# Modeling Social Network of Professional Sports Athletes Based on Machine Learning Algorithms

Authors: Ying Ma
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

With the progress of society and the development of science, the competition of competitive sports has become extremely fierce, and the talents of competitive sports have gradually shown their strong vitality. Elite athletes are an important group for the development of China’s competitive sports industry. To continue to progress and develop, it is inseparable from the careful training of elite athletes. In order to closely integrate the needs of China’s socialist modernization and the training of elite athletes, sports schools that focus on cultivating elite athletes need to strengthen their sense of urgency and mission. It is necessary to meet new challenges at the height of social development and constantly optimize the construction of the curriculum system and the reform of the teaching operation mechanism. The discussion and research of the current elite athlete’s talent training model has become a real problem faced by sports colleges, education departments, and sports departments. This paper conducts an experiment on the modeling of the sports athlete’s social network based on machine learning, and the experimental data are shown as follows: among the athlete social network pictures optimized by machine learning, there are 1,200 male athletes, accounting for 48%, and 1,000 female athletes, accounting for 40%. Before the optimization, male athletes accounted for 70.58% and female athletes accounted for 16.3%, which was a decrease of 22.58% for male athletes and an increase of 23.7% for female athletes. From the above data, it can be seen that after the research on the social network of sports athletes through machine learning, it has a positive effect on the development of the social network of athletes.


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## Body

## 1. Introduction

With the progress of society, the competition for competitive sports talents in various countries has become more and more fierce, and the comprehensive strength is enough to prove the strength of a country. China broke through zero gold medals at the 1984 Los Angeles Olympics and won 32 gold medals at the 2004 Athens Olympics, just three golds behind the United States, ranking second in the world. China created a brilliant scene in the 2008 Beijing Olympic Games, winning 51 gold medals and ranking first in the world. This is enough to show that the development of competitive sports is inseparable from the cultivation of outstanding athletes, and even more inseparable from the cultivation of high-level technical teachers, senior sports administrators, and high-quality sports scientific researchers. Therefore, people must stand at the height of the development of the times and seize the opportunity. In 2010, China’s Ministry of Finance, Sports Department, Ministry of Education, Ministry of Human Resources and Social Security jointly issued guidelines to further strengthen cultural education and protection of athletes. The main purpose is to “strengthen the cultural foundation of elite athletes, improve, and implement various incentives and safeguards and establish and improve the social support system for athletes’ careers, so as to achieve the goal of athletes’ transformation.” The newly proposed guidance has greatly increased the sense of responsibility and urgency of the sports colleges. It has important strategic significance and historical mission to further optimize and reform the talent training mode of outstanding athletes in sports colleges.In the current period, China is in the process of transforming from the planned economic system that has been followed for many years to a new market economic system. It has to reexamine and think about the training mode and path of Chinese athletes. The cultivation of outstanding athletes is the key to the development of competitive sports. At this stage, the competition of various countries in competitive sports is fundamentally the competition of talent training. The national system of competitive sports led by the Chinese government has made Chinese competitive sports achieve brilliant achievements in a short period of time, and it has obvious characteristics of the planned economic system. Under this talent training model, there have been a series of problems such as unbalanced development of reserve talents, difficult survival of retired athletes, and unreasonable project development.This paper conducts research on the modeling of the social network of sports players based on machine learning. Its data show a total of 2,500 pictures of social network athletes surveyed after the machine learning method was improved. There are 2,300 pieces of description objects, accounting for 92%; 100 pieces of coaches, accounting for 4%; 50 pieces of spectators, accounting for 2%; and 50 pieces of advertisements, accounting for 2%. It can be seen from the above data that it is of great significance to promote the development of the current athlete’s social network after the research experiment of the sports athlete’s social network through machine learning.


## 2. Related Work

This paper studies some techniques for modeling the social network of sports players, which can be fully applied to the research in this field. Kang aimed to observe the use of SNS by middle school, high school, and college athletes. From these observations, he empirically examines student-athletes’ athlete, coach interaction, empathic experience, and relationship satisfaction with SNS [1]. Fransen et al. examined the relationship between the perceived quality of athlete leadership and the effectiveness of elite sports teams [2]. Mitchell et al. aimed to explore the interaction and impact of tweets following an athlete’s diagnosis of an eating disorder [3]. Hatteberg investigated the perceived utility of support received from significant others, similar others, and individuals with similar experiences through interviews with college athletes [4]. Mclaren and Spink suggested that there may be a link between the degree to which athletes exchange information (at the individual and team levels) and team performance outcomes (i.e., wins or losses) [5]. Sanderson and Cassilo explored the role of social support in the life experience of athletes with concussion injuries [6]. These methods provide some references for our research, but due to the short time and small sample size of the relevant research, they have not been recognized by the public.Based on machine learning, we reviewed the following relevant materials to optimize the research on social network modeling of sports athletes. Gastegger et al. believed that machine learning has become an invaluable tool in many research fields [7]. Goodfellow et al. argued that advances in deep learning have led to the widespread application of deep neural networks to a range of important machine learning problems [8]. Cai et al. discussed several commonly used evaluation methods for feature selection, and then surveys supervised, unsupervised, and semisupervised feature selection methods that are widely used in machine learning problems such as classification and clustering [9]. Sacha et al. presented five open research challenges at the intersection of ML and visualization research whose solutions should lead to more efficient data analysis [10]. Thrane et al. gave a brief overview of various machine learning methods and their applications in optical communication [11]. These approaches provide ample literature support for our study of machine-learned modeling of sports players’ social networks.


## 3. Overview of Machine Learning and Athlete Social Networks

With the shift of China’s sports strategic focus, the country has begun to balance the overall development of mass sports, competitive sports, and school sports. The lag of the traditional talent training concept and model of competitive sports has begun to appear, and it is difficult to adapt to the requirements of the development of the current era. Therefore, this paper addresses this issue by investigating machine learning for modeling the social network of sports players.


### 3.1. Overview of Machine Learning

Machine learning is a core research problem in the field of artificial intelligence, which is widely used in many fields, such as pattern recognition and automatic reasoning. The research process of machine learning can be summarized into the following points: first, understanding the mechanisms of human learning, such as physiological and cognitive insights, and building computational or cognitive models of the human learning process. Second, develop learning theories and methods on this basis, study general learning algorithms, and conduct theoretical analysis. Finally, task-based learning systems with concrete applications are developed.A machine learning system consists of a training component, a knowledge base, and an executive component. The workflow of the system is divided into two main stages. In the first stage, the external environment provides some information to the learning part of the system, and the learning part uses this information to expand and modify the knowledge base to improve the ability of the execution part of the system to complete tasks. In the second stage, the execution part performs the learning task based on the knowledge base and returns the information to the learning part at the same time. Since different application tasks have different external environments, knowledge bases, and execution parts, these two components completely determine the work of machine learning systems. The problem to be solved by the learning component is also completely determined by these two components [12].Based on the types of tasks described in machine learning systems, current research on machine learning applications mostly focuses on two categories, classification and problem-solving tasks. In particular, classification learning requires the system to be able to analyze unknown input patterns based on known classification knowledge to determine which class the input pattern belongs to. The purpose of classification learning is to perform classification tasks on the one hand and to find suitable classification rules in the process of classification learning on the other hand.The process of classification learning involves two kinds of data. One is the training data (also called training instances) used to build the classification model in the training phase, and the other is the test data (also called test instances) used in the prediction phase. Classification learning can be divided into acute learning and lazy learning according to the processing time of training instances. The training phase of acute learning uses the training instances to construct an ensemble model of the problem space, and the prediction phase uses this ensemble model to evaluate the class of test instances. During the training phase of lazy learning, training instances are simply recorded. In the prediction stage, a local model is built by finding the nearest neighbors of the test instance in the training instance, and then the category of the test instance is obtained [13]. Whether in active learning or lazy learning, data need to be preprocessed during the learning process.The most commonly used support vector machine (SVM) algorithm is used to solve regression classification problems in machine learning. It has some features that make it superior to other methods. Classification is mainly performed on linear partitioning. The goal of SVM classification is to construct an optimal classification hyperplane in the multivariate feature space and learn it. It causes the resulting classification surface to separate two samples with different feature sets. The linear classifier is shown in Figure1.Figure 1
Linear classifier.The simplest classification model for support vector machines is the maximum interval classifier. For linear classification problems, defining a linear function to maximize the geometric target interval leads to the following optimization problem:(1)min12w2subject toniwTmi+b−1>0,i=1,…,y.Here, using Lagrangian to determine the maximum value of the geometric interval, the dual form of the above optimization problem is obtained, namely:(2)maxL=∑i=1yαi−12∑i,j=1yαiαjninjmiTmj,subject to∑i=1yniαi=0,αi≥0i=1,…,y.Among them, the Lagrangian coefficient is the solution of the optimization problem [14].Since many real data are noisy, the feature space cannot be fundamentally partitioned. In order to achieve a balance between model performance and generality, free variables are introduced, that is, a small number of error points and outliers are allowed. The optimization problem is finally transformed into:(3)min12w2+C∑i=1yγisubject toniwTmi+b≥1−γiγi>0,i=1,…,y.C is the penalty coefficient. Likewise, the above optimization problem is transformed into a binary problem using Lagrange multipliers, namely:(4)maxL=∑i=1yαi−12∑i,j−1yαiαjninjmiTmjsubject to∑i=1yniαi=0,0<αi<C,i=1,…,y.The above problem is solved by solving the coefficientαi to determine the optimal classification level. Today, reference vector machines are used in practical applications to deal with various problems and have achieved good results. Practical applications include image processing, text classification, signal processing, biological data mining, and genetic information discovery.Artificial neural networks are a multidisciplinary intersection of brain research, neuropsychology, and computer science. Its purpose is to enable computers to simulate the thinking of the human brain by learning, so that machines can have artificial intelligence. Various neural network models are derived from different topologies of connections or learning paths between neurons [15]. The three-layer BP neural network diagram is shown in Figure 2 below:Figure 2
Three-layer BP neural network diagram.BP neural network is a neural network with three or more layers, which consists of input layer, indirect layer, and output layer. The BP neural network algorithm is the most commonly used neural network at present, which is composed of two structures: forward propagation and backward error correction. The output value of the hidden layer unit of the network is(5)hj=f∑i=0I−1vijmi−θj.The output value of each unit of the output layer is(6)nk=f∑i=0J−1wjkhj−θk.In the backpropagation stage, the activation function of each layer of the BP neural network should use a sigmoid function, namely:(7)fnet=11+e−net.The BP algorithm transfers information through the forward process, updates the weights through the backward process, and repeats until the error falls below a threshold or reaches a predetermined number of iterations [16].The AdaBoost algorithm is an iterative classification algorithm that uses an adaptive approach. The basic idea is that samples misclassified by the base classifier in the previous step are highly valued, and the weighted samples are used to train the next classifier. At each step, a new classifier is added, and the iteration stops when the algorithm reaches a given threshold. Its basic classifier is(8)Gxm:m⟶−1,+1.Using the above formula, calculate the classification error rate for a set of training samples as follows:(9)ex=PGxmi≠ni=∑i=1YwxiIGxmi≠ni.The above formula shows that the error rate of Gx(m) on the set of previous training instances is the sum of the weights of the instances misclassified by Gx(m). The contribution of the final classifier is(10)ax=12log1−exex.Whenex ≤ 1/2, ax ≥ 0, and ax will become larger as ex becomes smaller. Combining the weak classifiers gives(11)fm=∑x=1XaxGxm.And then get the final classifier:(12)Gm=signfm=sign∑x=1XaxGxm.The above is the main idea of AdaBoost algorithm to deal with the problem [17].Algorithms consisting of a set of multicomponent classifiers and combining rules are called comprehensive learning algorithms or combined classifiers. In ensemble learning, multiple (usually homogeneous) component classifiers are used to jointly solve a problem. Multiple learners used for integrated learning are called individual learners. After training with multiple individual learners, ensemble learning uses some rules to integrate the individual learners, resulting in better training results than individual classifiers. Many experimental studies have shown that ensemble learning algorithms generally have higher classification accuracy than single-decision algorithms. The learning and training of the classifier is to find an optimal weight, which is an optimization problem with many local optima. The block diagram of the integration algorithm is shown in Figure3:Figure 3
Block diagram of the integration algorithm.Two factors determine the high performance of the ensemble learning algorithm: one is that each classifier in the ensemble has a sufficiently high classification accuracy; the other is that there are differences between the classifiers in the ensemble. Classification accuracy can be obtained by adequately training individual classifiers. The differences are mainly reflected in two aspects: training samples and training algorithms. Differences can be achieved by using different training samples or using different training algorithms for which the classifier can adopt different models [18].


### 3.2. Overview of Athlete Social Networks

From the 1950s to the 1970s after the founding of New China, competitive sports was one of the few fields that could participate in international competition and make continuous breakthroughs. Athletes who achieve great results are widely reported by the media. Their image of hard work, perseverance, and courage to challenge is a typical sign of the Chinese people in that era. The outlook on life and values conveyed by these images have also educated and influenced countless Chinese people, and at the same time, they have enhanced national pride and cohesion.Sport is an important aspect of portraying a country’s image, and the content and image of sport is a channel for all media. In 2008, the successful hosting of the Beijing Olympic Games was an important milestone in the development of sports in China. The world has become familiar with Chinese sports, and Chinese sports have also taken this opportunity to enter a new stage of development. For sports, 2008 was a historic year. In terms of sports communication, the provision of information has become more diversified, and the popularity of the Internet has led to the rapid development of online sports media [19]. The development of the Internet reflects the profound influence of the society on the Internet. At this time, the positioning of traditional Internet coverage of sports information began to receive research attention in 2008.Between 2008 and 2017, sports were on the rise in China. Continued attention to public policy and the public’s in-depth knowledge and awareness of sports have created a high demand for sports news in both volume and depth. This has resulted in a diverse representation of athletes in the network. In the past, athletes were presented to the public in general, unsentimental terms like ”athletic ability,” “performance,” and “mistakes.” Now, both the network and the athlete utilize different platforms to present the passive and active image of the athlete. With the diversification of the way audiences are exposed to the media, their demand for the image of the athlete is also increasing, which further contributes to the development of the image of the athlete.In view of these changes, social network coverage of athlete images is not only diverse in content and form but also provides audiences with a broader evaluation of athlete images. Especially after the 2008 Olympic Games, the changes in the image of athletes by social networks in the decade 2008–2017 reflect the public awareness and attitude towards athletes, as well as the entire society’s perception of the existing image of athletes. It also reflects the development achievements of Chinese sports in the past ten years.An athlete’s social network profile is a more specific type. It refers to the image of athletes created and disseminated by online platforms. In short, it can be seen as an aspect of the image of the athlete presented by the network. Athletes’ social network profiles can be attributed in different ways according to different distinguishing criteria. As far as the main body of the image is concerned, the media image of an athlete is a kind of social network image. The media image of an athlete is a part of an athlete’s image in terms of representation. The social network image of athletes is one of the most important means for the public to recognize athletes, and it is also an important part of the national image [20].Athletes are the main actors of competitive sports and therefore become the protagonists of sports reports on social networks. Athletes’ experiences, words and deeds, achievements, and so on, provide the media with reporting materials and content. The media selects and processes materials related to athletes, forms reports on athletes, and disseminates them to the society. This dissemination process will bring positive or negative effects to athletes and their related groups, institutions, and even this field. Therefore, the shaping and dissemination of athlete images in social networks essentially reflects the interaction and association between sports and social networks. Once the athlete image created by the social network is spread, it will inevitably have a social impact and realize its social function.It is generally believed that society refers to a stable social relationship between individuals within a certain range. The concept of society was first proposed by British anthropologists in 1940 and systematically explored in 1954 in a study of the class system in a Norwegian fishing village. In 1934, American social psychologists established the study of analytical sociometrics. Later, more and more researchers from sociology, anthropology, mathematics, statistics, and probability theory began to develop the concept of “social structure.”Since the 1960s, social network research has been widely used in social psychology, marketing, social mobility, political sociology, and gerontology. After decades of research, many social network theories have emerged. The representative theories mainly include the weak tie power hypothesis, the concept of infiltration, the social resource theory, the strong tie power hypothesis, and the structural hole theory.The study of the overall network is based on social network theory and analyzes the impact of the network as part of the social structure on the individual. Tools such as graph theory can be used as direct research objects rather than individuals in the network. Combined with other methods, it is based on the analysis of the properties of the network as a whole, that is, the general properties of the individuals affected by the network as a whole. This general property explains that individuals are constantly influenced by other individuals in the network as a whole and finally realize the establishment of social environment and social structure. Therefore, it can be considered that the overall network interaction is a dynamic research method, which examines the cognition and behavior of individuals under the influence of social network factors, and reflects the process of social network exerting its influence.Social network analysis methods are by far the most powerful tools for structural research. Social network analysis methods are widely used, but in general, the research focus is on individual networks, and there are few studies on the overall network. An important part of social network research is social support network.As a special social activity of human beings, sports are an important part of social network reporting and dissemination. With the rapid development of the two fields of sports and the Internet, the relationship between sports and the Internet has received more and more attention in the study of sports sociology. Almost all research papers on the sociology of sports have special chapters on the relationship between sports and the Internet.Researchers from all over the world have recognized that sports and the Internet have mutual effects and influences. As US researchers have pointed out, sports and social networks are a reciprocal relationship, they influence each other and their development depends on the popularity and success of the other. Swedish scholars analyzed and summarized the history, development, changes, and manifestations of different types of social network sports reports and believed that the rise of the Internet is the most important development force of modern sports. The relationship between sports, Internet, and advertising is symbiotic and interdependent. The symbiotic relationship between sports and the Internet and their interactions have a pervasive impact on society. As this relationship strengthens, so does the depth and breadth of its impact on society.Athletes are important participants and core actors in competitive sports, and they are also the objects of network attention, shaping, and dissemination. So, athlete actually contains two meanings. One is the athletes who act as the main body of competitive sports, which can be called real athletes; the other is the athletes who are shaped, presented, and disseminated by the network, which can be called network athletes. Real athletes undertake and practice competitive sports behavior, and their core function is to participate in sports competitions. After the process of athletes participating in competitive sports practice is selectively processed, presented, and disseminated by the network, it will have an impact on society. Therefore, the so-called social influence of athletes is essentially the social influence of online athletes.For the social impact of athletes, researchers represented by scholars from European and American countries have provided rich research results. It mainly focuses on two aspects: one is the impact on the social and spiritual aspects such as ideology, social culture, and values; the other is the impact on the social and material life aspects such as social economy and business activities.The image of the athlete is part of the representation of the national image. High-profile sportsmanship and positive image of athletes can be accepted by people of all countries beyond political, ethnic, and other factors under the increasing media attention. Therefore, the image value of athletes, together with the audience and market orientation, has gradually become the messenger of national cultural exchanges, demonstrating the national cultural strength.In a market economy, the image of the athlete is another area of interest for business development. Unlike entertainment stars, the economic benefits of nationally renowned athletes are more valuable to the commercial market and more attractive than other types of public figures. The popularity of sports is evident in many areas of business activity today, as are its endorsement results. Of course, it should not be overlooked that in this series of business activities, the communication and information exchange between companies and athletes, sports teams, and athletes are also issues that need to reach consensus now.


## 3.1. Overview of Machine Learning

Machine learning is a core research problem in the field of artificial intelligence, which is widely used in many fields, such as pattern recognition and automatic reasoning. The research process of machine learning can be summarized into the following points: first, understanding the mechanisms of human learning, such as physiological and cognitive insights, and building computational or cognitive models of the human learning process. Second, develop learning theories and methods on this basis, study general learning algorithms, and conduct theoretical analysis. Finally, task-based learning systems with concrete applications are developed.A machine learning system consists of a training component, a knowledge base, and an executive component. The workflow of the system is divided into two main stages. In the first stage, the external environment provides some information to the learning part of the system, and the learning part uses this information to expand and modify the knowledge base to improve the ability of the execution part of the system to complete tasks. In the second stage, the execution part performs the learning task based on the knowledge base and returns the information to the learning part at the same time. Since different application tasks have different external environments, knowledge bases, and execution parts, these two components completely determine the work of machine learning systems. The problem to be solved by the learning component is also completely determined by these two components [12].Based on the types of tasks described in machine learning systems, current research on machine learning applications mostly focuses on two categories, classification and problem-solving tasks. In particular, classification learning requires the system to be able to analyze unknown input patterns based on known classification knowledge to determine which class the input pattern belongs to. The purpose of classification learning is to perform classification tasks on the one hand and to find suitable classification rules in the process of classification learning on the other hand.The process of classification learning involves two kinds of data. One is the training data (also called training instances) used to build the classification model in the training phase, and the other is the test data (also called test instances) used in the prediction phase. Classification learning can be divided into acute learning and lazy learning according to the processing time of training instances. The training phase of acute learning uses the training instances to construct an ensemble model of the problem space, and the prediction phase uses this ensemble model to evaluate the class of test instances. During the training phase of lazy learning, training instances are simply recorded. In the prediction stage, a local model is built by finding the nearest neighbors of the test instance in the training instance, and then the category of the test instance is obtained [13]. Whether in active learning or lazy learning, data need to be preprocessed during the learning process.The most commonly used support vector machine (SVM) algorithm is used to solve regression classification problems in machine learning. It has some features that make it superior to other methods. Classification is mainly performed on linear partitioning. The goal of SVM classification is to construct an optimal classification hyperplane in the multivariate feature space and learn it. It causes the resulting classification surface to separate two samples with different feature sets. The linear classifier is shown in Figure1.Figure 1
Linear classifier.The simplest classification model for support vector machines is the maximum interval classifier. For linear classification problems, defining a linear function to maximize the geometric target interval leads to the following optimization problem:(1)min12w2subject toniwTmi+b−1>0,i=1,…,y.Here, using Lagrangian to determine the maximum value of the geometric interval, the dual form of the above optimization problem is obtained, namely:(2)maxL=∑i=1yαi−12∑i,j=1yαiαjninjmiTmj,subject to∑i=1yniαi=0,αi≥0i=1,…,y.Among them, the Lagrangian coefficient is the solution of the optimization problem [14].Since many real data are noisy, the feature space cannot be fundamentally partitioned. In order to achieve a balance between model performance and generality, free variables are introduced, that is, a small number of error points and outliers are allowed. The optimization problem is finally transformed into:(3)min12w2+C∑i=1yγisubject toniwTmi+b≥1−γiγi>0,i=1,…,y.C is the penalty coefficient. Likewise, the above optimization problem is transformed into a binary problem using Lagrange multipliers, namely:(4)maxL=∑i=1yαi−12∑i,j−1yαiαjninjmiTmjsubject to∑i=1yniαi=0,0<αi<C,i=1,…,y.The above problem is solved by solving the coefficientαi to determine the optimal classification level. Today, reference vector machines are used in practical applications to deal with various problems and have achieved good results. Practical applications include image processing, text classification, signal processing, biological data mining, and genetic information discovery.Artificial neural networks are a multidisciplinary intersection of brain research, neuropsychology, and computer science. Its purpose is to enable computers to simulate the thinking of the human brain by learning, so that machines can have artificial intelligence. Various neural network models are derived from different topologies of connections or learning paths between neurons [15]. The three-layer BP neural network diagram is shown in Figure 2 below:Figure 2
Three-layer BP neural network diagram.BP neural network is a neural network with three or more layers, which consists of input layer, indirect layer, and output layer. The BP neural network algorithm is the most commonly used neural network at present, which is composed of two structures: forward propagation and backward error correction. The output value of the hidden layer unit of the network is(5)hj=f∑i=0I−1vijmi−θj.The output value of each unit of the output layer is(6)nk=f∑i=0J−1wjkhj−θk.In the backpropagation stage, the activation function of each layer of the BP neural network should use a sigmoid function, namely:(7)fnet=11+e−net.The BP algorithm transfers information through the forward process, updates the weights through the backward process, and repeats until the error falls below a threshold or reaches a predetermined number of iterations [16].The AdaBoost algorithm is an iterative classification algorithm that uses an adaptive approach. The basic idea is that samples misclassified by the base classifier in the previous step are highly valued, and the weighted samples are used to train the next classifier. At each step, a new classifier is added, and the iteration stops when the algorithm reaches a given threshold. Its basic classifier is(8)Gxm:m⟶−1,+1.Using the above formula, calculate the classification error rate for a set of training samples as follows:(9)ex=PGxmi≠ni=∑i=1YwxiIGxmi≠ni.The above formula shows that the error rate of Gx(m) on the set of previous training instances is the sum of the weights of the instances misclassified by Gx(m). The contribution of the final classifier is(10)ax=12log1−exex.Whenex ≤ 1/2, ax ≥ 0, and ax will become larger as ex becomes smaller. Combining the weak classifiers gives(11)fm=∑x=1XaxGxm.And then get the final classifier:(12)Gm=signfm=sign∑x=1XaxGxm.The above is the main idea of AdaBoost algorithm to deal with the problem [17].Algorithms consisting of a set of multicomponent classifiers and combining rules are called comprehensive learning algorithms or combined classifiers. In ensemble learning, multiple (usually homogeneous) component classifiers are used to jointly solve a problem. Multiple learners used for integrated learning are called individual learners. After training with multiple individual learners, ensemble learning uses some rules to integrate the individual learners, resulting in better training results than individual classifiers. Many experimental studies have shown that ensemble learning algorithms generally have higher classification accuracy than single-decision algorithms. The learning and training of the classifier is to find an optimal weight, which is an optimization problem with many local optima. The block diagram of the integration algorithm is shown in Figure3:Figure 3
Block diagram of the integration algorithm.Two factors determine the high performance of the ensemble learning algorithm: one is that each classifier in the ensemble has a sufficiently high classification accuracy; the other is that there are differences between the classifiers in the ensemble. Classification accuracy can be obtained by adequately training individual classifiers. The differences are mainly reflected in two aspects: training samples and training algorithms. Differences can be achieved by using different training samples or using different training algorithms for which the classifier can adopt different models [18].


## 3.2. Overview of Athlete Social Networks

From the 1950s to the 1970s after the founding of New China, competitive sports was one of the few fields that could participate in international competition and make continuous breakthroughs. Athletes who achieve great results are widely reported by the media. Their image of hard work, perseverance, and courage to challenge is a typical sign of the Chinese people in that era. The outlook on life and values conveyed by these images have also educated and influenced countless Chinese people, and at the same time, they have enhanced national pride and cohesion.Sport is an important aspect of portraying a country’s image, and the content and image of sport is a channel for all media. In 2008, the successful hosting of the Beijing Olympic Games was an important milestone in the development of sports in China. The world has become familiar with Chinese sports, and Chinese sports have also taken this opportunity to enter a new stage of development. For sports, 2008 was a historic year. In terms of sports communication, the provision of information has become more diversified, and the popularity of the Internet has led to the rapid development of online sports media [19]. The development of the Internet reflects the profound influence of the society on the Internet. At this time, the positioning of traditional Internet coverage of sports information began to receive research attention in 2008.Between 2008 and 2017, sports were on the rise in China. Continued attention to public policy and the public’s in-depth knowledge and awareness of sports have created a high demand for sports news in both volume and depth. This has resulted in a diverse representation of athletes in the network. In the past, athletes were presented to the public in general, unsentimental terms like ”athletic ability,” “performance,” and “mistakes.” Now, both the network and the athlete utilize different platforms to present the passive and active image of the athlete. With the diversification of the way audiences are exposed to the media, their demand for the image of the athlete is also increasing, which further contributes to the development of the image of the athlete.In view of these changes, social network coverage of athlete images is not only diverse in content and form but also provides audiences with a broader evaluation of athlete images. Especially after the 2008 Olympic Games, the changes in the image of athletes by social networks in the decade 2008–2017 reflect the public awareness and attitude towards athletes, as well as the entire society’s perception of the existing image of athletes. It also reflects the development achievements of Chinese sports in the past ten years.An athlete’s social network profile is a more specific type. It refers to the image of athletes created and disseminated by online platforms. In short, it can be seen as an aspect of the image of the athlete presented by the network. Athletes’ social network profiles can be attributed in different ways according to different distinguishing criteria. As far as the main body of the image is concerned, the media image of an athlete is a kind of social network image. The media image of an athlete is a part of an athlete’s image in terms of representation. The social network image of athletes is one of the most important means for the public to recognize athletes, and it is also an important part of the national image [20].Athletes are the main actors of competitive sports and therefore become the protagonists of sports reports on social networks. Athletes’ experiences, words and deeds, achievements, and so on, provide the media with reporting materials and content. The media selects and processes materials related to athletes, forms reports on athletes, and disseminates them to the society. This dissemination process will bring positive or negative effects to athletes and their related groups, institutions, and even this field. Therefore, the shaping and dissemination of athlete images in social networks essentially reflects the interaction and association between sports and social networks. Once the athlete image created by the social network is spread, it will inevitably have a social impact and realize its social function.It is generally believed that society refers to a stable social relationship between individuals within a certain range. The concept of society was first proposed by British anthropologists in 1940 and systematically explored in 1954 in a study of the class system in a Norwegian fishing village. In 1934, American social psychologists established the study of analytical sociometrics. Later, more and more researchers from sociology, anthropology, mathematics, statistics, and probability theory began to develop the concept of “social structure.”Since the 1960s, social network research has been widely used in social psychology, marketing, social mobility, political sociology, and gerontology. After decades of research, many social network theories have emerged. The representative theories mainly include the weak tie power hypothesis, the concept of infiltration, the social resource theory, the strong tie power hypothesis, and the structural hole theory.The study of the overall network is based on social network theory and analyzes the impact of the network as part of the social structure on the individual. Tools such as graph theory can be used as direct research objects rather than individuals in the network. Combined with other methods, it is based on the analysis of the properties of the network as a whole, that is, the general properties of the individuals affected by the network as a whole. This general property explains that individuals are constantly influenced by other individuals in the network as a whole and finally realize the establishment of social environment and social structure. Therefore, it can be considered that the overall network interaction is a dynamic research method, which examines the cognition and behavior of individuals under the influence of social network factors, and reflects the process of social network exerting its influence.Social network analysis methods are by far the most powerful tools for structural research. Social network analysis methods are widely used, but in general, the research focus is on individual networks, and there are few studies on the overall network. An important part of social network research is social support network.As a special social activity of human beings, sports are an important part of social network reporting and dissemination. With the rapid development of the two fields of sports and the Internet, the relationship between sports and the Internet has received more and more attention in the study of sports sociology. Almost all research papers on the sociology of sports have special chapters on the relationship between sports and the Internet.Researchers from all over the world have recognized that sports and the Internet have mutual effects and influences. As US researchers have pointed out, sports and social networks are a reciprocal relationship, they influence each other and their development depends on the popularity and success of the other. Swedish scholars analyzed and summarized the history, development, changes, and manifestations of different types of social network sports reports and believed that the rise of the Internet is the most important development force of modern sports. The relationship between sports, Internet, and advertising is symbiotic and interdependent. The symbiotic relationship between sports and the Internet and their interactions have a pervasive impact on society. As this relationship strengthens, so does the depth and breadth of its impact on society.Athletes are important participants and core actors in competitive sports, and they are also the objects of network attention, shaping, and dissemination. So, athlete actually contains two meanings. One is the athletes who act as the main body of competitive sports, which can be called real athletes; the other is the athletes who are shaped, presented, and disseminated by the network, which can be called network athletes. Real athletes undertake and practice competitive sports behavior, and their core function is to participate in sports competitions. After the process of athletes participating in competitive sports practice is selectively processed, presented, and disseminated by the network, it will have an impact on society. Therefore, the so-called social influence of athletes is essentially the social influence of online athletes.For the social impact of athletes, researchers represented by scholars from European and American countries have provided rich research results. It mainly focuses on two aspects: one is the impact on the social and spiritual aspects such as ideology, social culture, and values; the other is the impact on the social and material life aspects such as social economy and business activities.The image of the athlete is part of the representation of the national image. High-profile sportsmanship and positive image of athletes can be accepted by people of all countries beyond political, ethnic, and other factors under the increasing media attention. Therefore, the image value of athletes, together with the audience and market orientation, has gradually become the messenger of national cultural exchanges, demonstrating the national cultural strength.In a market economy, the image of the athlete is another area of interest for business development. Unlike entertainment stars, the economic benefits of nationally renowned athletes are more valuable to the commercial market and more attractive than other types of public figures. The popularity of sports is evident in many areas of business activity today, as are its endorsement results. Of course, it should not be overlooked that in this series of business activities, the communication and information exchange between companies and athletes, sports teams, and athletes are also issues that need to reach consensus now.


## 4. Optimization of Social Network for Sports Players

### 4.1. Social Network Image of Sports Athletes

This section adopts the sampling survey method. In order to better analyze the image of sports players in the social network, 60 articles from 2011 to 2013 in newspapers and several articles from 2008 to 2014 in related sports websites were randomly selected. Through careful reading and classification of pictures and words, the social network image of sports athletes is analyzed. The number of picture description objects in the social network is shown in Figure4:Figure 4
The number of picture description objects in social networks.It can be seen from Figure4 that the description objects of pictures in the social network include athletes, coaches, audiences, and advertisements. Among these description objects, athletes have the most pictures, reaching 2140, accounting for 85.9%. The description of the audience is the least, only reaching 48, accounting for 1.3%, while other descriptions account for 3.6% and 9.2%, respectively. It can be seen that, in sports news photo reports, the reports on athletes are the most important part compared to other description objects. The number of athletes’ pictures used in social networks is shown in Figure 5:Figure 5
The number of athletes’ pictures used in social networks.It can be seen from Figure5 that among these pictures of athletes, most of them are pictures of various performances of athletes on the field; there are 1516 pictures, accounting for 72%. However, images of athletes off the field also accounted for 28% of the total images of athletes. These pictures mainly depict the private life of the athlete, including adverse events in the athlete’s life and training. Therefore, sports news is no longer limited to the performance of athletes on the field in the use of pictures of athletes but expanded to the performance of athletes in their private lives outside the field. The number of pictures of male and female athletes in the pictures in the social network is shown in Figure 6:Figure 6
Number of pictures of male and female athletes in pictures in social networks.It can be seen from Figure6 that the total number of pictures in the social network is 1249, of which 1748 are male athletes, accounting for 70.58% of the total number, and 392 are female athletes, accounting for 16.3%. Compared to the number of images of female athletes, the number of images of male athletes is more than 4 times that of female athletes. The number of pictures of male and female athletes in the front-page pictures in the social network is shown in Figure 7:Figure 7
Number of pictures of male and female athletes in front-page pictures in social networks.It can be seen from Figure7 that the total number of front-page pictures in the social network is 144, and there are 112 pictures of male athletes, accounting for 71.83%, and 32 pictures of female athletes, accounting for 28.17%. The picture of male athletes is more than twice that of female athletes. It can be seen that the use of pictures in sports news reports on athletes is mainly dominated by male athletes. The proportion of male and female athletes varies considerably, as is the case in the front-page image. Figure 8 shows the appearance of discourse-constructed athlete images in social networks:Figure 8
The appearance of discourse-constructed athlete images in social networks.As can be seen from Figure8, the probabilities of heroes, beasts, kings, gods, and bad boys appearing are 6.2%, 5%, 4%, 3.5%, and 3%, respectively, of which heroes have the highest probability of appearing.Through the analysis of the use of pictures in social networks, it can be seen that in the reports of athletes, the reports of male and female athletes on the Internet show an imbalance. Sports news focuses on male athletes, and there are far more pictures of male athletes than female athletes. At the same time, pictures of male athletes occupy a larger area on the front page than pictures of female athletes, and sports news reports favor male athletes more.


### 4.2. Machine Learning-Improved Social Network of Sports Players

The previous section raised the issue of the imbalanced coverage of male and female athletes in social networks. Therefore, this section adds machine learning methods to improve on the basis of the research in the previous part to solve this problem. After improving the social network image of sports through machine learning, the reports of related sports websites are extracted again. Since this section is to optimize the problem in the previous part, the same image analysis is performed as the previous part for comparison. After optimization based on the machine learning method, the number of image description objects in the social network is shown in Table1.Table 1
The number of studies on image description objects in social networks after machine learning method optimization.
ObjectQuantityProportion (%)Athletes230092Coach1004Audience502Advertisement502Total2500100As can be seen from Table1, there are a total of 2500 social network pictures investigated after the improved machine learning method. There are 2,300 pieces of description objects, accounting for 92%; 100 pieces of coaches, accounting for 4%; 50 pieces of spectators, accounting for 2%; and 50 pieces of advertisements, accounting for 2%. The number of pictures of male and female athletes in the optimized social network pictures is shown in Table 2.Table 2
Number of pictures of male and female athletes in social network pictures.
Sample imageQuantityProportion (%)Total2500100Male athlete120048Female athlete100040Other30012It can be seen from Table2 that among the social network pictures of athletes optimized by machine learning, there are 1200 pictures of male athletes, accounting for 48%, and 1000 pictures of female athletes, accounting for 40%. Before the optimization, male athletes accounted for 70.58% and female athletes accounted for 16.3%, which was a decrease of 22.58% for male athletes and an increase of 23.7% for female athletes. This shows that the use of machine learning methods to optimize the social network of athletes can play a huge role in improving the problems that occurred in the previous section. The number of pictures of male and female athletes in the improved front page social network pictures is shown in Table 3.Table 3
Number of pictures of male and female athletes in the improved front page social network pictures.
Sample imageQuantityProportion (%)Total150100Male athlete8053.4Female athlete7046.6It can be seen from Table3 that the number of pictures of male and female athletes in the optimized front page is 80 and 70, respectively, and their proportions are 53.4% and 46.6%, respectively. Compared with the unoptimized time, the proportion of male athletes decreased by 18.43%, while the proportion of female athletes increased by 18.43%. This shows that the gender distribution of sports athlete pictures on social network reports under the improvement of machine learning is gradually averaged, which can easily and quickly solve the problems raised in the previous section.


## 4.1. Social Network Image of Sports Athletes

This section adopts the sampling survey method. In order to better analyze the image of sports players in the social network, 60 articles from 2011 to 2013 in newspapers and several articles from 2008 to 2014 in related sports websites were randomly selected. Through careful reading and classification of pictures and words, the social network image of sports athletes is analyzed. The number of picture description objects in the social network is shown in Figure4:Figure 4
The number of picture description objects in social networks.It can be seen from Figure4 that the description objects of pictures in the social network include athletes, coaches, audiences, and advertisements. Among these description objects, athletes have the most pictures, reaching 2140, accounting for 85.9%. The description of the audience is the least, only reaching 48, accounting for 1.3%, while other descriptions account for 3.6% and 9.2%, respectively. It can be seen that, in sports news photo reports, the reports on athletes are the most important part compared to other description objects. The number of athletes’ pictures used in social networks is shown in Figure 5:Figure 5
The number of athletes’ pictures used in social networks.It can be seen from Figure5 that among these pictures of athletes, most of them are pictures of various performances of athletes on the field; there are 1516 pictures, accounting for 72%. However, images of athletes off the field also accounted for 28% of the total images of athletes. These pictures mainly depict the private life of the athlete, including adverse events in the athlete’s life and training. Therefore, sports news is no longer limited to the performance of athletes on the field in the use of pictures of athletes but expanded to the performance of athletes in their private lives outside the field. The number of pictures of male and female athletes in the pictures in the social network is shown in Figure 6:Figure 6
Number of pictures of male and female athletes in pictures in social networks.It can be seen from Figure6 that the total number of pictures in the social network is 1249, of which 1748 are male athletes, accounting for 70.58% of the total number, and 392 are female athletes, accounting for 16.3%. Compared to the number of images of female athletes, the number of images of male athletes is more than 4 times that of female athletes. The number of pictures of male and female athletes in the front-page pictures in the social network is shown in Figure 7:Figure 7
Number of pictures of male and female athletes in front-page pictures in social networks.It can be seen from Figure7 that the total number of front-page pictures in the social network is 144, and there are 112 pictures of male athletes, accounting for 71.83%, and 32 pictures of female athletes, accounting for 28.17%. The picture of male athletes is more than twice that of female athletes. It can be seen that the use of pictures in sports news reports on athletes is mainly dominated by male athletes. The proportion of male and female athletes varies considerably, as is the case in the front-page image. Figure 8 shows the appearance of discourse-constructed athlete images in social networks:Figure 8
The appearance of discourse-constructed athlete images in social networks.As can be seen from Figure8, the probabilities of heroes, beasts, kings, gods, and bad boys appearing are 6.2%, 5%, 4%, 3.5%, and 3%, respectively, of which heroes have the highest probability of appearing.Through the analysis of the use of pictures in social networks, it can be seen that in the reports of athletes, the reports of male and female athletes on the Internet show an imbalance. Sports news focuses on male athletes, and there are far more pictures of male athletes than female athletes. At the same time, pictures of male athletes occupy a larger area on the front page than pictures of female athletes, and sports news reports favor male athletes more.


## 4.2. Machine Learning-Improved Social Network of Sports Players

The previous section raised the issue of the imbalanced coverage of male and female athletes in social networks. Therefore, this section adds machine learning methods to improve on the basis of the research in the previous part to solve this problem. After improving the social network image of sports through machine learning, the reports of related sports websites are extracted again. Since this section is to optimize the problem in the previous part, the same image analysis is performed as the previous part for comparison. After optimization based on the machine learning method, the number of image description objects in the social network is shown in Table1.Table 1
The number of studies on image description objects in social networks after machine learning method optimization.
ObjectQuantityProportion (%)Athletes230092Coach1004Audience502Advertisement502Total2500100As can be seen from Table1, there are a total of 2500 social network pictures investigated after the improved machine learning method. There are 2,300 pieces of description objects, accounting for 92%; 100 pieces of coaches, accounting for 4%; 50 pieces of spectators, accounting for 2%; and 50 pieces of advertisements, accounting for 2%. The number of pictures of male and female athletes in the optimized social network pictures is shown in Table 2.Table 2
Number of pictures of male and female athletes in social network pictures.
Sample imageQuantityProportion (%)Total2500100Male athlete120048Female athlete100040Other30012It can be seen from Table2 that among the social network pictures of athletes optimized by machine learning, there are 1200 pictures of male athletes, accounting for 48%, and 1000 pictures of female athletes, accounting for 40%. Before the optimization, male athletes accounted for 70.58% and female athletes accounted for 16.3%, which was a decrease of 22.58% for male athletes and an increase of 23.7% for female athletes. This shows that the use of machine learning methods to optimize the social network of athletes can play a huge role in improving the problems that occurred in the previous section. The number of pictures of male and female athletes in the improved front page social network pictures is shown in Table 3.Table 3
Number of pictures of male and female athletes in the improved front page social network pictures.
Sample imageQuantityProportion (%)Total150100Male athlete8053.4Female athlete7046.6It can be seen from Table3 that the number of pictures of male and female athletes in the optimized front page is 80 and 70, respectively, and their proportions are 53.4% and 46.6%, respectively. Compared with the unoptimized time, the proportion of male athletes decreased by 18.43%, while the proportion of female athletes increased by 18.43%. This shows that the gender distribution of sports athlete pictures on social network reports under the improvement of machine learning is gradually averaged, which can easily and quickly solve the problems raised in the previous section.


## 5. Conclusion

With the reform of professional sports mechanism, the issue of social inclusion of athletes who have a direct impact on social stability and harmonious development has become more and more important. Professional athletes are the backbone of China’s competitive sports and the main driving force for the implementation of the “Olympic Art” strategy. With the development of professional sports in China, athletes will definitely get more attention in all aspects of society. The interaction between athletes and society is closer than ever. In this new environment, it has important theoretical value and practical significance to analyze athletes from the perspective of social network. This paper investigates the structure of professional athletes’ social networks through the study of machine learning. This paper puts forward practical suggestions for the modeling of sports athletes’ social network, which has certain theory and significance for accelerating the development of Chinese professional athletes.



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*Source: 6283618-2022-09-05.xml*