# An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph
## 5 Experiment and Analysis  
### Experimental Result Comparison Analysis  
The experimental environment is based on the Windows operating system; the development language is Python and Cypher; the knowledge extraction model framework uses the UIE framework; the entity extraction model framework uses the LAC framework. The experimental data mainly comes from relevant news crawled from the Internet using crawler scripts, as well as various fake news rumor websites, and also uses fake news generated based on Ernie. Figure 12: The process of matching two knowledge graphs. The LAC model consists of two Bi-GRU layers consisting of two forward GRUs and two reverse GRUs, and the hidden layer has a dimension of 256. The weight matrix in each layer of the model is first initialized randomly. For the Bi-GRU layer, sigmoid is used as the gating activation function, and \(tan\)\(h\) is used as the activation function of the hidden layer. During the model training, parameter optimization is performed using stochastic gradient descent. The learning rate of the base is 1e-3. The learning rate of the embedding layer is 5e-3. The size of the batch size is set to 250. ### Experimental Result Comparison Analysis  
The fake news detection task dataset used in this paper mainly has two parts: obtained by crawling various fake news websites and news websites using crawlers and obtained by using a fake news generation method based on Ernie, finally obtaining a fake news dataset with 40,000 entries. At present, most methods for fake news detection are based on deep learning; although deep learning's representation learning ability can obtain objects' underlying spatial features, these features are obtained through a black box and are a continuous vector; humans cannot understand them at all. At the same time, most deep learning methods only rely on contextual relationships in news text for identification, without fully utilizing commonsense knowledge contained in the news text. In response to these problems, a comparative experiment was designed based on the interpretability of the method, and the three graph matching methods of RRWM, SM, and SMAC were compared.. The algorithm based on random wandering graph matching used in this paper is also compared with several benchmark methods, namely SVM-TS, CNN, EANN, and GRU. The evaluation metrics used in this experiment are accuracy, precision, and recall. The example used in the experiments is described below: \"On Nov. 10, local time, the U.S. Department of Defense announced another $400 million worth of military assistance to Ukraine to meet its critical security and defense needs. According to the statement, the assistance program includes weapons such as the Hawk air defense missile. The Hawk air defense missiles, which cost up to $250,000 each to develop, have an effective range of 41 km and can be used for medium-range, low- and medium-altitude defense. Since President [PERSON] took office, the U.S. has pledged more than $19.3 billion in military assistance to Ukraine.\" According to the knowledge extraction method introduced in the previous section, the background knowledge triads extracted from them are: ['United States', 'President', '[PERSON]'], ['[PERSON]', 'Nationality', 'United States'], [\"Hawk\" anti-aircraft missile', 'Effective range', '41 km'], [\"Hawk\" anti-aircraft missile', 'Development cost', '$250,000']. According to the analysis based on the matching results, the news mentions that the development cost of the Hawk anti-aircraft missile is USD300,000, which conflicts with the knowledge in the commonsense knowledge graph, so the news is considered to be fake news, and the error lies in the fact that the development cost of the Hawk anti-aircraft missile does not match the actual one. Thus, the interpretable recognition of false news texts is completed. From the experimental results in Table 1, it can be seen that the effect of the RRWM algorithm is better than the SM algorithm and the SMAC algorithm in terms of the evaluation indexes such as accuracy, correctness, and recall.