nguyenvo09 / combatingfakenews

This is the repository of code and dataset for paper "The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News", SIGIR 2018

Recommending Fact-checking Articles to Combat Fake News

This is the repository for the paper "The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News" SIGIR 2018, https://arxiv.org/abs/1806.07516

Datasets

• Link to download our full dataset for the paper: You can analyze characteristics of guardians based on this dataset. https://drive.google.com/file/d/1n2nZT2440BYy7PDwdSwLMpWHKrKRObF1/view

• Link to download splitted data Splitted_data.rar https://drive.google.com/open?id=1riEsUNP3GHfn7XefuMkH50kW4W2dL0qW

• The splitted data has training, dev and testing interactions. In each part, there are 12,197 guardians with at least one interaction for each guardian

Analysis

• Temporal behavior of guardians
• Topical interests of guardians

How to run this code?

• Download the splitted data and extract it. The expected path is /pytorch/Splitted_data/sigir18/*
• Then, run the following command with default settings:

python Masters/master_gau.py

You could achive following performance:

|Epoch 11 | Train time: 8 (s) | Train loss: 79212.76166 | Eval time: 30.316 (s) | Vad mapks@10 = 0.06830 | Vad ndcg@10 = 0.08897 | Vad recall@10 = 0.15610 | Test mapks@10 = 0.06879 | Test ndcg@10 = 0.08991 | Test recall@10 = 0.15783
|Epoch 12 | Train time: 8 (s) | Train loss: 75769.19746 | Eval time: 30.028 (s) | Vad mapks@10 = 0.06833 | Vad ndcg@10 = 0.08906 | Vad recall@10 = 0.15635 | Test mapks@10 = 0.06918 | Test ndcg@10 = 0.09030 | Test recall@10 = 0.15832
|Epoch 13 | Train time: 8 (s) | Train loss: 72671.60144 | Eval time: 30.399 (s) | Vad mapks@10 = 0.06876 | Vad ndcg@10 = 0.08946 | Vad recall@10 = 0.15668 | Test mapks@10 = 0.06948 | Test ndcg@10 = 0.09066 | Test recall@10 = 0.15889
|Epoch 14 | Train time: 8 (s) | Train loss: 69873.45222 | Eval time: 29.985 (s) | Vad mapks@10 = 0.06858 | Vad ndcg@10 = 0.08913 | Vad recall@10 = 0.15578 | Test mapks@10 = 0.06952 | Test ndcg@10 = 0.09063 | Test recall@10 = 0.15865

Requirements:

We use PyTorch 0.4.1, Python 3.5. The SPPMI matrices, network and sim matrices are memory-intensive so please run it on a computer with at least 16GB.

Please cite our paper if you find the data and code helpful, thanks:

@inproceedings{vo2018guardians,
	title={The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News},
	author={Vo, Nguyen and Lee, Kyumin},
	booktitle={The 41st International ACM SIGIR Conference 
		  on Research and Development in Information Retrieval},
	year={2018}
}