False news detection method and system based on time interval and knowledge fusion

Abstract

The invention provides a false news detection method and system based on time interval and knowledge fusion, and belongs to the technical field of internet security and deep learning. Carrying out image feature extraction on the target news image based on a convolutional neural network and converting the target news image into an image feature vector; semantic representation is conducted on the target news text based on a text encoder, and the target news text is converted into text feature vectors; extracting entities from the target news text, performing entity linking to obtain external knowledge supplementation, and converting the external knowledge supplementation into external knowledge feature vectors; calculating a time interval by extracting occurrence time and news release time of a target news event, and converting the time interval into a time interval feature vector; and fusing the image feature vector, the text feature vector, the external knowledge feature vector and the time interval feature vector, and inputting the fused features into a classifier to judge the authenticity of the news data. According to the method, the accuracy and timeliness of identifying false news can be remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the field of Internet security and deep learning technology, and in particular to a false news detection method and system based on time interval and knowledge fusion. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of the Internet, social media has completely changed the way people get information, and along with it, various false information has been promoted. These false information often contain forged multimedia content, especially false news. The spread of false news has had a serious impact on society, and some lawless elements even use this information to guide public opinion and disrupt social peace. Therefore, it is imperative to detect and regulate false news, ensure that users get real information, and thus maintain social harmony.

[0004] However, the identification and detection of fake news on social media is still in an imperfect stage. It is difficult to completely deal with fake news that has been published if only manual identification and detection are relied upon. Under such circumstances, technicians in related fields have begun to choose to build algorithm models based on deep learning to achieve automatic detection of fake news, which has greatly improved the detection efficiency compared to manual identification.

[0005] At present, technicians in related fields have been conducting more and more in-depth research on fake news detection through deep learning methods; however, the inventors found that the existing fake news detection methods still have some technical problems, such as:

[0006] (1) In order to improve the accuracy of fake news detection, existing fake news detection methods began to analyze news data based on multiple modalities. However, in the process of model design and feature extraction, the modal features were only focused on the direct fusion of news text and news images, while ignoring more complex semantic associations, resulting in the neglect of the rich knowledge information behind the news text; and this information is useful for fake news detection, and external knowledge can provide rich related information. Therefore, the accuracy of existing fake news detection methods is still not ideal.

[0007] (2) Existing fake news detection methods lack the analysis and judgment of the time interval information of news events during detection, and ignore the characteristics of the time dimension; without the time interval drive, it is difficult to accurately capture the evolution of information and the change of relationships. Therefore, the existing technology lacks timeliness in judging the authenticity of news. Summary of the invention

[0008] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a fake news detection method and system based on time interval and knowledge fusion, which can take into account the complex semantic associations and time dimension characteristics in news data, thereby improving the accuracy and timeliness of identifying fake news.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0010] A first aspect of the present invention provides a fake news detection method based on time interval and knowledge fusion.

[0011] The fake news detection method based on time interval and knowledge fusion includes:

[0012] Acquire target news data, wherein the target news data includes a target news image and a target news text;

[0013] Based on the VGG-19 convolutional neural network, image features of the target news image are extracted, and the obtained image features are converted into image feature vectors; based on the BERT text encoder, semantic representation of the target news text is performed, and the obtained semantic representation is converted into a text feature vector; entities are extracted from the target news text and entity links are performed to obtain external knowledge supplements, and the obtained external knowledge supplements are converted into external knowledge feature vectors; the time interval is calculated by extracting the occurrence time of the target news event and the news release time, and the obtained time interval is converted into a time interval feature vector;

[0014] The image feature vector, the text feature vector, the external knowledge feature vector and the time interval feature vector are subjected to feature fusion, and the fused features are input into a multi-layer perceptron classifier to judge the authenticity of the target news data.

[0015] Furthermore, after acquiring the target news data and before extracting the features, it is necessary to determine whether there is noise data in the target news text, and if so, denoising the target news text; wherein the noise data includes special symbols and expressions in the news text.

[0016] Furthermore, the last layer of the VGG-19 convolutional neural network is a fully connected layer, which is used to adjust the dimension of the output image features.

[0017] Furthermore, the target news text is semantically represented based on the BERT text encoder, including: vectorizing the target news text and processing it into a word sequence, and inputting the word sequence into the BERT text encoder; encoding the word sequence based on the BERT text encoder, and using the aggregated sequence of the BERT text encoder as the semantic representation of the target news text.

[0018] Furthermore, entity linking tools are used to obtain external knowledge supplements and eliminate ambiguity between text and knowledge based on entity associations.

[0019] Furthermore, the time interval is normalized and encoded to convert the obtained time interval into a time interval feature vector.

[0020] Furthermore, when the fused features are input into the multi-layer perceptron classifier to judge the authenticity of the target news data, the softmax logistic regression function is used to obtain the binary classification result of whether the target news is false news.

[0021] The second aspect of the present invention provides a fake news detection system based on time interval and knowledge fusion.

[0022] The fake news detection system based on time interval and knowledge fusion includes:

[0023] The data acquisition module is configured to: acquire target news data, wherein the target news data includes a target news image and a target news text;

[0024] The feature vector acquisition module is configured to: extract image features of the target news image based on the VGG-19 convolutional neural network, and convert the obtained image features into image feature vectors; perform semantic representation of the target news text based on the BERT text encoder, and convert the obtained semantic representation into text feature vectors; extract entities from the target news text and perform entity linking to obtain external knowledge supplements, and convert the obtained external knowledge supplements into external knowledge feature vectors; calculate the time interval by extracting the occurrence time of the target news event and the news release time, and convert the obtained time interval into a time interval feature vector;

[0025] The fake news detection module is configured to: perform feature fusion on the image feature vector, text feature vector, external knowledge feature vector and time interval feature vector, and input the fused features into a multi-layer perceptron classifier to judge the authenticity of the target news data.

[0026] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the false news detection method based on time interval and knowledge fusion as described in the first aspect of the present invention.

[0027] A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the false news detection method based on time interval and knowledge fusion as described in the first aspect of the present invention are implemented.

[0028] One or more of the above technical solutions have the following beneficial effects:

[0029] (1) The present invention combines four types of features in news data: text features, image features, time information features, and external knowledge; then extracts these four types of modal features through a deep learning model and uses feature splicing to achieve efficient fusion of multimodal information. Based on image features and text features, the present invention uses entity extraction and entity linking technology to associate the target news text with an external knowledge base to obtain supplementary semantic information. Therefore, the present invention can significantly enhance the model's ability to understand news semantics, thereby improving the accuracy of identifying false news.

[0030] (2) When acquiring features, the present invention extracts the occurrence time and release time of news events and uses time normalization processing to generate time interval features; based on the time interval drive, the evolution of information and changes in relationships can be captured more accurately. Therefore, the present invention is more timely in judging the authenticity of news.

[0031] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 This is a flow chart of a fake news detection method based on time interval and knowledge fusion in Embodiment 1 of the present invention.

[0034] Figure 2 Schematic diagram of the overall framework structure adopted by the false news detection method based on time interval and knowledge fusion in Example 1 of the present invention. DETAILED DESCRIPTION

[0035] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0036] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0037] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0038] The overall idea proposed by the present invention is as follows: The present invention provides a false news detection method based on the fusion of time interval and knowledge, aiming to solve the problems of insufficient feature extraction, low external knowledge utilization and unsatisfactory model performance in the existing false news detection. Specifically: First, relevant data is obtained from the target news, and the news image and text are processed separately to extract image features and text features, and convert them into vectorized representations. Subsequently, entities are extracted and linked from the news text, external knowledge information is supplemented, and an external knowledge feature vector is obtained through further processing; at the same time, the occurrence time and release time of the target news event are extracted, and the time interval features are calculated and vectorized. Finally, the text features, image features, external knowledge features and time interval features are spliced, and the target news is classified as false news. Based on the mining of multimodal features and external knowledge, the present invention can effectively improve the accuracy and timeliness of false news detection, and provides a comprehensive and efficient solution for false news detection.

[0039] In addition, to facilitate the understanding of the technical solution of the present invention, the following term explanations are provided:

[0040] 1) Multimodal fake news: news content that spreads false or inaccurate information using multiple different types of media elements (usually in the form of text and images).

[0041] 2) Multimodal fake news identification: Identify whether news is fake or not based on multimodal news data.

[0042] 3) Vectorized representation: The process of converting text or other unstructured data into numerical vectors that can be processed by computers. It can map high-dimensional, unstructured data into a low-dimensional or fixed-dimensional feature space to facilitate model learning and calculation.

[0043] 4) BERT (Bidirectional Encoder Representations from Transformers): A pre-trained natural language processing model based on the Transformer architecture that can effectively generate text feature vector representations for various NLP tasks.

[0044] 5) VGG-19: A deep convolutional neural network (CNN) that is widely used in various computer vision tasks such as image classification and feature extraction, with a depth of 19 layers.

[0045] 6) Normalization Layers: Use activation functions (such as Sigmoid and Tanh) to convert input information into a feature representation acceptable to the model, thereby facilitating subsequent model processing.

[0046] 7) Fully Connected Layer: A basic layer type in a neural network, where each neuron is connected to each neuron in the previous layer. The fully connected layer can map the input features to the output space and is a key component of the model's final classification or regression task.

[0047] 8) Concatenation: concatenate two or more vectors into one vector.

[0048] 9) Binary classification model: A machine learning model used to solve binary classification tasks. The goal is to divide samples into two categories based on the input features, such as predicting whether a news is fake news (true or false).

[0049] Embodiment 1

[0050] This embodiment discloses a fake news detection method based on time interval and knowledge fusion.

[0051] like Figure 1 As shown, the fake news detection method based on time interval and knowledge fusion includes:

[0052] Step S1, obtaining target news data, wherein the target news data includes a target news image and a target news text;

[0053] Step S2, extracting image features of the target news image based on the VGG-19 convolutional neural network, and converting the obtained image features into image feature vectors; semantically representing the target news text based on the BERT text encoder, and converting the obtained semantic representation into a text feature vector; extracting entities from the target news text and performing entity linking to obtain external knowledge supplements, and converting the obtained external knowledge supplements into external knowledge feature vectors; calculating the time interval by extracting the occurrence time of the target news event and the news release time, and converting the obtained time interval into a time interval feature vector;

[0054] Step S3, performing feature fusion on the image feature vector, text feature vector, external knowledge feature vector and time interval feature vector, and inputting the fused features into a multi-layer perceptron classifier to judge the authenticity of the target news data.

[0055] Based on the above process, the present invention can pay attention to the complex semantic associations and time dimension characteristics in news data, thereby improving the accuracy and timeliness of identifying false news. In order to facilitate understanding of the technical solution of the present invention, the specific implementation steps of the technical solution of the present invention are further explained and illustrated below.

[0056] Step S1, obtaining target news data; wherein the target news data includes a target news image and a target news text.

[0057] In this embodiment, a public multimodal dataset is used as a basic dataset. At the same time, the data is amplified by acquiring new news data from several large websites with rich data using web crawler technology. Finally, target news data is acquired from the amplified multimodal dataset.

[0058] Furthermore, after acquiring the target news data and before extracting the features, it is necessary to determine whether there is noise data in the target news text. If there is, the target news text is denoised to reduce the interference of irrelevant information. The noise data generally refers to special symbols and expressions in the news text.

[0059] Step S2: extract image features of the target news image based on the VGG-19 convolutional neural network, and convert the obtained image features into image feature vectors; perform semantic representation of the target news text based on the BERT text encoder, and convert the obtained semantic representation into text feature vectors; extract entities from the target news text and perform entity linking to obtain external knowledge supplements, and convert the obtained external knowledge supplements into external knowledge feature vectors; calculate the time interval by extracting the occurrence time of the target news event and the news release time, and convert the obtained time interval into a time interval feature vector. This can be achieved specifically by the following steps:

[0060] Step S2-1, extract image features of the target news image based on the VGG-19 convolutional neural network, and convert the obtained image features into image feature vectors.

[0061] VGG-19 is a classic deep convolutional neural network, which consists of 19 weight layers in total, including 16 convolutional layers and 3 fully connected layers. Specifically, the convolutional layer is used for feature extraction, that is, efficient feature extraction is achieved by stacking small convolution kernels; the pooling layer is used for downsampling to reduce the size of the feature map while retaining important features; the fully connected layer is used to map the extracted features to the classification space; the Softmax output layer can output probability distribution for classification.

[0062] Furthermore, if Figure 2 As shown in the figure, a pre-trained VGG-19 network is used to extract the image features of the target news, that is, the target news image is input into the convolutional neural network based on VGG-19 for feature extraction, the deep features of the image are extracted through the convolutional layer in the network, and the extracted image features are vectorized to generate an image feature vector. In order to ensure that the multimodal features of subsequent news can be aligned and spliced, a fully connected layer is added to the last layer of the convolutional neural network based on VGG-19 to adjust the dimension of the final output image features. Specifically, the calculation formula of the image feature vector is as follows:

[0063] R v =σ(W v ·F vgg );

[0064] Among them, R v represents the image feature vector, W v represents the weight matrix of the fully connected layer attached to the output layer of the VGG-19 network; F vgg Represents the output features of the last layer of the VGG-19 network. The output features F vgg After the linear transformation, the tanh activation function (represented by the symbol σ) is applied to perform nonlinear processing on the result of the linear transformation, and finally the image feature vector R with unified dimension is obtained. v , in preparation for subsequent splicing operations.

[0065] The advantage of using VGG-19 as the image feature extractor in this embodiment is that by stacking multiple convolutional layers, high-dimensional deep features in the image can be extracted. This network structure can capture the details and high-level semantic information of the image, while reducing the dependence on large-scale annotated data and improving the accuracy of feature extraction.

[0066] Step S2-2: semantically represent the target news text based on the BERT text encoder, and convert the obtained semantic representation into a text feature vector.

[0067] First, the target news text is vectorized and BERT is used as a text encoder. When input, the text is processed into a word sequence T = [T1, T2, …, T n ]; where n represents the length of the text. Then, the word sequence T (i.e., the input sequence) is passed into the pre-trained BERT model and converted into an input feature matrix H, which contains the contextual features of each word, namely:

[0068] H = BERT(T);

[0069] Where H represents the input feature matrix, T represents the input sequence, BERT(·) represents encoding the input sequence T, and uses BERT’s aggregate sequence representation as the text feature; the text feature BERT is input into the fully connected layer, which can be further reduced to a fixed-dimensional text feature vector R T .

[0070] In this embodiment, the advantage of using BERT as a text feature extractor is that it can capture the deep contextual semantics of words from the input text and model the text semantics more accurately. At the same time, BERT can also capture the logical relationships across sentences and long-distance word associations well.

[0071] Step S2-3: extract entities from the target news text and perform entity linking to obtain external knowledge supplements, and convert the obtained external knowledge supplements into external knowledge feature vectors.

[0072] For entities extracted from the target news text content, the entity linking tool is used to obtain external knowledge information supplement, and entity association is performed to eliminate the ambiguity between text and knowledge. The acquired external knowledge is processed into a feature vector by the same BERT text encoder as in step S2-2, and further processed into a fixed-dimensional external knowledge feature R K .

[0073] Furthermore, the entity linking tool is used to obtain external knowledge information supplements, including:

[0074] Entities in the target news text are extracted through named entity recognition technology. The bert-chinese-ner model pre-trained on Hugging Face is used here. After the entities are extracted, the API of CN-DBpedia (Fudan University's large structured encyclopedia) is used to call the conceptual knowledge corresponding to the extracted entities and write them into the document. BERT is used for subsequent processing, and the process is consistent with that of processing news text.

[0075] Furthermore, entity association is used to eliminate ambiguity between text and knowledge, including:

[0076] Extract the sentence where the entity is located and its preceding and following sentences as context, obtain the knowledge description of the candidate entity through the API, use BERT to encode the context and candidate knowledge description into vectors, calculate the cosine similarity between the context vector and each candidate description vector, and select the best candidate entity and its conceptual knowledge. Specifically, the cosine similarity formula is expressed as:

[0077]

[0078] Among them, cos_similarity represents cosine similarity, v 上下文 represents the context vector, v 知识描述 Represents the candidate knowledge description vector.

[0079] Step S2-4, calculating the time interval by extracting the occurrence time of the target news event and the news release time, and converting the obtained time interval into a time interval feature vector.

[0080] For each article (target news event), record its release time and parse all event time sets mentioned in the target news; calculate the time interval by selecting the earliest time in the time set mentioned in the article. Specifically, the time interval calculation formula is:

[0081] tinterval =t post -min{t1,t2,…,t n};

[0082] Among them, t interval represents the time interval, t post represents the news release time, {t1,t2,…,t n} represents the time set of all events mentioned in the target news; min{t1,t2,…,t n} represents the minimum value of all event time sets mentioned in the target news, that is, the earliest time.

[0083] Furthermore, the time interval information of the target news is normalized and encoded using the Sigmoid function and the tanh function to convert the obtained time interval into a time interval feature vector. Specifically:

[0084] t rep =tanh(Sigmoid(t interval ));

[0085] Among them, t rep Represents the time interval feature vector, tanh() represents the activation function, which is used to map the input value to between -1 and 1; Sigmoid() also represents the activation function, which is used to map the input value to between 0 and 1, which is suitable for representing probability or normalized results. In the specific implementation process, the time interval value is first normalized to (0,1) through the activation function Sigmoid(); then, it is mapped to the (-1,1) interval through the activation function tanh to further enhance the feature's expressiveness.

[0086] In this embodiment, the advantage of introducing the time interval feature is that it can reflect the time difference from the occurrence to the reporting of a news event, obtain additional contextual information from the time dimension, thereby enhancing the overall expression ability and improving the accuracy and timeliness of fake news detection.

[0087] Step S3, performing feature fusion on the obtained image feature vector, text feature vector, external knowledge feature vector and time interval feature vector, and inputting the fused features into a multi-layer perceptron classifier to judge the authenticity of the target news data.

[0088] The image feature vector, text feature vector, external knowledge feature vector and time interval feature vector are concatenated (feature fusion), input into the pre-trained classifier, and the result of classification is whether the target news is fake news. Specifically:

[0089] The text feature vector R is obtained through step S2 T , image feature vector R v, external knowledge feature vector R K and the time interval characteristic t rep , the four multimodal features are concatenated to obtain the joint feature representation R of the target news, which is the fused feature. Specifically, the calculation method of the joint feature representation R is as follows:

[0090]

[0091] in, Represents a connection operation. Finally, the joint feature representation R is used to judge the authenticity of the target news data through a multi-layer perceptron classifier with a fully connected layer. Furthermore, when the fused features are input into the multi-layer perceptron classifier to judge the authenticity of the target news data, the softmax logistic regression function is used to obtain the binary classification result of whether the target news is fake news. Specifically, the calculation formula is as follows:

[0092] p=softmax(W R ·R);

[0093] Among them, p represents the binary classification result, W R represents the parameters of the fully connected layer in the multi-layer perceptron, softmax() represents the calculation of the logistic regression function, and R represents the joint feature representation. The present invention not only improves the detection accuracy but also significantly improves the detection efficiency through the joint modeling of multimodal features and the efficient classification strategy of the binary classification detector, thereby realizing the rapid identification of fake news.

[0094] Different from the existing fake news detection models, the present invention proposes a multimodal fake news detection method that comprehensively considers time interval features, external knowledge features, text features, and image features. This method uses an innovative time interval feature extraction strategy, combined with text, images, and external knowledge, to provide a more comprehensive and robust fake news detection mechanism from the perspective of temporal dynamics and semantic enhancement. This method effectively avoids the problems of information loss and modal imbalance that may exist in existing models, and significantly improves the accuracy and timeliness of fake news detection through more efficient feature extraction and fusion methods.

[0095] Embodiment 2

[0096] This embodiment discloses a fake news detection system based on time interval and knowledge fusion.

[0097] The fake news detection system based on time interval and knowledge fusion includes:

[0098] The data acquisition module is configured to: acquire target news data, wherein the target news data includes a target news image and a target news text;

[0099] The feature vector acquisition module is configured to: extract image features of the target news image based on the VGG-19 convolutional neural network, and convert the obtained image features into image feature vectors; perform semantic representation of the target news text based on the BERT text encoder, and convert the obtained semantic representation into text feature vectors; extract entities from the target news text and perform entity linking to obtain external knowledge supplements, and convert the obtained external knowledge supplements into external knowledge feature vectors; calculate the time interval by extracting the occurrence time of the target news event and the news release time, and convert the obtained time interval into a time interval feature vector;

[0100] The fake news detection module is configured to: perform feature fusion on the image feature vector, text feature vector, external knowledge feature vector and time interval feature vector, and input the fused features into a multi-layer perceptron classifier to judge the authenticity of the target news data.

[0101] Embodiment 3

[0102] The purpose of this embodiment is to provide a computer-readable storage medium.

[0103] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the false news detection method based on time interval and knowledge fusion as described in the first embodiment of the present disclosure.

[0104] Embodiment 4

[0105] The purpose of this embodiment is to provide an electronic device.

[0106] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the false news detection method based on time interval and knowledge fusion as described in the first embodiment of the present disclosure are implemented.

[0107] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0108] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0109] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A fake news detection method based on time interval and knowledge fusion, characterized in that: include: Acquire target news data, wherein the target news data includes a target news image and a target news text; Based on the VGG-19 convolutional neural network, image features of the target news image are extracted, and the obtained image features are converted into image feature vectors; based on the BERT text encoder, semantic representation of the target news text is performed, and the obtained semantic representation is converted into a text feature vector; entities are extracted from the target news text and entity links are performed to obtain external knowledge supplements, and the obtained external knowledge supplements are converted into external knowledge feature vectors; the time interval is calculated by extracting the occurrence time of the target news event and the news release time, and the obtained time interval is converted into a time interval feature vector; The image feature vector, the text feature vector, the external knowledge feature vector and the time interval feature vector are subjected to feature fusion, and the fused features are input into a multi-layer perceptron classifier to judge the authenticity of the target news data.

2. The false news detection method based on time interval and knowledge fusion as claimed in claim 1, characterized in that: After acquiring the target news data and before extracting the features, it is necessary to determine whether there is noise data in the target news text. If so, the target news text is denoised; wherein the noise data includes special symbols and expressions in the news text.

3. The false news detection method based on time interval and knowledge fusion as claimed in claim 1, characterized in that: The last layer of the VGG-19 convolutional neural network is a fully connected layer, which is used to adjust the dimension of the output image features.

4. The false news detection method based on time interval and knowledge fusion as claimed in claim 1, characterized in that: The target news text is semantically represented based on the BERT text encoder, including: vectorizing the target news text and processing it into a word sequence, and inputting the word sequence into the BERT text encoder; encoding the word sequence based on the BERT text encoder, and using the aggregated sequence of the BERT text encoder as the semantic representation of the target news text.

5. The false news detection method based on time interval and knowledge fusion as claimed in claim 1, characterized in that: Use entity linking tools to obtain external knowledge supplements and eliminate ambiguity between text and knowledge based on entity associations.

6. The false news detection method based on time interval and knowledge fusion as claimed in claim 1, characterized in that: The time interval is normalized and encoded to convert the obtained time interval into a time interval feature vector.

7. The false news detection method based on time interval and knowledge fusion as claimed in claim 1, characterized in that: When the fused features are input into the multi-layer perceptron classifier to judge the authenticity of the target news data, the softmax logistic regression function is used to obtain the binary classification result of whether the target news is false news.

8. A fake news detection system based on time interval and knowledge fusion, characterized by: include: The data acquisition module is configured to: acquire target news data, wherein the target news data includes a target news image and a target news text; The feature vector acquisition module is configured to: extract image features of the target news image based on the VGG-19 convolutional neural network, and convert the obtained image features into image feature vectors; perform semantic representation of the target news text based on the BERT text encoder, and convert the obtained semantic representation into text feature vectors; extract entities from the target news text and perform entity linking to obtain external knowledge supplements, and convert the obtained external knowledge supplements into external knowledge feature vectors; calculate the time interval by extracting the occurrence time of the target news event and the news release time, and convert the obtained time interval into a time interval feature vector; The fake news detection module is configured to: perform feature fusion on the image feature vector, text feature vector, external knowledge feature vector and time interval feature vector, and input the fused features into a multi-layer perceptron classifier to judge the authenticity of the target news data.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the false news detection method based on time interval and knowledge fusion as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the false news detection method based on time interval and knowledge fusion as described in any one of claims 1 to 7 are implemented.

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