "index:count", E.g., "index1:count1 index2:count2" (extracted from the "text" field in the json format from Weibo raw datasets)

For a detailed description of Twitter datafile 'data.TD_RvNN.vol_5000.txt' can be seen at RvNN.

Dependencies:

python==3.5.2
numpy==1.18.1
torch==1.4.0
torch_scatter==1.4.0
torch_sparse==0.4.3
torch_cluster==1.4.5
torch_geometric==1.3.2
tqdm==4.40.0
joblib==0.14.1

Make sure that cuda/bin, cuda/include and cuda/lib64 are in your $PATH, $CPATH and $LD_LIBRARY_PATH respectively before the installation, e.g.:

$ echo $PATH
>>> /usr/local/cuda/bin:...

$ echo $CPATH
>>> /usr/local/cuda/include:...

and

$ echo $LD_LIBRARY_PATH
>>> /usr/local/cuda/lib64

on Linux or

$ echo $DYLD_LIBRARY_PATH
>>> /usr/local/cuda/lib

on macOS.

Reproduce the experimental results:

Run script

$ sh main.sh

and choose "model/Weibo/BiGCN_Weibo.py" for BiGCN model on Weibo dataset or "model/Twitter/BiGCN_Twitter.py" on Twitter15/Twitter16 dataset.

In "main.sh", two arguments need to be specified, representing the datasetname and iteration times respectively. E.g.,

python ./model/Twitter/BiGCN_Twitter.py Twitter15 100

will reproduce the average experimental results of 100 iterations of BiGCN model on Twitter15 dataset with 5-fold cross-validation.

If you find this code useful, please let us know and cite our paper.
If you have any question, please contact Tian at: bt18 at mails dot tsinghua dot edu dot cn.