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https://github.com/rmsnow/www2021

Official repository to release the code and datasets in the paper "Mining Dual Emotion for Fake News Detection", WWW 2021.
https://github.com/rmsnow/www2021

emotion fake-news-detection keras www2021

Last synced: 25 days ago
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Official repository to release the code and datasets in the paper "Mining Dual Emotion for Fake News Detection", WWW 2021.

Host: GitHub
URL: https://github.com/rmsnow/www2021
Owner: RMSnow
Created: 2021-02-08T09:35:18.000Z (almost 4 years ago)
Default Branch: master
Last Pushed: 2021-12-31T03:57:11.000Z (almost 3 years ago)
Last Synced: 2023-03-06T21:59:38.755Z (almost 2 years ago)
Topics: emotion, fake-news-detection, keras, www2021
Language: Jupyter Notebook
Homepage: https://doi.org/10.1145/3442381.3450004
Size: 4.99 MB
Stars: 45
Watchers: 3
Forks: 15
Open Issues: 0
Metadata Files:
- Readme: readme.md

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README

# WWW 2021

This is the official repository of the paper:

> **Mining Dual Emotion for Fake News Detection.** [[PDF]](https://www.zhangxueyao.com/assets/www2021-dual-emotion-paper.pdf) [[Code]](https://github.com/RMSnow/WWW2021) [[Slides]](https://www.zhangxueyao.com/assets/www2021-dual-emotion-slides.pdf) [[Video]](https://www.zhangxueyao.com/assets/www2021-dual-emotion-video.mp4) [[中文讲解视频]](https://www.bilibili.com/video/BV13o4y1m7c3)
>
> Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu. Proceedings of 30th The Web Conference (**WWW 2021**)

## An Overall Framework

![1](https://github.com/RMSnow/WWW2021/blob/master/framework.png)

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.

## Datasets

The datasets are available at https://drive.google.com/drive/folders/1pjK0BYiiJt0Ya2nRIrOLCVo-o53sYRBV?usp=sharing. The downloaded datasets (i.e., the `dataset` folder) need to be moved into the root path of this project.

### RumourEval-19

The raw dataset is released by [SemEval-2019 Task 7](https://competitions.codalab.org/competitions/19938#learn_the_details-overview):

> 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.

Our experimental dataset is in the folder `dataset/RumourEval-19`, which contains three json files. In every json file,

- the `id` identifies the unique id of the post.
- the `label` identifies the veracity of the post, whose value ranges in [ `fake`, `real`, `unverified`].
- the `content` is the content of the post.
- the `comments` are the users' comments list towards the post.
- the `content_emotions_labels` and `cotent_emotions_probs` are the *Emotion Category* features of the content. And the `comments100_emotions_labels_mean_pooling`, `comments100_emotions_labels_max_pooling`, `comments100_emotions_probs_mean_pooling`, and `comments100_emotions_probs_max_pooling` are the *Emotion Category* features of the earliest 100 comments. The way how to use these features will be described in [here](https://github.com/RMSnow/WWW2021#step12-get-the-emotion-features).

### Weibo-16

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`. In every json file in these folders,

- the `label` identifies the veracity of the post, whose value ranges in [ `fake`, `real`].
- the `content` is the content of the post.
- the `comments` are the users' comments list towards the post.
- the `content_emotions` are the *Emotion Category* features of the content. And the `comments100_emotions_mean_pooling` and `comments100_emotions_max_pooling` are the *Emotion Category* features of the earliest 100 comments. The way how to use these features will be described in [here](https://github.com/RMSnow/WWW2021#step12-get-the-emotion-features).

### Weibo-20

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`. In every json file in these folders,

## Emotion Resources

| Type | Language | Resources |
| ----------------------- | -------- | ------------------------------------------------------------ |
| Emotion Category | English | https://github.com/NVIDIA/sentiment-discovery |
| | Chinese | https://ai.baidu.com/tech/nlp_apply/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](https://www.nltk.org/api/nltk.sentiment.html#nltk.sentiment.vader.SentimentIntensityAnalyzer) |
| | Chinese | `resources/Chinese/BosonNLP` |
| Other Auxilary Features | English | [Wiki: List of emoticons](https://en.wikipedia.org/wiki/List_of_emoticons), `resources/English/HowNet`, `resources/English/others` |
| | Chinese | `resources/Chinese/HowNet`, `resources/English/others` |

## Code

### Requirements

```
Python==3.6.10
Keras==2.1.2
Tensorflow==1.13.1
Tensorflow-GPU==1.14.0
```

### Usage

#### Step1: Preprocess

##### Step1.1: Get the `labels`

```
cd code/preprocess
python output_of_labels.py
```

##### Step1.2: Get the `emotion features`

```
cd code/preprocess
python input_of_emotions.py
```

Note that the *Emotion Category* features are depended on the external resources ([NVIDIA-sentiment-discovery](https://github.com/NVIDIA/sentiment-discovery) for English, and [Baidu AI](https://ai.baidu.com/tech/nlp_apply/emotion_detection) for Chinese). And they have been saved in the dataset files (e.g.: `content_emotions`, `comments100_emotions_mean_pooling`, `content_emotions_probs`, `comments100_emotions_labels_max_pooling`, etc.).

If you want to extract emotion features for your custom datasets, you need to access these external resources and prepare *Emotion Category* features. Of course, you can also leave *Emotion Category* unused and extract other features by `input_of_emotion.py`.

##### Step1.3: Get the `semantic features`

In this repo, we consider the semantic features as word embeddings. You need to download the preprained word embeddings ([see here](https://github.com/RMSnow/WWW2021/blob/master/word-embedding/readme.md) for more details) before running the following code:

```
cd code/preprocess
python input_of_semantics.py
```

Now, the preprocessed data are stored in `preprocess/data`.

#### Step 2: Configuration

Config the experimental dataset, the model and other hyperparameters in `code/train/config.py`.

#### Step3: Training and Testing

```
cd code/train
python master.py
```

Now, the results are stored in `train/results`.

# Citation

```
@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}
}
```