# A Hybrid Approach for Network Rumor Detection Based on Attention Mechanism and Bidirectional GRU Model in Big Data Environment

Authors: Li Zhang; Guan Huang
Journal: Mobile Information Systems
 (2022)

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## Abstract

In view of the low accuracy of Internet rumor detection in traditional machine learning detection methods due to the influence of comment directivity and incomplete features caused by too long sequences, an Internet rumor detection method combining attention mechanism and bidirectional gated recurrent unit (GRU) model in big data environment is proposed. First, the continuous bag of words (CBOW) model is modified by defining the context window and introducing the weighting module, and it is used to obtain the word vectors of the text. Then, the word vectors are used as the word embedding layer which is fed into a bidirectional GRU combined with an attention mechanism through the convolutional layer and pooling layer of the convolutional neural network (CNN) to fully extract the features of the text. Finally, the text feature vectors are input into the Softmax classifier for classification so as to realize the detection of Internet rumors. Based on the selected microblog dataset, the experimental results of the proposed model show that when the learning rate is set to 0.015, Accuracy, Precision, Recall, and F1 values are about 95.8%, 93.1%, 92.9% and 93.5%, respectively, which are better than other comparison models.


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## Body

## 1. Introduction

In the era of high-speed development of the Internet, social media will release news and information on the major social network platforms, and people also get relevant information on these platforms. At the same time, with the growth of social platforms, Internet users can also express their opinions on platforms, which make communication among people become more convenient [1]. Traditional newspapers and magazines are limited by convenience, so rumors cannot spread quickly and are generally restricted within a single region [2, 3]. The advanced information technology and the popularity of network information exchange, as well as the emergence of communication tools such as the Internet and cell phones, have broadened the ways and means of information dissemination. Also, they greatly reduced the cost of rumor fabrication to a certain extent, thus speeding up the spread of rumors, expanding their scope of dissemination, making them more personalized and open, and making them easier to fabricate and spread. The Internet users are able to create and spread rumors very conveniently [4, 5]. As a result, the spread of rumors can cause great harm to society, and the management of Internet rumors has become one of the most urgent issues to be solved [6].Nowadays, rumor propagation modeling is connected with social networks to establish a brand new rumor propagation model consistent with the characteristics of social networks, which can not only predict the peak point and specific time of rumors in the process of propagation, but also combine statistical methods to achieve parameter estimation and analysis, so as to predict the propagation trend and provide a theoretical reference for relevant functional departments [7, 8]. Furthermore, since Internet rumors are characterized by low cost, high speed, wide range, and wide channels, it is of great research value and social significance to identify rumors and stop their spread in a timely, effective, and accurate manner, to warn and alert them at an early stage, and to reduce the harm caused by rumors [9, 10].For the detection of Internet rumors, there has been some research at home and abroad, which can be divided into supervised and unsupervised based methods and deep learning-based detection methods [11, 12]. For example, Arp et al. in Ref. [13] focused on the datasets and the latest methods for rumor detection. In addition, the advantages and disadvantages of supervised and unsupervised methods and deep learning methods used for rumor detection are discussed in depth. In Ref [14], a network integrating entity recognition, sentence reconstruction, and ordinary differential equations was proposed, using the entity recognition method to enhance the semantic understanding of rumor texts and designing sentence reconstruction to advance the detection of important word frequencies, which effectively improves the efficiency of rumor detection. However, the efficiency of rumor detection under big data still needs improvement. In Ref. [15], Li et al. proposed a model to identify rumors based on homogeneity and conversation structure. A more comprehensive and accurate expression is learned by modeling the relationship of users and followers, simulating the propagation layout of tweets, and connecting the behaviors of responders to effectively achieve online rumor detection. A rumor detection method based on source detection in social networks was proposed in Ref. [16], which effectively improves the accuracy and efficiency of source detection of online information by introducing graphical classification, and has good support for Internet rumor detection.With the continuous development of computer technology, the advantages of deep learning algorithms in big data processing have gradually emerged, and they have been widely used in Internet rumor detection. A deep transfer model was proposed in Ref. [17] to achieve accurate rumor detection on social media platforms. But it fails to balance the efficiency and accuracy of the detection process. A novel early rumor detection model based on reinforcement learning was proposed in Ref. [18]. The dual-engine rumor detection model based on deep learning can achieve differential feature extraction of the original tweets and their replies. Combining the dual self-attention mechanism, it can eliminate data redundancy in both sentences and words, which can balance the efficiency and accuracy of rumor detection. However, the detection performance is poor when it comes to complex data sources. In Ref. [19], to address the problem that existing techniques need to periodically update rumor detection patterns to identify new rumors, a deep learning model that does not need to periodically update rumor-related patterns to detect rumor information was proposed, which effectively improves the efficiency of Internet rumor detection. But the accuracy of detection requires further improvement.Based on the above analysis, an Internet rumor detection model combining attention mechanism and bidirectional gated recurrent unit (GRU) model in big data environment is proposed to address the problem of low accuracy of rumor detection in traditional machine learning or deep learning algorithms. The main contributions are summarized as follows.(1)
As the traditional continuous bag of words (CBOW) model ignores the order of words and does not reflect the importance of the current word to the global information of the context, the proposed method is improved by introducing a context window and designing a weighted CBOW model structure to obtain the word vector information more comprehensively(2)
In order to improve the detection accuracy, the proposed model incorporates an attention mechanism based on the bidirectional GRU network to consider the magnitude of different words’ contribution to the model’s classification, providing a richer text representation and further ensuring the detection effect


## 2. Microblog Rumor Data Collection and Word Vector Acquisition

### 2.1. Microblog Rumor Data Collection

The research object is microblog; thus, enough data should be collected from microblog for experimental analysis. The data crawling will be divided into two parts, which correspond to two parts of the experiment. Nearly 50,000 pieces of data are crawled in the first part, which include both rumor data and nonrumor data (half of each). In order to ensure the reliability of information, relevant nonrumor data are crawled from major channels, while rumor data are crawled mainly through messages released by official rumor-denial microblog accounts [20]. The second part is about the rumor dissemination data. In this part, some widely spread rumors are first targeted. Then some influential disseminators among those who forward these rumors are locked, so as to crawl the specific forwarding network.


### 2.2. Text Preprocessing

#### 2.2.1. Data Cleaning

Data cleaning is the process of making the collected raw data suitable for subsequent analysis and modeling. The temporal data format of the raw data is extremely nonuniform, so they are processed using the datetime package. In addition, the data are filtered because a portion of raw data have been deleted and there is no textual content.


#### 2.2.2. Word Segmentation

For natural language analysis and modeling, it is usually necessary to split the text into words, i.e., to cut a sentence into individual words [21]. There are a large number of tools for Chinese word segmentation, such as jieba, thulac, and SnowNLP. And jieba is chosen because of its high speed and accuracy. Jieba supports three modes of word segmentation, including exact mode, full mode and search engine mode. Among them, the first mode can perform the most accurate segmentation of sentences, which is suitable for scenarios such as text analysis. The second mode can perform the most detailed segmentation of the sentences, and all possible words will be listed, which is the fastest. However, the results may be ambiguous because of the redundancy in the segmented words. The last mode further divides longer words on the basis of the first mode. The exact mode of jieba is chosen because of the need for data analysis and modeling.


#### 2.2.3. Stop Words Deletion

The concept of stop words was first introduced in the field of information retrieval to reduce storage space and improve computational and search efficiency by filtering out some meaningless words or phrases in the text. Specifically, stop words are words such as “the,” “had,” and “is,” which have no real meaning compared to other words. These words are widely used but contain little information and can interfere with search engines, thus reducing search efficiency and preventing them from giving truly relevant search results. Therefore, they are usually removed from sentences to improve search performance. Since rumor data and nonrumor data need to be analyzed, and stop words will affect the analysis results, increase model complexity, reduce model performance, and affect detection performance, each article will be split into words to be preprocessed, and all stop words will be removed [22].After data cleaning and preprocessing, the details of the final collected microblog rumor dataset are shown in Table1.Table 1
Basic attributes of microblog rumor dataset.
AttributeRumor eventsReal eventsTotalNumber of events5205141034Number of articles129371185124788Average number of articles per event24.8823.0623.97Average number of words per event312930653092


### 2.3. Word Vector Acquisition

A modified CBOW model is used for word vector acquisition, and the structure of CBOW model is shown in Figure1. It is divided into three main layers, which are the input layer, the projection layer, and the output layer.Figure 1
Structure of CBOW model.The model predicts the current word based on the context as input, and the context is selected using a window sliding approach, i.e., the words within a specified window based on the current word are selected as the context. However, two refinements are needed in the probabilistic prediction of the current wordct when it is achieved by specifying the context information within different the window sizes, i.e., ct−2, ct−1, ct+1, and ct+2.
(1)
From the input layer to the projection layer, the word vectors based on the context are summed cumulatively to obtain the vectors in the projection layer. However, this approach assumes that the importance of words in the context is the same and words exist in isolation from each other. Thus, the word order position information of the current word is ignored, while it is helpful for the semantic expression of the current word(2)
For the word vector with the specified window size of 2:c1c2xc3c4 where the prediction of the current word x is mainly determined by the context c1,c2,c3,c4 of the specified window size, but not associated with the global information of the text. Therefore, the word vector cannot reflect the importance of the current word to the global information of the textThe improvement methods are proposed for the two refinements needed in the CBOW model, respectively, which are described below.(1)
To address the problem that the CBOW model ignores the word order position information of the current word, the word vectors of contexts in fixed size are spliced by limiting the window of contexts. After that, the word vectors are averaged cumulatively, so as to preserve the word position information and avoid the dimensional disaster to some extentIn the modified CBOW model, the window size of the context is set to 2 and is expressed as(1)vct=vct−2⊕vct−1⊕vct+1⊕vct+2,where ⊕ denotes the splicing of word vectors and vc denotes the word vector of the text.
(2)
In response to the inability of the CBOW model to reflect the importance of the current word to the global information of the text, a weighted CBOW model structure is proposed so that the word vector can reflect both the importance of the current word in the document and the semantic information between words. The weighted partϑF mainly considers the frequency pF of feature words in the document and the measure ωF of the importance of a feature word, which can be expressed as(2)ϑF=pF×ωF,ωF=lgNn+1,where n denotes the number of texts containing feature word F in the text set and N denotes the total number of texts in the text set. When the number of texts containing the feature word F is larger, the value of ωF for the feature word will be smaller and the feature word F will be marked as having poor classification ability. However, in practical applications, when the total number of texts containing the feature word F is large, there are two cases: (1) when the number k of texts containing feature word F in the category li is large and the number m of texts containing feature word F in the remaining of the categories is smaller, the feature word F is with good classification ability; (2) when the feature word F is distributed uniformly in all categories, i.e., the difference between the number k of texts contained in the category li and the number m of texts contained in the remaining of the categories is small, then the classification ability is poor.Hence, when the numberk of texts containing the feature word F is large, the traditional measure ωF is imperfect in terms of the weight calculation for the feature word F, as it ignores the first case. Thus, an influence factor σ is introduced, which can be defined as
(3)σF,li=km.Moreover, if the numberk of texts containing feature word F in the category li is large, and the number m of texts containing feature word F in the remaining categories is small, then σ is large, i.e., the influence coefficient of feature word F is high. If the feature word F are uniformly distributed in all categories, i.e., the difference between the values of k and m is small, then σ≈1, i.e., the influence coefficient of feature word F is low.Based on the above, the improved equation forϑF can be formulated as
(4)ϑFF,d=pFF,d×lgNn×σ+1,where ϑFF,d denotes the weight value of the feature word F in the document d.The weighted CBOW model further improves the semantic representation of the trained word vectors. First, the CBOW model in word2vec is used to train the corpus data, and the obtained word vectors are represented as(5)vci=CBOWci.Then, the weightϑF is added. The word vector can be written as
(6)vci=ϑF×CBOWci.


## 2.1. Microblog Rumor Data Collection

The research object is microblog; thus, enough data should be collected from microblog for experimental analysis. The data crawling will be divided into two parts, which correspond to two parts of the experiment. Nearly 50,000 pieces of data are crawled in the first part, which include both rumor data and nonrumor data (half of each). In order to ensure the reliability of information, relevant nonrumor data are crawled from major channels, while rumor data are crawled mainly through messages released by official rumor-denial microblog accounts [20]. The second part is about the rumor dissemination data. In this part, some widely spread rumors are first targeted. Then some influential disseminators among those who forward these rumors are locked, so as to crawl the specific forwarding network.


## 2.2. Text Preprocessing

### 2.2.1. Data Cleaning

Data cleaning is the process of making the collected raw data suitable for subsequent analysis and modeling. The temporal data format of the raw data is extremely nonuniform, so they are processed using the datetime package. In addition, the data are filtered because a portion of raw data have been deleted and there is no textual content.


### 2.2.2. Word Segmentation

For natural language analysis and modeling, it is usually necessary to split the text into words, i.e., to cut a sentence into individual words [21]. There are a large number of tools for Chinese word segmentation, such as jieba, thulac, and SnowNLP. And jieba is chosen because of its high speed and accuracy. Jieba supports three modes of word segmentation, including exact mode, full mode and search engine mode. Among them, the first mode can perform the most accurate segmentation of sentences, which is suitable for scenarios such as text analysis. The second mode can perform the most detailed segmentation of the sentences, and all possible words will be listed, which is the fastest. However, the results may be ambiguous because of the redundancy in the segmented words. The last mode further divides longer words on the basis of the first mode. The exact mode of jieba is chosen because of the need for data analysis and modeling.


### 2.2.3. Stop Words Deletion

The concept of stop words was first introduced in the field of information retrieval to reduce storage space and improve computational and search efficiency by filtering out some meaningless words or phrases in the text. Specifically, stop words are words such as “the,” “had,” and “is,” which have no real meaning compared to other words. These words are widely used but contain little information and can interfere with search engines, thus reducing search efficiency and preventing them from giving truly relevant search results. Therefore, they are usually removed from sentences to improve search performance. Since rumor data and nonrumor data need to be analyzed, and stop words will affect the analysis results, increase model complexity, reduce model performance, and affect detection performance, each article will be split into words to be preprocessed, and all stop words will be removed [22].After data cleaning and preprocessing, the details of the final collected microblog rumor dataset are shown in Table1.Table 1
Basic attributes of microblog rumor dataset.
AttributeRumor eventsReal eventsTotalNumber of events5205141034Number of articles129371185124788Average number of articles per event24.8823.0623.97Average number of words per event312930653092


## 2.2.1. Data Cleaning

Data cleaning is the process of making the collected raw data suitable for subsequent analysis and modeling. The temporal data format of the raw data is extremely nonuniform, so they are processed using the datetime package. In addition, the data are filtered because a portion of raw data have been deleted and there is no textual content.


## 2.2.2. Word Segmentation

For natural language analysis and modeling, it is usually necessary to split the text into words, i.e., to cut a sentence into individual words [21]. There are a large number of tools for Chinese word segmentation, such as jieba, thulac, and SnowNLP. And jieba is chosen because of its high speed and accuracy. Jieba supports three modes of word segmentation, including exact mode, full mode and search engine mode. Among them, the first mode can perform the most accurate segmentation of sentences, which is suitable for scenarios such as text analysis. The second mode can perform the most detailed segmentation of the sentences, and all possible words will be listed, which is the fastest. However, the results may be ambiguous because of the redundancy in the segmented words. The last mode further divides longer words on the basis of the first mode. The exact mode of jieba is chosen because of the need for data analysis and modeling.


## 2.2.3. Stop Words Deletion

The concept of stop words was first introduced in the field of information retrieval to reduce storage space and improve computational and search efficiency by filtering out some meaningless words or phrases in the text. Specifically, stop words are words such as “the,” “had,” and “is,” which have no real meaning compared to other words. These words are widely used but contain little information and can interfere with search engines, thus reducing search efficiency and preventing them from giving truly relevant search results. Therefore, they are usually removed from sentences to improve search performance. Since rumor data and nonrumor data need to be analyzed, and stop words will affect the analysis results, increase model complexity, reduce model performance, and affect detection performance, each article will be split into words to be preprocessed, and all stop words will be removed [22].After data cleaning and preprocessing, the details of the final collected microblog rumor dataset are shown in Table1.Table 1
Basic attributes of microblog rumor dataset.
AttributeRumor eventsReal eventsTotalNumber of events5205141034Number of articles129371185124788Average number of articles per event24.8823.0623.97Average number of words per event312930653092


## 2.3. Word Vector Acquisition

A modified CBOW model is used for word vector acquisition, and the structure of CBOW model is shown in Figure1. It is divided into three main layers, which are the input layer, the projection layer, and the output layer.Figure 1
Structure of CBOW model.The model predicts the current word based on the context as input, and the context is selected using a window sliding approach, i.e., the words within a specified window based on the current word are selected as the context. However, two refinements are needed in the probabilistic prediction of the current wordct when it is achieved by specifying the context information within different the window sizes, i.e., ct−2, ct−1, ct+1, and ct+2.
(1)
From the input layer to the projection layer, the word vectors based on the context are summed cumulatively to obtain the vectors in the projection layer. However, this approach assumes that the importance of words in the context is the same and words exist in isolation from each other. Thus, the word order position information of the current word is ignored, while it is helpful for the semantic expression of the current word(2)
For the word vector with the specified window size of 2:c1c2xc3c4 where the prediction of the current word x is mainly determined by the context c1,c2,c3,c4 of the specified window size, but not associated with the global information of the text. Therefore, the word vector cannot reflect the importance of the current word to the global information of the textThe improvement methods are proposed for the two refinements needed in the CBOW model, respectively, which are described below.(1)
To address the problem that the CBOW model ignores the word order position information of the current word, the word vectors of contexts in fixed size are spliced by limiting the window of contexts. After that, the word vectors are averaged cumulatively, so as to preserve the word position information and avoid the dimensional disaster to some extentIn the modified CBOW model, the window size of the context is set to 2 and is expressed as(1)vct=vct−2⊕vct−1⊕vct+1⊕vct+2,where ⊕ denotes the splicing of word vectors and vc denotes the word vector of the text.
(2)
In response to the inability of the CBOW model to reflect the importance of the current word to the global information of the text, a weighted CBOW model structure is proposed so that the word vector can reflect both the importance of the current word in the document and the semantic information between words. The weighted partϑF mainly considers the frequency pF of feature words in the document and the measure ωF of the importance of a feature word, which can be expressed as(2)ϑF=pF×ωF,ωF=lgNn+1,where n denotes the number of texts containing feature word F in the text set and N denotes the total number of texts in the text set. When the number of texts containing the feature word F is larger, the value of ωF for the feature word will be smaller and the feature word F will be marked as having poor classification ability. However, in practical applications, when the total number of texts containing the feature word F is large, there are two cases: (1) when the number k of texts containing feature word F in the category li is large and the number m of texts containing feature word F in the remaining of the categories is smaller, the feature word F is with good classification ability; (2) when the feature word F is distributed uniformly in all categories, i.e., the difference between the number k of texts contained in the category li and the number m of texts contained in the remaining of the categories is small, then the classification ability is poor.Hence, when the numberk of texts containing the feature word F is large, the traditional measure ωF is imperfect in terms of the weight calculation for the feature word F, as it ignores the first case. Thus, an influence factor σ is introduced, which can be defined as
(3)σF,li=km.Moreover, if the numberk of texts containing feature word F in the category li is large, and the number m of texts containing feature word F in the remaining categories is small, then σ is large, i.e., the influence coefficient of feature word F is high. If the feature word F are uniformly distributed in all categories, i.e., the difference between the values of k and m is small, then σ≈1, i.e., the influence coefficient of feature word F is low.Based on the above, the improved equation forϑF can be formulated as
(4)ϑFF,d=pFF,d×lgNn×σ+1,where ϑFF,d denotes the weight value of the feature word F in the document d.The weighted CBOW model further improves the semantic representation of the trained word vectors. First, the CBOW model in word2vec is used to train the corpus data, and the obtained word vectors are represented as(5)vci=CBOWci.Then, the weightϑF is added. The word vector can be written as
(6)vci=ϑF×CBOWci.


## 3. Modified CBOW Model and Network Structure of CNN

After obtaining the embedding information of the text, the feature of the text needs to be extracted. The feature extraction is mainly based on the convolutional layer and pooling layer of the convolutional neural network (CNN). In the convolutional layer, a feature mappingfi can be obtained by convolution operation. And then, based on each feature mapping, they are stacked up in rows. The mathematical expression can be written as
(7)fi=δω∗vi:i+h−1+b,f=f1,f2,⋯,fn−h+1,where b∈R is a bias term. vi∈Rk represents the word vector of the ith word in the sentence, and δ is a nonlinear activation function. A scaled exponential linear unit (SeLU) activation function is introduced in this model, which is expressed as
(8)fx=λx,x>0,αexpx−1,x≤0,where α and λ are the parameters of the negative half-axis and scaling degree of the control function, respectively.The network obtained based on the SeLU activation function is capable of self-normalization, which enables the sample distribution to be automatically normalized to the mean value of 0 and unit variance. At the same time, the network can effectively prevent risks such as gradient explosion and vanishing gradient.After the convolution operation, the maximum pooling is used to take out the maximum value of the feature vector in the corresponding convolution kernel as the most important feature [23].At the same time, in order to avoid overfitting, dropout is added after the completion of the pooling layer. Namely, during the training, some neurons are randomly dropout to make some neuron nodes stop working, so that the model can be more generalized.


## 4. An Internet Rumor Detection Model Combining Attention Mechanism and Bidirectional GRU

For topics published on online social platforms, including the comments of netizens under them, they usually contain hundreds or thousands of words, and hot topics will be more. There will be many words that are not relevant to the detection classification task, which will have some influence on the classification performance of the model. Therefore, a text classification method combining attention mechanism is proposed based on a bidirectional GRU model, which considers the contribution of different words to the model classification and provides a richer text representation. Among them, the unidirectional GRU model can deduce the later information based on the previous information. But sometimes it is not enough to just look at the previous words, while the bidirectional GRU model can understand the context, which is more conducive to enhancing the understanding of the model [24].In the proposed Internet rumor detection model, the input is the original topics on the online social platform and the social comment data corresponding to each record. The model needs to learn the writing style and the extent of fraud in the rumor text as well as learn the subjective and objective degree and sentence-to-sentence correlation in the social comment data. The overall structure of the bidirectional GRU Internet rumor detection model based on attention mechanism is shown in Figure2.Figure 2
Bidirectional GRU model structure based on attention mechanism.The model is based on bidirectional GRU, and its principle is similar to long short-term memory (LSTM), which relies on historical information to make corresponding predictions at the present point. It is also based on the gate mechanism. The gate mechanism of LSTM mainly controls the information of memory unit through input gate, forget gate, and output gate to solve the problem of long dependence of information. And the GRU model improves the structure of the gate mechanism of LSTM, which is more concise than LSTM.GRU has two gates, one is the reset gate and the other is the update gate. At the time stept, the update gate can be calculated as
(9)zt=sigmoidwzxt+uzht−1,where xt is the vector input at the tth time step, which undergoes a linear transformation. ht−1saves the information from the previous time step, which also undergoes a linear transformation. The above transformed information is summed up and fed into the activation function, and the final result is transformed between 0 and 1. This determines the proportion of historical information that continues to be transmitted, copying information from the past to reduce the risk of vanishing gradient.At the time stept, the reset gate can be calculated as
(10)rt=sigmoidwrxt+urht−1.The reset gate is calculated in the same way as the update gate, except that the parameters used for the linear transformation are changed. The reset gate calculates the proportion of nonfocused information which will be filtered out later.At the time stept, the candidate hidden layer can be calculated as
(11)ht′=tanhwxt+urt⊙ht−1.The candidate hidden layer can be understood as the current memory content. The proportion of filtered information in the hidden layer is controlled, and then this information is added with the informationht′ of the hidden layer at the present moment to obtain the final output information ht of the hidden layer, which is also the final memory of the current time step. It can be formulated as
(12)ht=zt⊙ht−1+1−zt⊙ht′.The model allocates more resources to the relatively important information in the context of the text sequence by applying attention mechanism, while other resources are allocated at a lower ratio. In some special cases, the information irrelevant to model classification is discarded directly, which can solve the problem of too much information that the model cannot receive all of it to a certain extent and improve the efficiency of model classification [25].With the advancement of attention mechanism, the calculation will be designed differently according to different scenarios. The proposed model uses self-attention.aijc denotes the degree of alignment between the model output word ci and input word cj, which can be calculated as
(13)aijc=expeijc∑g=1Nexpeigc,where the score eijc depends on the hidden state of decoder at the previous moment before moment ti and the hidden state of encoder at moment tj.The context vector can be obtained using all the hidden states of encoder at the momentti based on the alignment. Thus, the expectation Ei for all alignment cases can be calculated as
(14)Ei=∑j=1Naijchj.The attention mechanism makes it possible to focus only on the information in the part of the original sentence, instead of gathering all the information of the original sentence together. Then the information is encoded uniformly and converted into a specific feature vector so as to be input to the model for training [26, 27]. The attention mechanism can better extract the important features in the sample data as it can assign corresponding weights to the key information in each part of the sentence and decide the proportion of each part to be retained based on the weights.The model will finally find out the probability corresponding to its classification type by Softmax function, and the type with high probability will be selected as the result.


## 5. Experiment and Analysis

To validate the performance of the proposed Internet rumor detection model, experiments are conducted on the microblog rumor dataset. And in order to obtain a model with better generalization ability, all events are divided into training/validation/testing sets in a 8 : 1 : 1 ratio, and the model hyper-parameters and structure are selected based on the recall rate of the model on the validation set. The server hardware environment used in this experiment is as follows: graphics card GTX1080Ti dual card, 22G video memory, CPU2.6GHz, 16 threads, 32G memory capacity, and 240G hard disk capacity, and the operating system is Ubuntu 16.04.In addition, in the experiments, the Adam optimizer is used to train the model with the following parameters. The distributed word vector dimension is set to 250. The sizes of the convolutional kernels used are 3, 4, and 5. And each convolutional kernel is set to 100. In the pooling layer, dropout is used to avoid overfitting, and its parameter is set to 0.5. To prevent vanishing gradient and gradient explosion, SeLU activation function is used to further improve the robustness of the model.


### 5.1. Evaluation Index

Accuracy, Recall, Precision, and F1 values are used to compare and analyze the proposed model. The evaluation index of the Internet rumor detection model can be calculated as(15)Accuracy=PAPA+FA,(16)Recall=PAPA+FN,(17)Precision=PA+PNPA+FA+FN+PN,(18)F1=Precision∗RecallPrecision+Recall.where PA is the number of documents correctly classified to the true class, FA is the number of documents incorrectly classified to the true class, FN is the number of correct documents incorrectly classified to the false class, and PN is the number of documents correctly classified to the false class.


### 5.2. The Curves of Evaluation Index under Different Learning Rates of Different Model

The detection results of each model (Ref. [15], Ref. [17], Ref. [18], and the proposed model) differ when the learning rate is different, and the results of different evaluation index are shown in Figure 3.Figure 3
Change curves of evaluation index under different learning rates of each model.
(a)
Accuracy curve(b)
Precision curve(c)
Recall curve(d)
F1 curveFrom Figure3, it can be seen that the proposed model has the best performance for rumor detection compared with other models regardless of the learning rate. It is also found that each model has an inflection point when the learning rate is 0.015, which indicates that the parameter of learning rate is more suitable to be set to 0.015 for training. Meanwhile, compared with other models, the proposed model can achieve a better performance of classification in the rumor detection at the learning rate of 0.015, with Accuracy, Precision, Recall, and F1 values of about 95.8%, 93.1%, 92.9%, and 93.5%, respectively. However, when the learning rate is greater than 0.015, the effectiveness of the proposed model and the models studied in Ref. [17] and Ref. [18] will be degraded due to the possible overfitting state in training. Therefore, in the parameter setting, the experimental demonstration is carried out when the selected learning rate is 0.015.


### 5.3. Performance Comparison of Models

To demonstrate the detection performance of the proposed model, it is compared with Ref. [15], Ref. [17], and Ref. [18] based on the selected microblog dataset, and the values of each evaluation index are shown in Table 2.Table 2
Comparison of various models based on microblog dataset.
Ref. [15]Ref. [17]Ref. [18]Proposed modelAccuracy/%88.390.593.195.8Recall/%85.289.392.192.9Precision/%87.589.191.693.1F1 value/%86.692.292.893.5From Table2, it can be seen that the proposed model performs better in the detection of Internet rumor dataset, and the index values of Accuracy, Precision, Recall, and F1 are higher than those of other comparison models, which also indicates that the dataset of Internet rumor detection is strongly correlated with time and the bidirectional GRU-based model is more accurate in detecting Internet rumors. Also, by combining the attention mechanism and modified CBOW model. The detection precision is further improved.The model proposed in Ref. [15] detects rumor based on homogeneity and conversation structure model, but the model structure is simple and not applicable to big data environment, so the recognition performance is poor and Accuracy is less than 90%. Ref. [17] uses deep transfer learning model to realize rumor detection on social media platforms, and Ref. [18] conducts rumor detection based on Deep Q-Learning. So both of them use deep reinforcement learning models to improve data processing capabilities, and their detection performances are improved compared with Ref. [15]. But due to the lack of consideration of correlation information and incomplete text feature extraction, their detection performance still needs improvement compared to the proposed model.


## 5.1. Evaluation Index

Accuracy, Recall, Precision, and F1 values are used to compare and analyze the proposed model. The evaluation index of the Internet rumor detection model can be calculated as(15)Accuracy=PAPA+FA,(16)Recall=PAPA+FN,(17)Precision=PA+PNPA+FA+FN+PN,(18)F1=Precision∗RecallPrecision+Recall.where PA is the number of documents correctly classified to the true class, FA is the number of documents incorrectly classified to the true class, FN is the number of correct documents incorrectly classified to the false class, and PN is the number of documents correctly classified to the false class.


## 5.2. The Curves of Evaluation Index under Different Learning Rates of Different Model

The detection results of each model (Ref. [15], Ref. [17], Ref. [18], and the proposed model) differ when the learning rate is different, and the results of different evaluation index are shown in Figure 3.Figure 3
Change curves of evaluation index under different learning rates of each model.
(a)
Accuracy curve(b)
Precision curve(c)
Recall curve(d)
F1 curveFrom Figure3, it can be seen that the proposed model has the best performance for rumor detection compared with other models regardless of the learning rate. It is also found that each model has an inflection point when the learning rate is 0.015, which indicates that the parameter of learning rate is more suitable to be set to 0.015 for training. Meanwhile, compared with other models, the proposed model can achieve a better performance of classification in the rumor detection at the learning rate of 0.015, with Accuracy, Precision, Recall, and F1 values of about 95.8%, 93.1%, 92.9%, and 93.5%, respectively. However, when the learning rate is greater than 0.015, the effectiveness of the proposed model and the models studied in Ref. [17] and Ref. [18] will be degraded due to the possible overfitting state in training. Therefore, in the parameter setting, the experimental demonstration is carried out when the selected learning rate is 0.015.


## 5.3. Performance Comparison of Models

To demonstrate the detection performance of the proposed model, it is compared with Ref. [15], Ref. [17], and Ref. [18] based on the selected microblog dataset, and the values of each evaluation index are shown in Table 2.Table 2
Comparison of various models based on microblog dataset.
Ref. [15]Ref. [17]Ref. [18]Proposed modelAccuracy/%88.390.593.195.8Recall/%85.289.392.192.9Precision/%87.589.191.693.1F1 value/%86.692.292.893.5From Table2, it can be seen that the proposed model performs better in the detection of Internet rumor dataset, and the index values of Accuracy, Precision, Recall, and F1 are higher than those of other comparison models, which also indicates that the dataset of Internet rumor detection is strongly correlated with time and the bidirectional GRU-based model is more accurate in detecting Internet rumors. Also, by combining the attention mechanism and modified CBOW model. The detection precision is further improved.The model proposed in Ref. [15] detects rumor based on homogeneity and conversation structure model, but the model structure is simple and not applicable to big data environment, so the recognition performance is poor and Accuracy is less than 90%. Ref. [17] uses deep transfer learning model to realize rumor detection on social media platforms, and Ref. [18] conducts rumor detection based on Deep Q-Learning. So both of them use deep reinforcement learning models to improve data processing capabilities, and their detection performances are improved compared with Ref. [15]. But due to the lack of consideration of correlation information and incomplete text feature extraction, their detection performance still needs improvement compared to the proposed model.


## 6. Conclusion

With the popularity of online social network, Internet rumors can have a very great impact on society, and how to accurately detect rumors on online social network platforms is particularly important to maintain the order of society. Therefore, an Internet rumor detection model combining attention mechanism and bidirectional GRU model in a big data environment is proposed. The classification model takes the word vectors obtained from the modified CBOW model as the word embedding layer and inputs them into the convolutional and pooling layers of CNN and the bidirectional GRU network combining the attention mechanism to fully extract the features of text for the classification of Softmax-based text. The experimental results based on the microblog dataset show that the proposed model optimizes the construction method of word vectors and the classification model, respectively, so that the text representation is more semantically informative and the classification accuracy is better, with the values of each index exceeding 90%.The proposed model shows better performance, but due to the complexity of the model structure, there are great many parameters involved. In the future, we will adjust and optimize the parameters by cross-validation and other methods. Meanwhile, we will try to combine different attention mechanisms to optimize the model and increase the weight of the features with higher impact on the text, so as to make the model more generalizable and robust.



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*Source: 4185208-2022-04-28.xml*