This is the official repository of the paper:
Mining Dual Emotion for Fake News Detection. [PDF] [Code] [Slides] [Video] [中文讲解视频]
Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu. Proceedings of 30th The Web Conference (WWW 2021)
An overall framework of using Dual Emotion Features for fake news detection. Dual Emotion Features consist of three components:
a) Publisher Emotion extracted from the content;
b) Social Emotion extracted from the comments;
c) Emotion Gap representing the similarity and difference between publisher emotion and social emotion.
Dual Emotion Features are concatenated with the features from d) Fake News Detector (here, BiGRU as an example) for the final prediction of veracity.
The datasets are available at https://drive.google.com/drive/folders/1pjK0BYiiJt0Ya2nRIrOLCVo-o53sYRBV?usp=sharing.
The raw dataset is released by SemEval-2019 Task 7:
Genevieve Gorrell, Ahmet Aker, Kalina Bontcheva, Elena Kochkina, Maria Liakata, Arkaitz Zubiaga, Leon Derczynski (2019). SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours. Proceedings of the 13th International Workshop on Semantic Evaluation, ACL.
After preprocessing, our experimental dataset is in the folder dataset/RumourEval-19.
The original dataset is firstly proposed in:
Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting rumors from microblogs with recurrent neural networks. In IJCAI 2016. 3818–3824.
In Section 4.1.2 and Appendix A of our paper, we described that there are many fake news duplications in the original dataset. The original version of Weibo-16 is in the folder dataset/Weibo-16-original, and our experimental dataset (a deduplicated version) of Weibo-16 is in the folder dataset/Weibo-16.
Weibo-20 is our newly proposed dataset, and it is in the folder dataset/Weibo-20. Besides, in Section 4.4.3 of the paper, we conducted the experiments under the real-world scenario simulation. This temporal version of Weibo-20 is in the folder dataset/Weibo-20-temporal.
| Type | Language | Resources |
|---|---|---|
| Emotion Category | English | https://github.com/NVIDIA/sentiment-discovery |
| Chinese | https://ai.baidu.com/tech/nlp/emotion_detection | |
| Emotion Lexicon | English | resources/English/NRC |
| Chinese | /resources/Chinese/大连理工大学情感词汇本体库 |
|
| Emotional Intensity | English | resources/English/NRC |
| Chinese | /resources/Chinese/大连理工大学情感词汇本体库 |
|
| Sentiment Score | English | nltk.sentiment.vader.SentimentIntensityAnalyzer |
| Chinese | resources/Chinese/BosonNLP |
|
| Other Auxilary Features | English | Wiki: List of emoticons, resources/English/HowNet, resources/English/others |
| Chinese | resources/Chinese/HowNet, resources/English/others |
Python==3.6.10
Keras==2.1.2
Tensorflow==1.13.1
Tensorflow-GPU==1.14.0
cd code/preprocess
Get the labels:
python output_of_labels.py
Get the emotion features:
python input_of_emotions.py
Get the semantic features:
python input_of_semantics.py
Now, the preprocessed data are stored in preprocess/data.
Config the experimental dataset, the model and other hyperparameters in code/train/config.py.
cd code/train
python master.py
Now, the results are stored in train/results.
@inproceedings{10.1145/3442381.3450004,
author = {Zhang, Xueyao and Cao, Juan and Li, Xirong and Sheng, Qiang and Zhong, Lei and Shu, Kai},
title = {Mining Dual Emotion for Fake News Detection},
year = {2021},
url = {https://doi.org/10.1145/3442381.3450004},
doi = {10.1145/3442381.3450004},
booktitle = {Proceedings of the Web Conference 2021},
pages = {3465–3476},
series = {WWW '21}
}