LIAR2

The [LIAR](https://doi.org/10.18653/v1/P17-2067) dataset has been widely followed by fake news detection researchers since its release, and along with a great deal of research, the community has provided a variety of feedback on the dataset to improve it. We adopted these feedbacks and released the LIAR2 dataset, a new benchmark dataset of ~23k manually labeled by professional fact-checkers for fake news detection tasks. We have used a split ratio of 8:1:1 to distinguish between the training set, the test set, and the validation set, details of which are provided in the paper of "[An Enhanced Fake News Detection System With Fuzzy Deep Learning](https://doi.org/10.1109/ACCESS.2024.3418340)". The LIAR2 dataset can be accessed at [Huggingface](https://huggingface.co/datasets/chengxuphd/liar2) and [Github](https://github.com/chengxuphd/LIAR2), and statistical information for LIAR and LIAR2 is provided in the table below: 


| **Statistics**                               | **LIAR** | **LIAR2** |
|----------------------------------------------|----------|------------|
| Training set size                            | 10,269   | 18,369     |
| Validation set size                          | 1,284    | 2,297      |
| Testing set size                             | 1,283    | 2,296      |
| Avg. statement length (tokens)               | 17.9     | 17.7       |
| Avg. speaker description length (tokens)     | \        | 39.4       |
| Avg. justification length (tokens)           | \        | 94.4       |
| **Labels**
| Pants on fire                                | 1,050    | 3,031      |
| False                                        | 2,511    | 6,605      |
| Barely-true                                  | 2,108    | 3,603      |
| Half-true                                    | 2,638    | 3,709      |
| Mostly-true                                  | 2,466    | 3,429      |
| True                                         | 2,063    | 2,585      |


## Ablation Experiment

The LIAR2 dataset is an upgrade of the LIAR dataset, which inherits the ideas of the LIAR dataset, refines the details and architecture, and expands the size of the dataset to make it more responsive to the needs of fake news detection tasks. We believe that with the help of the LIAR2 dataset, it will be able to perform better fake news detection tasks. The analysis and baseline information about the LIAR2 dataset is provided in below.

| **Feature**              | **Val. Accuracy** | **Val. F1-Macro** | **Val. F1-Micro** | **Test Accuracy** | **Test F1-Macro** | **Test F1-Micro** | **Mean** |
|--------------------------|-------------------|-------------------|-------------------|-------------------|-------------------|-------------------|----------|
| Statement              | 0.3174       | 0.1957       | 0.3117       | 0.3197       | 0.2380       | 0.3197       | 0.2837 |
| Date                   | 0.2912       | 0.1879       | 0.2912       | 0.3079       | 0.1775       | 0.3079       | 0.2606 |
| Subject                | 0.3243       | 0.2311       | 0.3183       | 0.3267       | 0.2271       | 0.3267       | 0.2924 |
| Speaker                | 0.3283       | 0.2250       | 0.3174       | 0.3310       | 0.2462       | 0.3310       | 0.2965 |
| Speaker Description    | 0.3322       | 0.2444       | 0.3250       | 0.3280       | 0.2444       | 0.3280       | 0.3003 |
| State Info             | 0.2930       | 0.1577       | 0.2950       | 0.2979       | 0.1521       | 0.2979       | 0.2489 |
| Credibility History    | 0.5007       | 0.4696       | 0.4985       | 0.5057       | 0.4656       | 0.5057       | 0.4910 |
| Context                | 0.2982       | 0.1817       | 0.2982       | 0.3132       | 0.1791       | 0.3132       | 0.2639 |
| Justification          | **0.5964**   | **0.5657**   | **0.5827**   | **0.6115**   | **0.5968**   | **0.6115**   | **0.5941** |
| All without 
|Statement               | **0.7079**   | **0.6734**   | **0.6822**   | **0.7182**   | **0.7108**   | **0.7182**   | **0.7018** |
| Date                   | 0.6931       | 0.6572       | 0.6680       | 0.7078       | 0.6993       | 0.7078       | 0.6889 |
| Subject                | 0.7000       | 0.6579       | 0.6681       | 0.7078       | 0.7013       | 0.7078       | 0.6905 |
| Speaker                | 0.6944       | 0.6648       | 0.6757       | 0.7043       | 0.6942       | 0.7043       | 0.6896 |
| Speaker Description    | 0.6892       | 0.6640       | 0.6739       | 0.7169       | 0.7073       | 0.7169       | 0.6947 |
| State Info             | 0.7074       | 0.6625       | 0.6729       | 0.7099       | 0.7016       | 0.7099       | 0.6940 |
| Credibility History    | 0.6025       | 0.5717       | 0.5900       | 0.6185       | 0.6046       | 0.6185       | 0.6010 |
| Context                | 0.7005       | 0.6622       | 0.6720       | 0.7043       | 0.6967       | 0.7043       | 0.6900 |
| Justification          | 0.5285       | 0.4898       | 0.5153       | 0.5340       | 0.5148       | 0.5340       | 0.5194 |
| Statement +
| Date                   | 0.3431       | 0.2540       | 0.3343       | 0.3380       | 0.2514       | 0.3380       | 0.3098 |
| Subject                | 0.3548       | 0.2759       | 0.3513       | 0.3375       | 0.2580       | 0.3375       | 0.3192 |
| Speaker                | 0.3618       | 0.2862       | 0.3539       | 0.3476       | 0.2640       | 0.3476       | 0.3269 |
| Speaker Description    | 0.3583       | 0.2814       | 0.3531       | 0.3667       | 0.2886       | 0.3667       | 0.3358 |
| State Info             | 0.3317       | 0.2367       | 0.3294       | 0.3328       | 0.2362       | 0.3328       | 0.2999 |
| Credibility History    | 0.5067       | 0.4737       | 0.5084       | 0.5244       | 0.5000       | 0.5244       | 0.5063 |
| Context                | 0.3361       | 0.2682       | 0.3391       | 0.3458       | 0.2560       | 0.3458       | 0.3152 |
| Justification          | 0.6017       | 0.5578       | 0.5796       | 0.6176       | 0.6026       | 0.6176       | 0.5962 |
| All                    | **0.6974**   | **0.6570**   | **0.6676**   | **0.7021**   | **0.6961**   | **0.7021**   | **0.6871**|

Introducing paper: An Enhanced Fake News Detection System With Fuzzy Deep Learning