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.
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.
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.
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.
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