# Mobile App News English Communication Based on Machine Learning Algorithm

Authors: Jun Zhang
Journal: Mobile Information Systems
 (2022)

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## Abstract

With the rapid development of information technology, especially wireless Internet, the application of mobile terminals has gradually become an indispensable part of people’s life and provides new opportunities for higher education. At present, with the rise of the Internet, Chinese English online media have mushroomed and are facing fierce competition. In order to further test the current communication strategies of mobile app News English, this paper uses machine learning algorithms to capture the superficial and deep semantic features of words and sentences in English news and comprehensively uses existing methods to express semantic features. The surface and deep parts of English news are analyzed and classified by regression calculation. Then, in order to analyze the communication of mobile app English news, this paper uses random questionnaires and interviews to investigate students, combined with machine learning algorithm knowledge, analyzes the communication effect of Chinese English news mobile program on college students, and analyzes its influencing factors on the communication effect of news. It is hoped that the results of this study can clearly identify the advantages and disadvantages of English News Mobile Applications in the development of the current era and timely adjust strategic decisions. By studying machine learning algorithm and introducing it into the field of English news communication, this paper tests an English news communication mobile app and analyzes its effect on English news communication.


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## Body

## 1. Introduction

With the implementation of the reform and opening-up policy, China has gradually developed into a global media power in all aspects in the past three decades. In terms of television, radio, Internet, newspapers, and other media, both the development scale and the number of media are in the forefront of the world. But this does not mean that China has become a communication power. As the largest developing country in the world, China has less media and less influence than developed countries. China’s national image is still mostly depicted by Western media, and the export volume of media products is far lower than the import volume of Western media products. According to the data, the number of Chinese Internet users reached 989 million in December 2021. At this stage of development, any Internet user can connect to the Internet through various ways, such as mobile phones and tablets. Mobile terminals play a very important role in every aspect of people’s life. Mobile phones and other mobile devices have had a huge impact on public life. In order to meet the needs of the current mobile device Internet era, user sharing environment and competing for public attention, major media institutions continue to improve the attractiveness of media information to users. The development of smart mobile devices such as mobile phones and tablets makes it easier for users to retrieve information, and the introduction of machine learning algorithms makes the information acquisition scene smaller and fragmented.The progress of media technology, the popularity of intelligent devices, and the development of mobile applications are changing the mode of modern communication [1]. English online media in China has sprung up and joined the fierce competition in the English media industry. Due to the development trend of economic globalization and informatization of social life, English has become the main way of communication for international talents [2]. As we all know, college students are a powerful reserve force in China. They are in the stage of constantly improving their English learning level, and English news applications provide a convenient platform for their autonomous learning and also stimulate the development of Internet English education in China [3]. The communication power of English news mobile app can provide effective information to distance vocational education institutions, thus paving the way for the win-win cooperation between English news app and English learning app [4]. At the same time, carrying out such research is also an exploration and attempt of the development of English journalism and communication in China, which can provide good reference and data support for the teaching and development of international journalism in colleges and universities [5].


## 2. Related Work

Based on empirical investigation, the literature analyzes the communication effect and influencing factors of English media in China from both positive and negative aspects. The results show that the development role of English media in China is irreplaceable, and great progress has been made, but there are still some problems, such as convergence of positioning [6]. According to the literature, the single media management means, poor management ability, great cultural differences between the east and the west, and low media credibility are the main factors affecting the communication effect of Chinese English media [7]. The literature shows that the media literacy of news publishers in the 5G era mainly focuses on the overall analysis of the current media environment. People mainly focus on improving the symbiotic environment of the Internet, changing the communication mode, and changing the requirements of media literacy, and less on the specific editing process [8]. Literature interprets the essence of news from the perspective of technology intermediary. In addition to the two dimensions determined in objective academic facts (ontology) and text expression facts (epistemology or news practice theory), in the new technological environment, the presentation of technology intermediary law (intermediary facts) has had an unprecedented impact on the way news is reported [9]. From the perspective of communication, the communication report is indirect, which is manifested in some facts recorded by means of information technology or the real picture, real situation, or state of affairs processed by technology [10]. According to the literature, the contradictions of current news algorithms mainly focus on human subjectivity and algorithm objectivity, the public interest of the media and the profitability of the algorithm, as well as the diversity and closeness of society [11, 12]. Taking people’s network as an example, the literature discusses the system of people’s network push information from information collection to information processing to information storage and transmission, and then points out that mobile phone manufacturers actually have great influence in news publicity [13]. According to the literature, stereotype is a form of subjective prejudice that people have toward things. Based on this point, it is manifested as an independent consciousness. Although it can quickly establish a reference for people to recognize things, it will also prevent the public from receiving new information and updating old ideas to a certain extent. The literature emphasizes that the public has the freedom of consciousness, and the dissemination of information needs to respect the freedom of consciousness of the public; otherwise, it will be resisted and excluded [14]. This view overemphasizes the self-consciousness of the media information audience and ignores the psychological variability of the audience, thus ignoring the adjustment and guidance for the information carrier.According to the literature, mass communication may not be a necessary and sufficient factor to produce audience effect in general, but it is more likely to interact in different factors and play a role through these constraints and influences. The literature believes that the impact of news communication is two-fold [15]. The first is the impact of news reports on the audience and the consistency with the expected goal of communication. Generally speaking, the higher the degree of coincidence, the better the transmission effect. This kind of communication effect is the standard to measure the level of news business, indicating whether app has correctly used the way of news communication. Based on the general environment, the literature analyzes the challenges of the international situation and the development of international media to China’s English news media in the new century. It also analyzes the problems existing in Chinese English media at present and then describes the development trend since the 21st century—more diversified media forms, updated communication concepts, more advanced reporting methods, and more reasonable talent structure [16].This paper expounds the three major trends of the future development of the media: the central government is the main, and the local government is the auxiliary; integrate external communication with the market; English media will return to journalistic standards. The literature takes online forums as the research object, analyzes the communication impact of various types of posts, and predicts the development trend of topics [17]. In the discussion of the impact of forum communication, the impact of communication is specifically divided into meeting the public’s right to know and spiritual and emotional needs; publicity hot spots to attract the attention of traditional media; carry out reasonable opinion expression and exchange, as well as timely interactive feedback; and realize group behavior to discuss the micro-effect [18, 19].


## 3. Theoretical Basis and Application of Machine Learning Algorithm

### 3.1. Support Vector Machine

Support vector machine is a classification algorithm using supervised learning training. SVM is determined by important training samples, such as support vector. Therefore, if all classifications are correct, the SVM classification problem can be described as a finite problem of optimizing linear classification, as shown in(1)minω,b12|ω|2(2)s.t.yiω⋅xi+b−1≥0.For each inequality constraint, Lagrange multipliers AI ≥ 0,i = 1, 2, N; the Lagrangian function is constructed as follows:(3)Lω,b,α=12|ω|2−∑i=1Naiyiω⋅xi+b−1.According to Lagrange duality, the original finite optimization problem can be equivalent to the dipole minimax problem, as shown in(4)maxαminω,bLω,b,α.Next,l(ω, b, A) after deriving, we get(5)∂L∂ω=ω−∑i−1Nαiyixi,∂L∂b=∑i−1Nαiyi.After it is brought into the Lagrange function, the solution of the SVM problem can be equivalent to the problem of optimizing the following functions, and the solution learning algorithm can be completed by the following operations:(6)minα12∑i=1N∑j=1Nαiαjyiyjxi⋅xj−∑i=1Nαi,s.t.∑i=1Naiyi=0,αi≥0,i=1,2…,N.


### 3.2. Bayes Theorem

The concept of conditional probability is particularly important in Bayesian theorem. Conditional probability means that there are two eventsA and B, and the probability of event B is greater than 0. Then, in the case of event B, the probability of event A is called the conditional probability of A when B occurs, that is, P (a|b). For event B, there are(7)PB=∑i−1∞PAiPA∣Bi.Naive Bayes formula is shown in(8)PBi∣A=PBiPA∣Bi∑j=1nPBjPA∣Bj.Bayesian formula is used to classify the dimensional components of the data set, assuming that the components of the training set are independent of each other. But in real life, this assumption is almost impossible. There may be an inseparable relationship between any features. If there is a strong potential relationship between features, the effect of naive Bayesian classification will be affected.


### 3.3. Text Classification of English News

Firstly, the English news text data are encoded into the template, and the granularity text data are obtained by using the template. Then, the semantic vectorization of news text topic is carried out.The sentence coding method uses the average value of Bert layer coding from 9 to 12 to represent the wordwi, and the formulas are shown in(9)wi=BERTrepresentti,(10)represent=13∑klayerk.The superscript ofti represents the specific coding method of the current word ti, and layer k represents the Bert coding of layer K (9 ≤ K ≤ 11). Based on the sequence of input data, the text decision model uses the structure of BiLSTM attention model to determine the pairs of input sentences. In order to let the model learn the influence of keywords on connectivity, the granularity of words is taken as the input of the model, and the attention mechanism is used to give weight to the input. The main decision-making model is shown in Figure 1.Figure 1
Topic decision model.Specifically, suppose that the input data sentenceI, sentence i + 1, sentence I, sentence i + 1 = w1,w2,…,wm, and BiLSTM first encode the input at each time, as shown in(11)ot=BiLSTMwt.Use the attention mechanism to calculate the weight of the output of BiLSTM model at each time point, and the formulas are shown in(12)ut=tanhWw⋅Ot+bw,(13)at=exputT⋅uw∑texputT⋅uw.UT, WW, and BW are the parameters of the layer where the attention mechanism is located, and at is the weight of the input sequence in the total input at thet-th time point. Therefore, after the attention mechanism layer, the calculation formula of the input vector expression VT and the weighting expression obtained is as follows:(14)vt=at⋅Ot.After using the attention mechanism to calculate the vector representation with lexical weight, attach it to a full connection layer to reduce the size, and then use the softmax function to classify. Let it be the combined vectorV′, and the calculation formula is shown in equations (17) to (19).(15)v=conv1,v2,⋯,vmv′=fcvy=softmaxv′⋅Ws+bs.Taking a training group as an example, suppose that the current training groupe = {e1, e2,..., en} in N training samples, the mean square error function is expressed as shown in(16)lossYE,YE˜=−1n∑i=1nyi⋅logy˜i+1−yi⋅log1−y˜i.YE is the current batch data category. The semantic vectorization of composition subject and topic is the key to accurately capture the similarities between them and verify the methodology of this paper. Once the topic and subject are semantically vectorized, they are projected into the same semantic space, and the distance between them reflects the semantic similarity between them. By calculating the distance between two vectors, we can get the similarity between them.


## 3.1. Support Vector Machine

Support vector machine is a classification algorithm using supervised learning training. SVM is determined by important training samples, such as support vector. Therefore, if all classifications are correct, the SVM classification problem can be described as a finite problem of optimizing linear classification, as shown in(1)minω,b12|ω|2(2)s.t.yiω⋅xi+b−1≥0.For each inequality constraint, Lagrange multipliers AI ≥ 0,i = 1, 2, N; the Lagrangian function is constructed as follows:(3)Lω,b,α=12|ω|2−∑i=1Naiyiω⋅xi+b−1.According to Lagrange duality, the original finite optimization problem can be equivalent to the dipole minimax problem, as shown in(4)maxαminω,bLω,b,α.Next,l(ω, b, A) after deriving, we get(5)∂L∂ω=ω−∑i−1Nαiyixi,∂L∂b=∑i−1Nαiyi.After it is brought into the Lagrange function, the solution of the SVM problem can be equivalent to the problem of optimizing the following functions, and the solution learning algorithm can be completed by the following operations:(6)minα12∑i=1N∑j=1Nαiαjyiyjxi⋅xj−∑i=1Nαi,s.t.∑i=1Naiyi=0,αi≥0,i=1,2…,N.


## 3.2. Bayes Theorem

The concept of conditional probability is particularly important in Bayesian theorem. Conditional probability means that there are two eventsA and B, and the probability of event B is greater than 0. Then, in the case of event B, the probability of event A is called the conditional probability of A when B occurs, that is, P (a|b). For event B, there are(7)PB=∑i−1∞PAiPA∣Bi.Naive Bayes formula is shown in(8)PBi∣A=PBiPA∣Bi∑j=1nPBjPA∣Bj.Bayesian formula is used to classify the dimensional components of the data set, assuming that the components of the training set are independent of each other. But in real life, this assumption is almost impossible. There may be an inseparable relationship between any features. If there is a strong potential relationship between features, the effect of naive Bayesian classification will be affected.


## 3.3. Text Classification of English News

Firstly, the English news text data are encoded into the template, and the granularity text data are obtained by using the template. Then, the semantic vectorization of news text topic is carried out.The sentence coding method uses the average value of Bert layer coding from 9 to 12 to represent the wordwi, and the formulas are shown in(9)wi=BERTrepresentti,(10)represent=13∑klayerk.The superscript ofti represents the specific coding method of the current word ti, and layer k represents the Bert coding of layer K (9 ≤ K ≤ 11). Based on the sequence of input data, the text decision model uses the structure of BiLSTM attention model to determine the pairs of input sentences. In order to let the model learn the influence of keywords on connectivity, the granularity of words is taken as the input of the model, and the attention mechanism is used to give weight to the input. The main decision-making model is shown in Figure 1.Figure 1
Topic decision model.Specifically, suppose that the input data sentenceI, sentence i + 1, sentence I, sentence i + 1 = w1,w2,…,wm, and BiLSTM first encode the input at each time, as shown in(11)ot=BiLSTMwt.Use the attention mechanism to calculate the weight of the output of BiLSTM model at each time point, and the formulas are shown in(12)ut=tanhWw⋅Ot+bw,(13)at=exputT⋅uw∑texputT⋅uw.UT, WW, and BW are the parameters of the layer where the attention mechanism is located, and at is the weight of the input sequence in the total input at thet-th time point. Therefore, after the attention mechanism layer, the calculation formula of the input vector expression VT and the weighting expression obtained is as follows:(14)vt=at⋅Ot.After using the attention mechanism to calculate the vector representation with lexical weight, attach it to a full connection layer to reduce the size, and then use the softmax function to classify. Let it be the combined vectorV′, and the calculation formula is shown in equations (17) to (19).(15)v=conv1,v2,⋯,vmv′=fcvy=softmaxv′⋅Ws+bs.Taking a training group as an example, suppose that the current training groupe = {e1, e2,..., en} in N training samples, the mean square error function is expressed as shown in(16)lossYE,YE˜=−1n∑i=1nyi⋅logy˜i+1−yi⋅log1−y˜i.YE is the current batch data category. The semantic vectorization of composition subject and topic is the key to accurately capture the similarities between them and verify the methodology of this paper. Once the topic and subject are semantically vectorized, they are projected into the same semantic space, and the distance between them reflects the semantic similarity between them. By calculating the distance between two vectors, we can get the similarity between them.


## 4. Analysis of the Mobile App Propagation Model

### 4.1. System Architecture Design

According to the characteristics of mobile application development, in the overall architecture design, the whole software mainly includes two parts: mobile end and server end. The main functions and levels of each part are shown in Table1.Table 1
System architecture.
ServerMobile terminalData layer (DB, DAO, DTO)Integrated appBusiness logic layer (service)UI interfaceService (resource) layer (resource)Offline serviceControl layer (web api)Remote service (http)In order to cope with high concurrency, applications are separated from static resources by grouping, such as placing static resources and service programs on static resource servers and application servers, and using Nginx as the reverse proxy server, as the front-end server, to process server integration requests and send various requests, such as static resource requests and application requests. Load balancing is created between clustered servers. The specific physical topology of the network is shown in Figure2.Figure 2
Physical network topology of the system.


### 4.2. Relevant Machine Learning Models and Parameter Design

#### 4.2.1. Learning Process of AdaBoost Algorithm

First, extract the first weak classifier by learningn training samples. The initial learning samples of weak classification with other new data are established in the new N training samples, and a weak classifier is obtained by relearning; combine the first and second error samples with other new sample data to form a new N training relearning samples and relearn; cycle until the end of the algorithm, and finally a strong classifier is formed.


#### 4.2.2. Algorithm Flow of AdaBoost

Initialize training data weight distribution:(17)D1=w11,…,w1i,…,w1N,w1i=1N,i=1,2,…,N,m = 1, 2,..., M.The basic classifier is obtained by learning the training data set of weight distribution DM:(18)Gmx:X⟶−1,1.Calculate the classification error rate of the basic classifier GM (x) on the training data set:(19)em=PGmx1≠y1=∑i=1NwmiIGmx1≠y1.Calculate the coefficients of the basic classifier GM (x):(20)αm=12log1−emem.Update the weight distribution of the training database:(21)Dm+1=wm+1,1,…,wm+1,i,…,wm+1,Nwm+1,i=wmizmexp−αmyiGmxi,i=1,2,…,NZm=∑i=1Nwmiexp−αmyiGmxi.Zm is the normalization factor, which makes Dm + 1 a probability distribution.Construct linear combination of basic classifiers:(22)fx=∑m=1MαmGmx.Finally, the final classifier is obtained:(23)Gx=signfx=sign∑m=1MαmGmx.


#### 4.2.3. AdaBoost Framework Parameters

Base estimator: the decision tree is used here as the weak classification learner.N estimators: number of weak learners (maximum iterations), default_50.Learning rate: the weight reduction coefficient of each weak learner, which is 1 by default. In the process of effectively adjusting parameters,n estimators and learning are often considered at the same time_Rate parameter to determine the algorithm fitting that performs properly.Algorithm: this parameter represents two AdaBoost classification algorithms implemented in scikit-learn. The default value of AdaBoost classifier algorithm is also SAMME.R.


#### 4.2.4. AdaBoost Decision Tree Parameters

Max features: the largest feature part, which means that all features are considered in the division. This parameter can take several different values, and the default is “None.”Max depth is the maximum depth of the decision tree, and the normally used value is between 10 and 100.Min sample split: the minimum number of samples required for internal node subdivision.Min leaf samples: the minimum number of leaf samples. The default is 1.Min weight fraction leaf: the minimum weight of the leaf node sample, that is, the weight problem is not considered.Max leaf nodes: the maximum number of leaf nodes, which is “None” by default. In general, this parameter is limited to avoid overfitting.


### 4.3. Analysis of Experimental Results

Here, five machine learning models, LIBFFM, light GBM, Vowpal Wabbit, XGBoost, and random forest, are used to train the data, and AdaBoost model is used to train and compare the generated data set. The final prediction results of the five models are shown in Table2.Table 2
Comparison of prediction effects of various models.
ModelLog-lossAUCLIBFFM0.122119114570.698978541173Light GBM0.1175941745870.748343536724Vowpal Wabbit0.1428245347630.697595468316XGBoost0.1221678341520.775976543747Random forest0.124538789760.775674287815AdaBoost0.1123418768760.78748377195As shown in Table3, the prediction effect of AdaBoost model is enhanced in both indicators, indicating that AdaBoost model has advantages over other models. In addition, we calculate the cumulative distribution of positive samples in the prediction results, divide the prediction probability of positive samples into 10 levels, and calculate the cumulative distribution of positive samples at each level. The calculation results show that the cumulative distribution of the sixth-level positive samples is higher than the normal value, which also shows that the prediction method used in this paper is correct and can be used to solve the model of CTR prediction problem.Table 3
AdaBoost model positive sample accumulation distribution.
GradeNumber of positive samplesNumber of negative samplesTotal number of samplesCumulative distribution of positive samples11154737585290.4782307822085270.6053228830085280.7004187834185270.7775166836385280.8456142838585270.9057102842585270.946889844085280.984945848285271.0021017850985261.010


## 4.1. System Architecture Design

According to the characteristics of mobile application development, in the overall architecture design, the whole software mainly includes two parts: mobile end and server end. The main functions and levels of each part are shown in Table1.Table 1
System architecture.
ServerMobile terminalData layer (DB, DAO, DTO)Integrated appBusiness logic layer (service)UI interfaceService (resource) layer (resource)Offline serviceControl layer (web api)Remote service (http)In order to cope with high concurrency, applications are separated from static resources by grouping, such as placing static resources and service programs on static resource servers and application servers, and using Nginx as the reverse proxy server, as the front-end server, to process server integration requests and send various requests, such as static resource requests and application requests. Load balancing is created between clustered servers. The specific physical topology of the network is shown in Figure2.Figure 2
Physical network topology of the system.


## 4.2. Relevant Machine Learning Models and Parameter Design

### 4.2.1. Learning Process of AdaBoost Algorithm

First, extract the first weak classifier by learningn training samples. The initial learning samples of weak classification with other new data are established in the new N training samples, and a weak classifier is obtained by relearning; combine the first and second error samples with other new sample data to form a new N training relearning samples and relearn; cycle until the end of the algorithm, and finally a strong classifier is formed.


### 4.2.2. Algorithm Flow of AdaBoost

Initialize training data weight distribution:(17)D1=w11,…,w1i,…,w1N,w1i=1N,i=1,2,…,N,m = 1, 2,..., M.The basic classifier is obtained by learning the training data set of weight distribution DM:(18)Gmx:X⟶−1,1.Calculate the classification error rate of the basic classifier GM (x) on the training data set:(19)em=PGmx1≠y1=∑i=1NwmiIGmx1≠y1.Calculate the coefficients of the basic classifier GM (x):(20)αm=12log1−emem.Update the weight distribution of the training database:(21)Dm+1=wm+1,1,…,wm+1,i,…,wm+1,Nwm+1,i=wmizmexp−αmyiGmxi,i=1,2,…,NZm=∑i=1Nwmiexp−αmyiGmxi.Zm is the normalization factor, which makes Dm + 1 a probability distribution.Construct linear combination of basic classifiers:(22)fx=∑m=1MαmGmx.Finally, the final classifier is obtained:(23)Gx=signfx=sign∑m=1MαmGmx.


### 4.2.3. AdaBoost Framework Parameters

Base estimator: the decision tree is used here as the weak classification learner.N estimators: number of weak learners (maximum iterations), default_50.Learning rate: the weight reduction coefficient of each weak learner, which is 1 by default. In the process of effectively adjusting parameters,n estimators and learning are often considered at the same time_Rate parameter to determine the algorithm fitting that performs properly.Algorithm: this parameter represents two AdaBoost classification algorithms implemented in scikit-learn. The default value of AdaBoost classifier algorithm is also SAMME.R.


### 4.2.4. AdaBoost Decision Tree Parameters

Max features: the largest feature part, which means that all features are considered in the division. This parameter can take several different values, and the default is “None.”Max depth is the maximum depth of the decision tree, and the normally used value is between 10 and 100.Min sample split: the minimum number of samples required for internal node subdivision.Min leaf samples: the minimum number of leaf samples. The default is 1.Min weight fraction leaf: the minimum weight of the leaf node sample, that is, the weight problem is not considered.Max leaf nodes: the maximum number of leaf nodes, which is “None” by default. In general, this parameter is limited to avoid overfitting.


## 4.2.1. Learning Process of AdaBoost Algorithm

First, extract the first weak classifier by learningn training samples. The initial learning samples of weak classification with other new data are established in the new N training samples, and a weak classifier is obtained by relearning; combine the first and second error samples with other new sample data to form a new N training relearning samples and relearn; cycle until the end of the algorithm, and finally a strong classifier is formed.


## 4.2.2. Algorithm Flow of AdaBoost

Initialize training data weight distribution:(17)D1=w11,…,w1i,…,w1N,w1i=1N,i=1,2,…,N,m = 1, 2,..., M.The basic classifier is obtained by learning the training data set of weight distribution DM:(18)Gmx:X⟶−1,1.Calculate the classification error rate of the basic classifier GM (x) on the training data set:(19)em=PGmx1≠y1=∑i=1NwmiIGmx1≠y1.Calculate the coefficients of the basic classifier GM (x):(20)αm=12log1−emem.Update the weight distribution of the training database:(21)Dm+1=wm+1,1,…,wm+1,i,…,wm+1,Nwm+1,i=wmizmexp−αmyiGmxi,i=1,2,…,NZm=∑i=1Nwmiexp−αmyiGmxi.Zm is the normalization factor, which makes Dm + 1 a probability distribution.Construct linear combination of basic classifiers:(22)fx=∑m=1MαmGmx.Finally, the final classifier is obtained:(23)Gx=signfx=sign∑m=1MαmGmx.


## 4.2.3. AdaBoost Framework Parameters

Base estimator: the decision tree is used here as the weak classification learner.N estimators: number of weak learners (maximum iterations), default_50.Learning rate: the weight reduction coefficient of each weak learner, which is 1 by default. In the process of effectively adjusting parameters,n estimators and learning are often considered at the same time_Rate parameter to determine the algorithm fitting that performs properly.Algorithm: this parameter represents two AdaBoost classification algorithms implemented in scikit-learn. The default value of AdaBoost classifier algorithm is also SAMME.R.


## 4.2.4. AdaBoost Decision Tree Parameters

Max features: the largest feature part, which means that all features are considered in the division. This parameter can take several different values, and the default is “None.”Max depth is the maximum depth of the decision tree, and the normally used value is between 10 and 100.Min sample split: the minimum number of samples required for internal node subdivision.Min leaf samples: the minimum number of leaf samples. The default is 1.Min weight fraction leaf: the minimum weight of the leaf node sample, that is, the weight problem is not considered.Max leaf nodes: the maximum number of leaf nodes, which is “None” by default. In general, this parameter is limited to avoid overfitting.


## 4.3. Analysis of Experimental Results

Here, five machine learning models, LIBFFM, light GBM, Vowpal Wabbit, XGBoost, and random forest, are used to train the data, and AdaBoost model is used to train and compare the generated data set. The final prediction results of the five models are shown in Table2.Table 2
Comparison of prediction effects of various models.
ModelLog-lossAUCLIBFFM0.122119114570.698978541173Light GBM0.1175941745870.748343536724Vowpal Wabbit0.1428245347630.697595468316XGBoost0.1221678341520.775976543747Random forest0.124538789760.775674287815AdaBoost0.1123418768760.78748377195As shown in Table3, the prediction effect of AdaBoost model is enhanced in both indicators, indicating that AdaBoost model has advantages over other models. In addition, we calculate the cumulative distribution of positive samples in the prediction results, divide the prediction probability of positive samples into 10 levels, and calculate the cumulative distribution of positive samples at each level. The calculation results show that the cumulative distribution of the sixth-level positive samples is higher than the normal value, which also shows that the prediction method used in this paper is correct and can be used to solve the model of CTR prediction problem.Table 3
AdaBoost model positive sample accumulation distribution.
GradeNumber of positive samplesNumber of negative samplesTotal number of samplesCumulative distribution of positive samples11154737585290.4782307822085270.6053228830085280.7004187834185270.7775166836385280.8456142838585270.9057102842585270.946889844085280.984945848285271.0021017850985261.010


## 5. Investigation on the Spread of Mobile App News English

### 5.1. Investigation Objects and Methods

In order to make the survey more orderly and the results more systematic, this paper aims to limit the survey objects to freshmen to seniors in colleges and universities according to the principle of demography and the age difference and education level. From the perspective of college students’ media literacy, they are loyal users of various media. In addition, they have rich basic knowledge, fast acceptance of new things, strong understanding, high initiative, strong thirst for knowledge, and a spirit of inquiry. Such qualities determine their higher requirements for the media. From the perspective of college students’ influence on the media, no matter what kind of media, college students will be an important group to use this kind of media in the future. These habits will inevitably affect and further determine their tendency to use media in the future. In the highly competitive media industry, there is now a comprehensive discussion from lifestyle to ideology. The transformation of audience-centered communication relationship is particularly valued in the media. Therefore, when this group grows into the social backbone or the main user group of the media, it is the main audience of the major media. This study adopts a combination of random questionnaires and interviews.A total of 600 samples were collected in this survey, and 586 samples were recovered, with a recovery rate of 97.67%. After screening, 12 nonconformities were eliminated, and 574 questionnaires were collected, with an effective rate of 95.67%.


### 5.2. Analysis of English News Reading Preference of Mobile App

As shown in Figure3, in terms of the frequency of use of the mobile app, college students do not often use the mobile app. About 81.9% of students use it at most twice a week, about 15.5% of students use it 3-4 times a week, and only 2.6% of students use it more than 5 times a week. The low frequency of use proves, to a certain extent, that college students are less dependent on mobile app.Figure 3
Reading frequency of English news on mobile app.As shown in Figure4, in terms of the average time spent, about half of the students usually spend between 30 minutes and 1 hour, nearly 40% of the students spend less than half an hour, and only a few students spend more than 1 hour. From the use time, it can be seen that most students’ use of mobile applications is only in the stage of “quick browsing” or “light reading”; that is, they prefer to choose content that can quickly attract attention to browse the web, such as titles, images, and keywords. This kind of reading method is difficult to give students the opportunity to have in-depth communication and interaction with app, thus reducing dependence and weakening influence.Figure 4
Reading duration of English news on mobile app.Figure5 shows the reading preferences of mobile app English news. By analyzing English mobile applications, we find that these applications significantly increase the coverage of international news events, which meets the needs of students. In addition, in the report of app news, we also pay attention to this problem: with the development of China, more and more people are eager to understand the human rights, legal system, and economic situation of today’s Chinese society.Figure 5
Reading preferences of mobile app English news.As shown in Figure6, from the perspective of the purpose of using the app, the original intention of the respondents to use the mobile app from top to bottom is learning English (49.0%), obtaining news information (25.1%), collecting data (15.0%), and only 9% of the students are curious or for other purposes. Although this survey shows that the purpose of students’ use of app is relatively clear and targeted, it is not all the same, which also means that there is still much room for improvement in the attractiveness of English app to college students.Figure 6
Reading purpose of mobile app English news.


### 5.3. Enlightenment of Mobile App News English Communication

Communication power, as the name suggests, refers to “the ability of media communication, including the amount of information, the speed of information transmission, and the coverage and influence of information. The influence effect is the main feature of communication effect. In media communication, technical means are the decisive factor affecting communication.” “Communication power” exists as a power, there must be different indicators to measure this power, and these indicators naturally become a part of “communication power.” Generally, indicators can be divided into two categories. One is hard indicators, including quantifiable indicators such as media audience coverage, infrastructure, income funds, and the number of employees; the other is the soft index, which is mainly determined by the quality of communicators and the credibility of the media. The two are complementary and necessary systems. High-quality communicators help to improve the credibility of the media, and high credibility media can cultivate and absorb high-quality communication practitioners. At present, 5G and artificial intelligence technology are being initially applied and gradually popularized. The mobile bandwidth will be effectively improved, the connection delay will be shorter, and everything can be interconnected. These application technologies create conditions for media to enhance sensory forms and optimize interactive experience. As an integrated media platform, app is partly based on the credibility of CCTV and has the advantages of fast speed, multimedia support, fast interaction, and other new media communication. Combining the two to build a short video media system can create a more intuitive English cultural experience for users. The platform can not only launch the production and release function of English short videos, but also experience and big data analysis of the target audience, discover the interests, preferences, and attention trends of short video users, and build a short video service model. Considering the communication needs of the audience, videos can be shared to other social platforms such as circle of friends, Weibo, and QQ space, which are favored by a large number of young people. In this way, by designing and promoting short video services, app can solve the fragmented reading needs of the audience from the perspective of the current task, promote communication and interaction within the platform, and enable the media to achieve the creation of high-quality online services and the sustainable development of English news applications.The intelligent design of app integrating media experience can use artificial intelligence technology, combined with QR code scanning, to optimize user interaction through robot news, image recognition, language recognition, audio reading, machine translation, and other technologies. Provide users with simpler and more convenient English cultural service resources, and stimulate users’ active participation. On the one hand, with the support of AI technology, app can use face recognition system to make it easier for users to identify themselves, such as registration, login, and authentication. On the other hand, AI applets can also be developed. For example, unique symbols such as the applet logo can be drawn into humanized images for dialogue and interaction with users. It can also complete commands such as automatic playback, screen projection playback, and live video streaming through voice interaction, which can realize voice reading news and bring users a multi-sensory experience of audio-visual integration. These functions are more interactive and interesting, optimize the user experience, and help improve user participation. In addition to the reporter function, AI applications can also be used as robot customer service to provide better services to the public through more knowledge base reserves, application scenario-based scheme adjustment, intelligent guidance and related problems, automatic service effect evaluation, and other functions, and to collect problems and highlight weaknesses according to platform changes.Hypertext and hyperlink technology broke through the past linear and single media communication mode, not only spread information through the network in multiple dimensions, but also strengthened the function of collecting and network communication. App disseminators organize information according to specific connections, which makes the form of online news display emerge one after another, and relevant news, news rankings, news recommendations, and other forms of expression emerge in a timely manner, which undoubtedly helps to improve network utilization. However, the convergence of the Internet puts forward higher requirements for reporters and editors to organize information in English news applications. They need to carefully study the psychological characteristics of the audience, constantly create intensive columns, and regularly create live and intensive columns to effectively organize information. It will make it easier for the public to find, obtain more, and understand the event more deeply.Even if the communication effect is only a manifestation of the final result, any element in the communication process will change this result. If the founder of app is proficient in news, English, Internet, brand, and other aspects, even in all fields, that is, “one specialty and many abilities,” or even “many specialties and many abilities,” then the efficiency of editing manuscripts, page layout web, app maintenance, and other work will be greatly improved, and the spread of app will be smoother. At present, there are three main ways to cultivate English Media Talents in China: one is the on-the-job training organized by the media itself, the other is the short-term training of local propagandists through a variety of ways, and the third is the joint training of the college of journalism and the college of foreign languages as a reserve force for the cause of news media communication. These three ways have a strong guiding role in building and training Chinese English news application talents.In addition to the continuous improvement of professional ability, news communicators must also strengthen the construction of professional ethics. From the very beginning, the English media in China have been shouldering the task of national propaganda. Every subtle point of view, such as what pictures are used in the daily app to make question maps and how English words are expressed, will have a great or small impact on society. Therefore, the professional ethics of English news app workers is very important in practical work. Staff engaged in English news online broadcasting must carry out professional ethics quality training under the joint action of heteronomy and self-discipline. Heteronomy means to strengthen the training of laws and regulations of online media, attach great importance to the establishment of Internet laws and regulations, strictly abide by laws and regulations, and clarify the rights and obligations of English online news practitioners to run websites in a civilized manner. Self-discipline refers to the fact that the most direct driving force of English online news self-discipline comes from the communication topic. Professional ethics must be transformed into self-journalistic ethics, forming professional conscience, professional honor, and basic professional ethics, so as to dominate their own journalistic communication activities and provide strong spiritual support for objective communication.


## 5.1. Investigation Objects and Methods

In order to make the survey more orderly and the results more systematic, this paper aims to limit the survey objects to freshmen to seniors in colleges and universities according to the principle of demography and the age difference and education level. From the perspective of college students’ media literacy, they are loyal users of various media. In addition, they have rich basic knowledge, fast acceptance of new things, strong understanding, high initiative, strong thirst for knowledge, and a spirit of inquiry. Such qualities determine their higher requirements for the media. From the perspective of college students’ influence on the media, no matter what kind of media, college students will be an important group to use this kind of media in the future. These habits will inevitably affect and further determine their tendency to use media in the future. In the highly competitive media industry, there is now a comprehensive discussion from lifestyle to ideology. The transformation of audience-centered communication relationship is particularly valued in the media. Therefore, when this group grows into the social backbone or the main user group of the media, it is the main audience of the major media. This study adopts a combination of random questionnaires and interviews.A total of 600 samples were collected in this survey, and 586 samples were recovered, with a recovery rate of 97.67%. After screening, 12 nonconformities were eliminated, and 574 questionnaires were collected, with an effective rate of 95.67%.


## 5.2. Analysis of English News Reading Preference of Mobile App

As shown in Figure3, in terms of the frequency of use of the mobile app, college students do not often use the mobile app. About 81.9% of students use it at most twice a week, about 15.5% of students use it 3-4 times a week, and only 2.6% of students use it more than 5 times a week. The low frequency of use proves, to a certain extent, that college students are less dependent on mobile app.Figure 3
Reading frequency of English news on mobile app.As shown in Figure4, in terms of the average time spent, about half of the students usually spend between 30 minutes and 1 hour, nearly 40% of the students spend less than half an hour, and only a few students spend more than 1 hour. From the use time, it can be seen that most students’ use of mobile applications is only in the stage of “quick browsing” or “light reading”; that is, they prefer to choose content that can quickly attract attention to browse the web, such as titles, images, and keywords. This kind of reading method is difficult to give students the opportunity to have in-depth communication and interaction with app, thus reducing dependence and weakening influence.Figure 4
Reading duration of English news on mobile app.Figure5 shows the reading preferences of mobile app English news. By analyzing English mobile applications, we find that these applications significantly increase the coverage of international news events, which meets the needs of students. In addition, in the report of app news, we also pay attention to this problem: with the development of China, more and more people are eager to understand the human rights, legal system, and economic situation of today’s Chinese society.Figure 5
Reading preferences of mobile app English news.As shown in Figure6, from the perspective of the purpose of using the app, the original intention of the respondents to use the mobile app from top to bottom is learning English (49.0%), obtaining news information (25.1%), collecting data (15.0%), and only 9% of the students are curious or for other purposes. Although this survey shows that the purpose of students’ use of app is relatively clear and targeted, it is not all the same, which also means that there is still much room for improvement in the attractiveness of English app to college students.Figure 6
Reading purpose of mobile app English news.


## 5.3. Enlightenment of Mobile App News English Communication

Communication power, as the name suggests, refers to “the ability of media communication, including the amount of information, the speed of information transmission, and the coverage and influence of information. The influence effect is the main feature of communication effect. In media communication, technical means are the decisive factor affecting communication.” “Communication power” exists as a power, there must be different indicators to measure this power, and these indicators naturally become a part of “communication power.” Generally, indicators can be divided into two categories. One is hard indicators, including quantifiable indicators such as media audience coverage, infrastructure, income funds, and the number of employees; the other is the soft index, which is mainly determined by the quality of communicators and the credibility of the media. The two are complementary and necessary systems. High-quality communicators help to improve the credibility of the media, and high credibility media can cultivate and absorb high-quality communication practitioners. At present, 5G and artificial intelligence technology are being initially applied and gradually popularized. The mobile bandwidth will be effectively improved, the connection delay will be shorter, and everything can be interconnected. These application technologies create conditions for media to enhance sensory forms and optimize interactive experience. As an integrated media platform, app is partly based on the credibility of CCTV and has the advantages of fast speed, multimedia support, fast interaction, and other new media communication. Combining the two to build a short video media system can create a more intuitive English cultural experience for users. The platform can not only launch the production and release function of English short videos, but also experience and big data analysis of the target audience, discover the interests, preferences, and attention trends of short video users, and build a short video service model. Considering the communication needs of the audience, videos can be shared to other social platforms such as circle of friends, Weibo, and QQ space, which are favored by a large number of young people. In this way, by designing and promoting short video services, app can solve the fragmented reading needs of the audience from the perspective of the current task, promote communication and interaction within the platform, and enable the media to achieve the creation of high-quality online services and the sustainable development of English news applications.The intelligent design of app integrating media experience can use artificial intelligence technology, combined with QR code scanning, to optimize user interaction through robot news, image recognition, language recognition, audio reading, machine translation, and other technologies. Provide users with simpler and more convenient English cultural service resources, and stimulate users’ active participation. On the one hand, with the support of AI technology, app can use face recognition system to make it easier for users to identify themselves, such as registration, login, and authentication. On the other hand, AI applets can also be developed. For example, unique symbols such as the applet logo can be drawn into humanized images for dialogue and interaction with users. It can also complete commands such as automatic playback, screen projection playback, and live video streaming through voice interaction, which can realize voice reading news and bring users a multi-sensory experience of audio-visual integration. These functions are more interactive and interesting, optimize the user experience, and help improve user participation. In addition to the reporter function, AI applications can also be used as robot customer service to provide better services to the public through more knowledge base reserves, application scenario-based scheme adjustment, intelligent guidance and related problems, automatic service effect evaluation, and other functions, and to collect problems and highlight weaknesses according to platform changes.Hypertext and hyperlink technology broke through the past linear and single media communication mode, not only spread information through the network in multiple dimensions, but also strengthened the function of collecting and network communication. App disseminators organize information according to specific connections, which makes the form of online news display emerge one after another, and relevant news, news rankings, news recommendations, and other forms of expression emerge in a timely manner, which undoubtedly helps to improve network utilization. However, the convergence of the Internet puts forward higher requirements for reporters and editors to organize information in English news applications. They need to carefully study the psychological characteristics of the audience, constantly create intensive columns, and regularly create live and intensive columns to effectively organize information. It will make it easier for the public to find, obtain more, and understand the event more deeply.Even if the communication effect is only a manifestation of the final result, any element in the communication process will change this result. If the founder of app is proficient in news, English, Internet, brand, and other aspects, even in all fields, that is, “one specialty and many abilities,” or even “many specialties and many abilities,” then the efficiency of editing manuscripts, page layout web, app maintenance, and other work will be greatly improved, and the spread of app will be smoother. At present, there are three main ways to cultivate English Media Talents in China: one is the on-the-job training organized by the media itself, the other is the short-term training of local propagandists through a variety of ways, and the third is the joint training of the college of journalism and the college of foreign languages as a reserve force for the cause of news media communication. These three ways have a strong guiding role in building and training Chinese English news application talents.In addition to the continuous improvement of professional ability, news communicators must also strengthen the construction of professional ethics. From the very beginning, the English media in China have been shouldering the task of national propaganda. Every subtle point of view, such as what pictures are used in the daily app to make question maps and how English words are expressed, will have a great or small impact on society. Therefore, the professional ethics of English news app workers is very important in practical work. Staff engaged in English news online broadcasting must carry out professional ethics quality training under the joint action of heteronomy and self-discipline. Heteronomy means to strengthen the training of laws and regulations of online media, attach great importance to the establishment of Internet laws and regulations, strictly abide by laws and regulations, and clarify the rights and obligations of English online news practitioners to run websites in a civilized manner. Self-discipline refers to the fact that the most direct driving force of English online news self-discipline comes from the communication topic. Professional ethics must be transformed into self-journalistic ethics, forming professional conscience, professional honor, and basic professional ethics, so as to dominate their own journalistic communication activities and provide strong spiritual support for objective communication.


## 6. Conclusion

At present, there are some problems in Chinese English news apps, such as the convergence of content settings, the rigidity of news reporting style, the prominent phenomenon of Chinglish, the low level of marketing, and the insufficient popularity of applications. And most of the news reports are translated directly from Chinese manuscripts, lacking characteristics, and innovation. At present, many apps rely on the government and large media groups and regard them as the parents of food and clothing, thus ignoring the basic needs of the public. These problems have more or less affected the dissemination effect of apps. In the context of information globalization and increasingly frequent information exchange between countries, English media should play an important role in shaping a positive national image. In a longer period of time, China English news app has great development potential. Therefore, this paper mainly focuses on the problems reflected in the questionnaire and the factors affecting communication, combined with the characteristics of network communication, especially the communication power and the public reliability of app, the progressiveness of communication technology, environmental comfort, the professional ethics of communicators, and other aspects to improve the brand construction of app, trying to improve the communication influence construction effect of English news app.



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*Source: 1919707-2022-10-03.xml*