This is a repository containing code for the paper:
J. Kim, B. Tabibian, A. Oh, B. Schölkopf, M. Gomez-Rodriguez. Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), 2018.
This code is developed under Python 3 and the following packages are required for executing the code:
The repository contains the code for the execution of the model (
Curb) and several baseline methods. Also, it contains Jupyter notebook files for generating the figures in the paper and the user exposure data for the
codedirectory contains the code for executing the model and the baselines.
generate_results.py: Given the user exposure data in the
Curband the baseline methods) and saves the results in
curb.py: API for
flagratio.py: API for the
baseline.py: API for the
Jupyternotebook files for generating the figures in the paper. These notebooks use the results generated by the scripts in the
reshare_datacontains user reshare logs for each story. For each txt file, each line consists of user id and timestamp of the reshare event, separated by tab.
resultscontains pre-computed results for
Flag Ratiobaseline and the
We use data from
S. Kwon, M. Cha, and K Jung. 2017. Rumor detection over varying time windows. PLOS ONE 12, 1 (2017), e0168344.
and it can be downloaded from the following link:
For further inquiries, please contact Jooyeon Kim (firstname.lastname@example.org)