# Application Research of Fake News and Rumors Detection in Complex Network Environment

Authors: Meng-Zhe Huang; Rong-Wang Yin
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Nowadays, the network environment is very complicated, and so is the information transmission in the network. False news and rumors have become a big problem in the network environment. How to detect the effective information content in the complex network environment? Interest in effective detection techniques has also grown rapidly in recent years. There is an urgent need to develop effective tools to address this challenge by employing advanced Artificial Intelligence (AI) technologies. In this article, we analyze and study the current state of fake news and rumors in the complex network environment, summarize different methods of detecting fake news and rumors, and point out the important directions for the application of intelligent models in the detection of false information sources. The main purpose is to show possible solutions on the one hand, and on the other hand to determine the main challenges and methodological inadequacy to stimulate future research.


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## Body

## 1. Introduction

The Internet has become an important publishing tool for journalists and the main way to find the latest news. Facts have proved that social networks have the ability to spread breaking news faster than traditional media. However, social networks also have their downsides: the lack of verification of facts greatly compromises the validity and reliability of information. People often post or share others’ posts to verify the validity and reliability of the source or information. Usually, an eye-catching headline is enough to make an article shared thousands of times, although it may contain unverified or false content. For various reasons, countless fake news and rumors spread through social media every day.Nowadays, the popularity of social networks has greatly promoted the widespread dissemination of false information, which has profoundly affected readers’ awareness and understanding of the incident. False information has become a social problem that has to be faced. If it is serious, it may cause a huge burden of social costs.In the past few years, research interest in fake news and rumors has greatly increased. Researchers have conducted various analysis and research on false information on the Internet in order to understand the ways and means of transmission of such information, thereby reducing its impact on social network users. Conroy et al. summarized two methods for evaluating fake news, which come from two main categories: language prompt methods and network methods [1]. Zubiaga and others introduced and discussed two kinds of rumors circulating on social media: the long-term rumors circulating for a long time, and new rumors generated in fast-paced events such as breaking news. They summarized the efforts and efforts in the development of the rumor classification system so far. Achievements, and proposed future research approaches for detecting and resolving rumors in social media mining [2]. Sharma et al. studied active intervention strategies for identifying fake news, discussed existing methods and technologies for identification, and made a comprehensive summary of the characteristics of available data sets [3]. Tschiatschek et al. developed a new algorithm DETECTIVE. The algorithm uses posterior sampling to select news that maximizes objective value and information value to understand the accuracy of user markings, and performs Bayesian inference to detect fake news [4].Based on the analysis and research of false news and rumors detection technology by Bondielli and Marcelloni [5], this article provides a more comprehensive and in-depth study on the field of fake news and rumors detection. The main content includes the following aspects: information collection, information analysis, detection technology and future development direction.


## 2. Fake News and Rumor Data Collection

For false news and rumors, the first thing we must face is to collect relevant data for analysis. Figure1 describes the main categories of false information that currently exist. This section mainly describes ways to obtain fake news and rumor data on social media.Figure 1
Classification of false information.


### 2.1. Fake News

News refers to a style that is often used by the media to record society, spread information, and reflect the times. Jana and Sophie divided the concept of fake news into two dimensions: “The fake news genre, which is misleading information in the form of fake news intentionally created, and the fake new label, which is used as a tool to weaken the legitimacy of news media” [6]. The general definition of fake news is “intentional and provable fake news articles.” Such a definition depends on two keys: intent and verifiability. Therefore, fake news is a news article that deliberately misleads readers, but it can be confirmed as fake news from other sources. According to the above content, the three key elements of fake news can be determined as follows:(1)
It is a news article(2)
It is deceptive(3)
Its content is completely or partially falseAt present, the main methods of obtaining false information are: algorithms and knowledge graph-based methods and domain experts-based methods.


### 2.2. Rumors

Online rumors refer to offensive and purposeful discourses that are spread through network media without factual basis. The spread of rumors is sudden and spread very fast, so it is easy to cause adverse effects on the normal social order.The main ways to collect information for rumor detection are: the top-down approach and the bottom-up approach. The top-down approach is mainly through searching keywords describing rumors. The main disadvantages of the top-down collection strategy are:(1)
Rumors must be a priori, making this type of data set unsuitable for discovering breaking news rumors(2)
Due to the limitations of network services, retrieval is not always successfulOn the contrary, the bottom-up approach is particularly suitable for collecting potential rumors in breaking news [7]. It first collects as much information as possible, and then manually evaluates and ranks the information. Compared with the top-down approach, this strategy increases the cost of human resources and computing resources. In addition, rumor analysis also includes subtasks such as rumor detection, rumor accuracy classification, rumor position classification, and rumor tracking.


## 2.1. Fake News

News refers to a style that is often used by the media to record society, spread information, and reflect the times. Jana and Sophie divided the concept of fake news into two dimensions: “The fake news genre, which is misleading information in the form of fake news intentionally created, and the fake new label, which is used as a tool to weaken the legitimacy of news media” [6]. The general definition of fake news is “intentional and provable fake news articles.” Such a definition depends on two keys: intent and verifiability. Therefore, fake news is a news article that deliberately misleads readers, but it can be confirmed as fake news from other sources. According to the above content, the three key elements of fake news can be determined as follows:(1)
It is a news article(2)
It is deceptive(3)
Its content is completely or partially falseAt present, the main methods of obtaining false information are: algorithms and knowledge graph-based methods and domain experts-based methods.


## 2.2. Rumors

Online rumors refer to offensive and purposeful discourses that are spread through network media without factual basis. The spread of rumors is sudden and spread very fast, so it is easy to cause adverse effects on the normal social order.The main ways to collect information for rumor detection are: the top-down approach and the bottom-up approach. The top-down approach is mainly through searching keywords describing rumors. The main disadvantages of the top-down collection strategy are:(1)
Rumors must be a priori, making this type of data set unsuitable for discovering breaking news rumors(2)
Due to the limitations of network services, retrieval is not always successfulOn the contrary, the bottom-up approach is particularly suitable for collecting potential rumors in breaking news [7]. It first collects as much information as possible, and then manually evaluates and ranks the information. Compared with the top-down approach, this strategy increases the cost of human resources and computing resources. In addition, rumor analysis also includes subtasks such as rumor detection, rumor accuracy classification, rumor position classification, and rumor tracking.


## 3. Feature Extraction of Fake News and Rumors

In this section, methods related to analyzing information, content-based methods and context-based methods are described.


### 3.1. Content Methods

The content method usually extracts information directly from the text. Through the Natural Language Processing (NLP) toolbox, it is possible to extract basic knowledge from the text and obtain a structured representation for further analysis.The content method is mainly analyzed from the perspective of sentence syntax, word and semantics. Syntax judges the reliability of information through the complexity of sentences. Words use the most prominent words or expressions as features to help determine the accuracy of the information [8]. Semantic features are usually used for sentiment analysis and opinion mining through advanced NLP technology. Semantic technology has been successfully used for fake news and rumor detection, especially in machine learning and deep learning methods [9].The content methods good performance in detecting potential deceptions makes it widely used in false news detection. However, it also has some shortcomings. First of all, in the real word application system, the deceptive text will lead to the weak generalization ability of feature extraction. Second, as the writing styles of fake news and real news become more and more similar, it becomes more and more difficult to describe them correctly. Finally, the rumor text on social media is relatively short, making it difficult to extract more valuable information.


### 3.2. Context Methods

Contextual methods mainly include user analysis, source analysis of rumors or news, analysis of information dissemination, and analysis of the chain reaction caused.User-based analysis can model the characteristics of social media users who make or share news (such as the number of posts, age of accounts, the number of friends/followers, etc.). In addition, an important aspect to consider when conducting user analysis is usability. Due to privacy restrictions, some Internet service providers may close access to users.Network-based analysis is used to model information about the network attributes of shared news, such as the number of reposts and the time characteristics of the spread [10], or clustering based on the friendship status between users and the way the information is spread. Analyze and extract features [11].


## 3.1. Content Methods

The content method usually extracts information directly from the text. Through the Natural Language Processing (NLP) toolbox, it is possible to extract basic knowledge from the text and obtain a structured representation for further analysis.The content method is mainly analyzed from the perspective of sentence syntax, word and semantics. Syntax judges the reliability of information through the complexity of sentences. Words use the most prominent words or expressions as features to help determine the accuracy of the information [8]. Semantic features are usually used for sentiment analysis and opinion mining through advanced NLP technology. Semantic technology has been successfully used for fake news and rumor detection, especially in machine learning and deep learning methods [9].The content methods good performance in detecting potential deceptions makes it widely used in false news detection. However, it also has some shortcomings. First of all, in the real word application system, the deceptive text will lead to the weak generalization ability of feature extraction. Second, as the writing styles of fake news and real news become more and more similar, it becomes more and more difficult to describe them correctly. Finally, the rumor text on social media is relatively short, making it difficult to extract more valuable information.


## 3.2. Context Methods

Contextual methods mainly include user analysis, source analysis of rumors or news, analysis of information dissemination, and analysis of the chain reaction caused.User-based analysis can model the characteristics of social media users who make or share news (such as the number of posts, age of accounts, the number of friends/followers, etc.). In addition, an important aspect to consider when conducting user analysis is usability. Due to privacy restrictions, some Internet service providers may close access to users.Network-based analysis is used to model information about the network attributes of shared news, such as the number of reposts and the time characteristics of the spread [10], or clustering based on the friendship status between users and the way the information is spread. Analyze and extract features [11].


## 4. Detection Methods

Most methods aimed at detecting fake news focus on using content features for classification. In contrast, rumor detection and verification methods usually use a mixture of content and contextual features for their models. This is because the social nature of rumors may play a key role in improving detection performance. Due to the lack of sufficient data, researchers need to test different classification algorithms when detecting fake news and rumors in order to find the most suitable algorithm. In Figure2, the existing different news detection methods are summarized. It mainly introduces the methods based on machine learning and deep learning.Figure 2
News detection method.


### 4.1. Detection Method Based on Machine Learning

Facts have proved that machine learning algorithms are very effective for solving many tasks in the field of information engineering. Most machine learning methods implemented for fake news and rumor detection use supervised learning strategies.Least Square Support Vector Machines (LSSVM) is one of the most widely used classification methods in the research field. LSSVM is a classifier defined by a single hyper-plane. The expression of the LSSVM model in the feature space is:(1)fx=ωT⋅φx+b.In formula (1), φx represents the mapping of the input sample data to the high-dimensional feature space, and ω represents the weight vector. According to the principle of structural risk minimization, comprehensively considering the function complexity and fitting error, the objective function of LSSVM can be described as(2)minω,b,ξJω,ξ=12ωTω+γ2∑i=1nξi2,s.t.yiωTφx+b=1−ξi,i=1,2,…,l.In formula (2), ξi is the error variable; γ is the adjustable parameter sample, which is used to control the degree of punishment for samples that exceed the error.Obviously, LSSVM has only equality constraints, and the loss function in the target is the second norm of the error, which greatly simplifies the solution of the problem. Define the Lagrange function(3)Lω,b,ξ,α=Jω,ξ−∑i=1lαiyiωTφxi−b−1+ξi.In formula (3), it is the Lagrange multiplier.According to the optimal conditions of Karush-Kuhn-Tucker (KKT), calculate in turn∂L/∂ω=0, ∂L/∂b=0, ∂L/∂ξi=0, ∂L/∂αi=0, i = 1,2, …, l, After eliminating ξi and ω, the following linear equations are obtained:(4)0QTQPPT+γ−1Iba=01,among them; P=φx1Ty1⋯φxlTyl; I=1⋯1T; Q=y1⋯ylT; a=a1⋯alTAccording to the Mercer condition, the kernel function can be written as(5)kxk,xh=φxkTφxh.The formula (4) can be modified as:(6)0y1⋯yly1y1y1kx1,x1+γ−1⋯y1y1kx1,xl⋮⋮⋱⋮y1y1y1kxl,xll⋯ylylkxl,xl+γ−1ba1⋮a=01⋮1.Obviously, the optimization problem of least squares support vector machine is transformed into solving the above-mentioned linear equations. Afterb and a are calculated from formula (6), the regression function of LSSSVM is(7)yx=∑i=1lαikx,xi+b.The performance of LSSVM in text spoofing detection is better than many supervised machine learning methods, and the obtainedF metric F1 is 0.84. However, as pointed out in the text, there are significant differences in performance depending on the data set selected for training [12]. Rubin et al. trained LSSVM to support multiple content-based false news detection, and obtained an F1 of 0.87 [13].Decision tree is another widely studied algorithm that can be proposed for the task of rumor and fake news analysis. Decision trees use information gain to select attributes, and perform recursive splitting on feature values to determine classification. Assuming that the category set isC=c1,c2,⋯cn, the information gain of feature fj can be expressed as(8)IGfj=−∑i=1mpcilogpci+pfj∑i=1mpci|fjlogpci|fj+pfj¯∑i=1mpci|fj¯logpci|fj¯.Among them,pci is the probability that the text in the text set belongs to category ci, pfi is the probability that feature fj appears, and pfj¯ is the probability that feature fj does not appear.Information gain is an effective feature selection method. Luo et al. combined content-based and context-based functions to evaluate the credibility of Weibo rumors, and they have reached an accuracy of more than 0.82 [14].Random forest is a combination of decision trees, and the set of prediction results of each tree is the final output. Among various machine learning algorithms for rumors and fake news tasks, random forest has a greater advantage [15].The learning algorithm based on the logistic regression model has also been used in the task of rumor position classification. Logistic regression is an improvement on the basis of linear regression, using a kernel function to map the model output to a range of0,1 value. The expression of the logistic regression function is(9)y=11+eωTx+b.In formula (9), the values of ω and b can be estimated by the “maximum likelihood method.” Hardalov et al. used logistic regression to analyze the credibility of true and false news, and the accuracy on the given data set reached 0.75 [16].Hidden Markov Model (HMM) has also been used to process sequence-based data in language recognition and information accuracy classification. Since the state cannot be directly observed in the modeling process, but the existence of the state and its attributes are predicted by the characteristics of the random process itself, it is called a hidden Markov model. The hidden Markov model can be expressed as:(10)λ=π,A,B.In formula (10), π is the initial probability distribution vector, and π=π1,π2,…,πn. Among them, πi=Pqt=θi,1≤i≤N, and N are the number of states, and qt is the state of the model at time t. A is the state transition probability matrix, A=aij of which, aij=Pqt+1=θj|qt=θi,1≤i,j≤N. B is the probability matrix of observations, B=bjk. Among them, bjk=Pot=υk|qt=θj,1≤j≤N,1≤K≤M, ot are the observed values at time t, and M is the number of observed values.The author proposes to model the characteristics of content-based and context-based rumors in the form of time series. The time series of the observed data is used to train two HMMs, one for real rumors and one for false rumors. Use these two models to estimate the likelihood of new rumors and select the most likely rumors. The proposed method achieves an overall accuracy of 0.75 [17].


### 4.2. Detection Method Based on Deep Learning

Deep learning is one of the most extensive research topics in machine learning. In modern artificial neural networks, the two most widely used networks are Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN).RNN neural network nodes are connected to each other in order to form a directed graph. Participate in the collection of data related to user attributes and text attributes through the use of Long Short-Term Memory (LSTM) networks. In order to achieve cross-time and cross-layer reverse error transmission, the LSTM unit will automatically save the error term used in each step. By maintaining a stable error term, the recursive network can learn multiple time steps. The core storage unit of LSTM is called Cell. A Cell consists of three gates (input gate, output gate, forget gate) and one Cell unit. The gate unit uses the Sigmoid function to generate the analog signal between. Ma et al. used RNN for rumor detection and obtained 0.89 and 0.95 accuracy on the corresponding data set [18].The CNN network consists of an input layer, an output layer and a hidden layer. It is the latest technology for computer vision tasks. It imitates the biological visual nervous system, shares weights in the network, effectively reduces model parameters, significantly reduces model complexity, and improves model robustness and generalization capabilities. Chen et al. solved the problem of the position and accuracy classification of tweets through the CNN network [19].In addition, the hybrid model of RNN and CNN has shown good performance in solving some complex problems. Wang et al. used CNN to encode text information and LSTM layer to encode metadata about the author of the text, thereby obtaining a hybrid model for false news detection [19]. Zubiaga and others successfully applied LSTM and hybrid LSTM-CNN models for rumor detection and achieved an accuracy of 0.82 [20].Both detection methods based on machine learning and detection methods based on deep learning can detect fake news content in a certain range. In terms of methods, deep learning puts more emphasis on active learning, which can make complex models show better performance.


## 4.1. Detection Method Based on Machine Learning

Facts have proved that machine learning algorithms are very effective for solving many tasks in the field of information engineering. Most machine learning methods implemented for fake news and rumor detection use supervised learning strategies.Least Square Support Vector Machines (LSSVM) is one of the most widely used classification methods in the research field. LSSVM is a classifier defined by a single hyper-plane. The expression of the LSSVM model in the feature space is:(1)fx=ωT⋅φx+b.In formula (1), φx represents the mapping of the input sample data to the high-dimensional feature space, and ω represents the weight vector. According to the principle of structural risk minimization, comprehensively considering the function complexity and fitting error, the objective function of LSSVM can be described as(2)minω,b,ξJω,ξ=12ωTω+γ2∑i=1nξi2,s.t.yiωTφx+b=1−ξi,i=1,2,…,l.In formula (2), ξi is the error variable; γ is the adjustable parameter sample, which is used to control the degree of punishment for samples that exceed the error.Obviously, LSSVM has only equality constraints, and the loss function in the target is the second norm of the error, which greatly simplifies the solution of the problem. Define the Lagrange function(3)Lω,b,ξ,α=Jω,ξ−∑i=1lαiyiωTφxi−b−1+ξi.In formula (3), it is the Lagrange multiplier.According to the optimal conditions of Karush-Kuhn-Tucker (KKT), calculate in turn∂L/∂ω=0, ∂L/∂b=0, ∂L/∂ξi=0, ∂L/∂αi=0, i = 1,2, …, l, After eliminating ξi and ω, the following linear equations are obtained:(4)0QTQPPT+γ−1Iba=01,among them; P=φx1Ty1⋯φxlTyl; I=1⋯1T; Q=y1⋯ylT; a=a1⋯alTAccording to the Mercer condition, the kernel function can be written as(5)kxk,xh=φxkTφxh.The formula (4) can be modified as:(6)0y1⋯yly1y1y1kx1,x1+γ−1⋯y1y1kx1,xl⋮⋮⋱⋮y1y1y1kxl,xll⋯ylylkxl,xl+γ−1ba1⋮a=01⋮1.Obviously, the optimization problem of least squares support vector machine is transformed into solving the above-mentioned linear equations. Afterb and a are calculated from formula (6), the regression function of LSSSVM is(7)yx=∑i=1lαikx,xi+b.The performance of LSSVM in text spoofing detection is better than many supervised machine learning methods, and the obtainedF metric F1 is 0.84. However, as pointed out in the text, there are significant differences in performance depending on the data set selected for training [12]. Rubin et al. trained LSSVM to support multiple content-based false news detection, and obtained an F1 of 0.87 [13].Decision tree is another widely studied algorithm that can be proposed for the task of rumor and fake news analysis. Decision trees use information gain to select attributes, and perform recursive splitting on feature values to determine classification. Assuming that the category set isC=c1,c2,⋯cn, the information gain of feature fj can be expressed as(8)IGfj=−∑i=1mpcilogpci+pfj∑i=1mpci|fjlogpci|fj+pfj¯∑i=1mpci|fj¯logpci|fj¯.Among them,pci is the probability that the text in the text set belongs to category ci, pfi is the probability that feature fj appears, and pfj¯ is the probability that feature fj does not appear.Information gain is an effective feature selection method. Luo et al. combined content-based and context-based functions to evaluate the credibility of Weibo rumors, and they have reached an accuracy of more than 0.82 [14].Random forest is a combination of decision trees, and the set of prediction results of each tree is the final output. Among various machine learning algorithms for rumors and fake news tasks, random forest has a greater advantage [15].The learning algorithm based on the logistic regression model has also been used in the task of rumor position classification. Logistic regression is an improvement on the basis of linear regression, using a kernel function to map the model output to a range of0,1 value. The expression of the logistic regression function is(9)y=11+eωTx+b.In formula (9), the values of ω and b can be estimated by the “maximum likelihood method.” Hardalov et al. used logistic regression to analyze the credibility of true and false news, and the accuracy on the given data set reached 0.75 [16].Hidden Markov Model (HMM) has also been used to process sequence-based data in language recognition and information accuracy classification. Since the state cannot be directly observed in the modeling process, but the existence of the state and its attributes are predicted by the characteristics of the random process itself, it is called a hidden Markov model. The hidden Markov model can be expressed as:(10)λ=π,A,B.In formula (10), π is the initial probability distribution vector, and π=π1,π2,…,πn. Among them, πi=Pqt=θi,1≤i≤N, and N are the number of states, and qt is the state of the model at time t. A is the state transition probability matrix, A=aij of which, aij=Pqt+1=θj|qt=θi,1≤i,j≤N. B is the probability matrix of observations, B=bjk. Among them, bjk=Pot=υk|qt=θj,1≤j≤N,1≤K≤M, ot are the observed values at time t, and M is the number of observed values.The author proposes to model the characteristics of content-based and context-based rumors in the form of time series. The time series of the observed data is used to train two HMMs, one for real rumors and one for false rumors. Use these two models to estimate the likelihood of new rumors and select the most likely rumors. The proposed method achieves an overall accuracy of 0.75 [17].


## 4.2. Detection Method Based on Deep Learning

Deep learning is one of the most extensive research topics in machine learning. In modern artificial neural networks, the two most widely used networks are Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN).RNN neural network nodes are connected to each other in order to form a directed graph. Participate in the collection of data related to user attributes and text attributes through the use of Long Short-Term Memory (LSTM) networks. In order to achieve cross-time and cross-layer reverse error transmission, the LSTM unit will automatically save the error term used in each step. By maintaining a stable error term, the recursive network can learn multiple time steps. The core storage unit of LSTM is called Cell. A Cell consists of three gates (input gate, output gate, forget gate) and one Cell unit. The gate unit uses the Sigmoid function to generate the analog signal between. Ma et al. used RNN for rumor detection and obtained 0.89 and 0.95 accuracy on the corresponding data set [18].The CNN network consists of an input layer, an output layer and a hidden layer. It is the latest technology for computer vision tasks. It imitates the biological visual nervous system, shares weights in the network, effectively reduces model parameters, significantly reduces model complexity, and improves model robustness and generalization capabilities. Chen et al. solved the problem of the position and accuracy classification of tweets through the CNN network [19].In addition, the hybrid model of RNN and CNN has shown good performance in solving some complex problems. Wang et al. used CNN to encode text information and LSTM layer to encode metadata about the author of the text, thereby obtaining a hybrid model for false news detection [19]. Zubiaga and others successfully applied LSTM and hybrid LSTM-CNN models for rumor detection and achieved an accuracy of 0.82 [20].Both detection methods based on machine learning and detection methods based on deep learning can detect fake news content in a certain range. In terms of methods, deep learning puts more emphasis on active learning, which can make complex models show better performance.


## 5. Direction of Development

At present, the detection of false news and rumors has made good progress, and at the same time, it is also facing some challenges.First, the lack of universally accepted benchmark data sets must be addressed, especially data sets related to fake news and related social media posts. This is the basis for evaluating the effectiveness of each method and comparing it among them.In terms of detection technology, they tend to use supervised classification methods to detect false news and rumors. In fact, deep learning technology has achieved the desired results, and the latest methods are dedicated to the development of this framework to some extent. The hybrid model is an important development direction. It can simultaneously model different aspects of fake news.From a model point of view, the use of semisupervised or unsupervised models will be an important research direction. Their main advantage is that they can learn from unlabeled data, thereby reducing the time cost and accuracy of labeled data.


## 6. Conclusion

In this article, an overview of the latest technologies and methods for detecting false information problems is provided. The main contents are: provide clear definitions and distinctions for different subproblems such as fake news and rumors; explain the different functions based on content and context that can be used to detect fake news and rumors; outline the implementation of fake news and rumors detection and classification Technology; emphasizes how to implement supervised machine learning technology; finally, provides some insights into possible future trends in the research field.From a social and research perspective, it is necessary to carry out further research on news detection technology, because these studies will surely play a vital role in the future digital life.



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*Source: 8787958-2022-04-22.xml*