Network Analysis and Audience Behavior Modeling of Popular Music Communication Paths in the Information Age
Publicado en línea: 19 mar 2025
Recibido: 09 nov 2024
Aceptado: 20 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0375
Palabras clave
© 2025 Yufeng Wang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The media are all the channels and means through which people acquire and disseminate information. Any change in media technology also provides a brand new communication channel and operation mode for the communication of popular music, in order to transcend the technical connotation of its own existence and obtain a wider communication meaning, and together with the social environment and cultural environment, constitute the ecology of popular music [1-2]. From phonograph, record, tape, MP3, radio, television to network media, every stage of the development of popular music is related to communication technology, and every change of communication media provides new ways of communication for popular music [3]. Especially today, the synergy formed by traditional communication media and network media has opened up a new path for the development of popular music, and a new ecological environment of popular music has been jointly constructed under the combination and interaction of old and new communication media [4-6].
The value of the Internet comes not only from network technology, but also from the accumulation of users’ behavioral data, and meeting users’ needs is the starting point and landing point of media transformation for new media product development [7-8]. The development of social media, more and more “user-centered”, focusing on instant interaction, advocating personalized content, completely subverted the traditional media point-to-multipoint communication form [9-10]. Fast, accurate, precise and diversified communication channels allow users to choose to use the most suitable communication channels and methods at any time and any place to obtain and disseminate all kinds of information they need [11-13]. In the “user-centered” network environment, through the use of background big data tracking user behavior, analysis of data, making the user’s access to content has become more convenient and accurate, giving users a more “intimate” experience [14-16]. The listening habits of the public have also been changed accordingly by the corporate demands of “user first” and “enhancing user stickiness”, thus affecting the dissemination of popular music [17-18].
Li, X. studied the influence of mass media on music communication in the context of digitization, and there is an important link between the Internet-based media industry and music art, which deeply influences the production, dissemination and popularity of music art, reflecting a strong media influence [19]. Khosravian, L. describes the history of the development of music on the Internet, the early days of the online music industry where service providers limited individual taste and expression of art through music category selection, and the rise of video-sharing social media, with its user-generated creative content providing space for a wide range of musical genres, making the music industry more democratic [20]. Hagen, A. N. describes the relationship between digitization and the democratization of the music industry, showing that service providers of digital music platforms have made music digitization their core activity, creating and sustaining a digital divide by using music data as a commodity, in which data literacy for accessing and analyzing listener behaviors will be a key resource [21]. Jiang, M. et al. analyzed the influencing factors of sub-healthy people’s willingness to purchase mobile digital music, and found that perceived quality, perceived price, social value, and emotional value would significantly affect users’ willingness to purchase, which would help to promote the further development of the music industry of mobile terminals by searching for personalized mobile digital music solutions for specific populations [22]. Kaye, D. B. V.’s exploration of music creation in digital media platforms suggests that TikTok possesses a range of participatory culture and distributed creativity features that successfully engage users in music creation, overcoming the precariousness of creative labor in online digital spaces [23]. Parc, J. et al. countered the adverse effects of digital transformation in the music industry by stating that Korean popular music has achieved international dissemination using digital technology, that digital transformation has facilitated the further development of the music industry, and that embracing technological advances can improve the competitiveness of cultural industries [24]. Datta, H. analyzed the listening behavior of music streaming consumers on music platforms and found that users try a greater number and diversity of music after using streaming technology, reducing the number of music repeats and when increasing the discovery rate of new music [25].
The study takes audience behavior in popular music communication as the object of research, and analyzes the supportive relationship of communication media to popular music communication on the basis of analyzing the characteristics of each constituent element of popular music communication using social network analysis. Based on the Theory of Planned Behavior, we construct a model of factors influencing audience’s sharing behavior towards popular music, collect data and put forward hypotheses using questionnaire method, and finally analyze the data using Structural Equation Modeling. Explore the influencing factors of the audience’s sharing behavior towards popular music and improve the dissemination of music information.
In the Internet age, digital technology and new media technologies provide more channels for people to obtain information and also accelerate the speed of information dissemination. On the one hand, the Internet technology enables mainstream music to spread rapidly to the public, although it narrows the living space of music to a certain extent, bringing challenges to the dissemination of music, but on the other hand, the threshold of the Internet is lower, which enables the rapid dissemination of huge amount of information in a short time, which is also an opportunity for the dissemination of popular music. On the other hand, the low barrier of entry of the Internet enables the rapid dissemination of massive amounts of information in a short period of time, which is also an opportunity for the dissemination of popular music.
Currently, mainstream music has strong communication power and expressive power, and can be communicated through apps, web pages, short videos, etc., which is a form of communication that pop music can learn from, and on the basis of innovative communication content, popular music can be communicated to a wider audience through animation and video [26]. For example, it can be rooted in the “Learning Strong Country” platform, and use the “Learning Strong Country” platform as the base to produce relevant broadcasts of popular music, and publicize in the form of combining broadcasts and music. At the same time, online interaction and live broadcasting can be utilized to disseminate the process of pop music creation and performance in real time, so that the audience can actively participate in the communication and dissemination of pop music through online interaction.
At the same time, through the function of big data analysis, it can also analyze the contents of the speeches and browsing records watched by online users and feedback them to professional pop music research experts and scholars, so as to continuously innovate and enrich the communication forms of pop music, make more people understand pop music, a music genre full of artistic charm, and thus promote the rapid transmission of the cultural connotations of pop music and the dissemination of excellent works to the outside world. The program will promote the rapid transmission of the cultural meanings of popular music and the dissemination of excellent works.
Social network analysis is a research method in the field of sociology, the essence of which is to study the inner relationship of the set formed by points (actors) based on a multidimensional perspective, providing an important quantitative tool for the construction of the relevant theoretical system and the verification of propositions. Its expression form includes “relationship matrix” and “community diagram” [27-28]. Among them, there are many ways to express the relationship matrix, and this paper adopts the adjacency matrix, in which the rows and columns represent the same actors, and the rows and columns are sorted in the same order. Community graph consists of “points” (actors) and “lines” (relationships between actors), according to the direction and closeness of the relationship can be categorized into directed graphs, undirected graphs, and binary value graphs, assignment graphs, the community is shown in Figure 1. Since this paper studies the role of spatial nodes in relation to each other, it constructs an undirected network graph and adopts “binary undirected graph” and “multi-valued undirected graph” in spatial network and behavioral network, respectively.
Community diagram
The
K-nucleus schematic diagram
Faction refers to the network contains at least 3 points of the largest complete subgraph, indicating the existence of small groups in the network, small groups within the connection is tight and stable, while the connection between small groups is relatively weak. There are many ways to analyze the factions, in this paper, we choose to analyze the Fcations in Netdraw software.
Degree centrality indicates the number of associations between a node and other nodes in the network, reflecting the importance of the node in the network, and is calculated as follows:
The cut point represents the only node that a subgroup is connected to the network, if the point is taken away, the structure of the whole network is divided into a number of unrelated subgraphs, so it is a critical node in the network. However, when there are more cutpoints in a network, it indicates that the network has a more serious fragmentation problem, and it is easy to cause the instability of the network structure due to the damage of several key nodes.
Through the visualization and analysis of the software, a topology diagram of the popular music network in the information age can be obtained, as shown in Figure 3. According to the figure, it can be seen that the node propagation relationship is relatively close in the propagation path network of popular music in the information age.
Popular music network propagation path
The measurement results of network density using ucinet6.0 software are shown in Table 1, and the comparative measurements are made by binaryizing the popular music propagation path data. It can be seen that the density of pop music propagation valued directed network is 4.21, while after transforming the relation matrix into Boolean matrix, its network density is only 0.55. Therefore, the network density has a direct correlation with the strength of the relationship between the nodes, and the value of the network density will show a decreasing trend with the increase of the number of nodes.
Network density measurement results
| There is a value to the network density | There is a network density of the Boolean matrix |
|---|---|
| Density (matrix average) = 4.2141 Standard deviation=7.3522 | Density (matrix average) = 0.5545 Standard deviation=0.5122 |
The partial measurements of centrality of nodes of popular music communication are shown in Table 2.
The results of the center of the music communication point (Portion)
| Propagation path | Point of point | Point of entry | Center potential | Center potential |
|---|---|---|---|---|
| media | 912 | 123 | 1.479 | 0.226 |
| media | 646 | 34 | 1.058 | 0.085 |
| media | 527 | 483 | 0.869 | 0.797 |
| media | 424 | 184 | 0.706 | 0.323 |
| media | -19 | 205 | 0.003 | 0.356 |
| official | 359 | 7 | 0.603 | 0.447 |
| official | 336 | 122 | 0.224 | 0.567 |
| official | 293 | 185 | 0.498 | 0.325 |
| Social platform | 187 | 22 | 0.33 | 0.066 |
| Social platform | 152 | 34 | 0.084 | 0.275 |
| Public welfare dissemination | 6 | 13 | 0.027 | 0.015 |
| Public welfare dissemination | 4 | 8 | 0.005 | 0.027 |
| Video entertainment | 75 | 58 | 0.152 | 0.123 |
| Video entertainment | 3 | 6 | 0.03 | 0 |
| Internet promotion | 85 | 29 | 0.168 | 0.077 |
| Internet promotion | 8 | 7 | 0.002 | 0.009 |
| Network Centralization (Outdegree) = 1.224% | ||||
| Network Centralization (INdegree) = 0.45% | ||||
From the table, it can be seen that different types of nodes have different point centrality and point centrality potential, and nodes of the same type show different point centrality due to factors such as the degree of participation and influence. The value of centrality can reflect the position and influence of nodes in the communication network. Point centrality potential is the result of standardizing point centrality, which reflects the centralized tendency of node connections. In the popular music communication network, media, official platforms, and social platforms have greater node centrality.
The specific inter-node distance measurement results are shown in Table 3. According to the table, the maximum geodesic distance between nodes in the popular music propagation network is 3, and the geodesic distance between most of the nodes is 1. It can be seen that in the process of popular music propagation, the information needs to pass through fewer nodes, which also reflects that popular music has a faster propagation speed. On the whole, the average distance between nodes is 1.044, and this result also shows that popular music has better propagation ability.
Popular music propagation node distance measurement (Portion)
| Serial number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 2 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 |
| 3 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 3 |
| 4 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| 5 | 1 | 1 | 1 | 1 | 0 | 3 | 1 | 1 |
| 6 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 |
| 7 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
| 8 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 0 |
| Average distance=1.033 | ||||||||
| Distance-bansed cohesion(“C0mpactness”)=0.878 | ||||||||
The K-kernel measurement results in the popular music dissemination network are shown in Table 4. According to the K-kernel measurement results, the maximum value of K-kernel in the popular music dissemination network is 30, and there are 40 nodes with K-kernel value of 30, which indicates that there is a closely-connected small group structure in the popular music dissemination network. And this small group occupies a dominant position in the whole popular music dissemination network, controlling a large number of dissemination sources, and its nodes are in the position of key nodes in the whole dissemination network. On the whole, the media nodes have a strong ability to be disseminated, and the sources of their information are also relatively wide, which is consistent with the actual characteristics of music media.
| Propagation path | Node | K value |
|---|---|---|
| media | Netease cloud music | 30 |
| media | Qq music | 30 |
| Public welfare dissemination | Public donation | 30 |
| Video entertainment | flutter | 30 |
| Video entertainment | Station b | 30 |
| Social platform | 30 | |
| Internet promotion | Micro blog | 30 |
The Theory of Planned Behavior is the best-known theory of attitude-behavior relationships in social psychology, a theory that explains the general decision-making process of individual behavior from the perspective of information processing and using expected value theory as a starting point. Planned behavior holds that behavioral willingness is the most direct factor influencing behavior, and behavioral willingness is in turn influenced by behavioral attitudes, subjective norms, and perceived behavioral control.
Based on the theory of planned behavior, a model is constructed from the information sharing behavior of the information audience to explore the influence of attitude, subjective norms, perceived behavioral control, and willingness to share corporate marketing information on corporate marketing information sharing behavior. The theory of planned behavior holds that enterprise marketing information sharing behavior depends on users’ information sharing willingness, and information sharing willingness is influenced by three aspects: sharing attitude, subjective norms and perceived behavioral control, and the model key factors are defined as shown in Table 5.
Model key factors and their definitions
| Key factor | Key factor positioning |
|---|---|
| Sharing attitude | The positive or negative feeling of the audience’s convection music is positive or negative. |
| Subjective specification | The audience is under social pressure to share popular music. |
| Sharing control | It is easy/difficult to control and execute the music sharing behavior. |
| Popular music sharing will | The decision of the subjective probability of the popular music sharing behavior reflects the desire of the audience to share the behavior of music. |
| Audience convection music sharing behavior | The behavior of spreading and sharing music information to other audiences. |
Based on the Theory of Planned Behavior, starting from the information sharing behavior of information audience, the attitude, subjective norms, perceived behavioral control of information audience and the willingness to share corporate marketing information are taken as the influencing factors of the corporate marketing information sharing behavior, and it is believed that audience behavior depends on the willingness of audience to share the information of popular music, and the willingness to share the information is affected by the three aspects of the attitude of sharing, subjective norms and perceived behavioral control. In addition, based on some sources, sharing effectiveness is introduced as an influencing factor.
H1: Audience attitudes toward sharing popular music positively influence popular music sharing intention.
H2: Audience subjective norms towards popular music positively affect popular music sharing intention.
H3: Audience control over pop music sharing positively influences pop music sharing intention.
H4: Audience control over pop music sharing positively influences pop music sharing behavior.
H5: Audience self-efficacy for popular music sharing positively influences popular music sharing intention.
H6: Audience self-efficacy for popular music sharing positively influences popular music sharing behavior.
H7: Audience’s intention to share popular music positively influences popular music sharing behavior.
Based on the content of the Theory of Planned Behavior, this paper hypothesizes that sharing attitude, subjective norms and perceived behavioral control can have a significant positive effect on the willingness to share, while the willingness to share can have a significant positive effect on audience behavior. At the same time, the user’s attitude towards sharing, subjective norms, and perceived behavioral control can indirectly affect audience behavior by influencing the willingness to share corporate marketing information. In this paper, the influencing factor model of popular music audience behavior is constructed as shown in Figure 4.
Influence factors of popular music audience behavior
This paper chooses communication platforms with good communication effect and high audience willingness as the research object, such as NetEase cloud, microblog, B station, etc., and screens the data released by each platform for investigation. The use of questionnaires, the research object to use the communication channels for popular music communication, the questionnaire was issued 400 copies, 356 copies were recovered, and after removing the invalid questionnaires, 320 valid questionnaires were finally obtained, with an effective recovery rate of 80%.
Structural equation modeling consists of two parts: the measurement model and the structural model. The measurement model describes the measurement relationship between observed and latent variables. The structural model describes the structural relationship between latent and potential variables.
In the measurement model,
In structural equation modeling, all observed variables are represented by linear combinations of latent variables. The measurement model can be viewed as a measure of the latent variables in terms of the observed variables, which is very similar to the way in which factor analysis is done, hence the name validation factor analysis, as opposed to traditional exploratory factor analysis.
In the structural model,
From the measurement model and the structural equation model, it is known that in solving the structural equation model, the four coefficient matrices of Λx,Λy,
Reliability test
Reliability analysis is mainly used to test the stability of analysis results and determine the degree of truth or reliability of empirical research results. It is generally believed that a Cronbach’s coefficient above 0.80 indicates very good reliability. In this paper, the overall questionnaire was tested for reliability using SPSS 25.0, and the value of Cronbach’s coefficient for the overall questionnaire was 0.975, indicating that the scale has excellent internal consistency. The reliability test was also conducted for each dimension, and the results showed high reliability for each dimension. The combined reliability (CR) was also estimated and tested as a reliability indicator for testing latent variables. The data showed that the combined reliability CR was greater than the critical value of 0.7, indicating that the internal consistency and reliability of the questionnaire were good.
Validity analysis
Firstly, the variables that influence sharing behavior and the indicators measuring sharing intention and behavior were tested separately. The results show that the KMO value of the sharing behavior influencing factors variable is 0.884, and Bartlett’s test of sphericity is significant at the level of 0.000; the KMO value of the sharing intention and behavior variable is 0.861, and Bartlett’s test of sphericity is significant at the level of 0.000. The test results reveal that the relationship between the variables is improved and the data are more suited for factor analysis.
Aggregation validity
The factor loadings were obtained after rotating the component matrices of the factors influencing the sharing behavior variables and the factors of the sharing intention and behavior variables, and set to show only the factor loadings higher than 0.5, and calculated the AVE value and CR value based on the factor loadings, and the results showed that the AVE value was greater than 0.5, which indicated that the convergent validity of the factors was better, and the individual question items could effectively reflect the factor constructs, and the convergent validity was in line with the requirements.
Distinguishing validity
Distinguishing validity is an index to judge whether the correlation between measurement items from different variables is as small as possible. By judging whether the square root of the AVE value of each variable is greater than the correlation coefficient between that variable and other variables the results are shown in Tables 6 and 7. The results indicate that the discriminant validity of the scale data is satisfactory because the square root of the AVE value of each variable is higher than the correlation coefficient of each measured variable.
The effect of the influence factor is the result of the test result
| Self-efficacy | Controlling force | Subjective specification | Attitude | |
|---|---|---|---|---|
| Self-efficacy | 0.722 | |||
| Controlling force | 0.623 | 0.766 | ||
| Subjective specification | 0.714 | 0.475 | 0.743 | |
| Attitude | 0.554 | 0.444 | 0.672 | 0.761 |
Validity index test results
| Behavior | Intention | |
|---|---|---|
| Behavior | 0.882 | |
| Intention | 0.634 | 0.874 |
In summary, the test indicators meet the statistical requirements, indicating that the data of this survey have good reliability and validity.
This study mainly handles the structural equation modeling analysis through AMOS 24.0. After modeling through AMOS, the structural equation model is used to complete the path test and the goodness-of-fit test of the measurement model and to determine whether the hypotheses are valid or not by observing the significance of the paths or not.
Model fit is the degree of consistency between the constructed theoretical model and the actual data. The results, as shown in Table 8, indicate that the model in this study has a good fit.
Model fitting test
| Fitting index | X2/df | RMSEA | GFI | AGFI | GFI | TLI |
|---|---|---|---|---|---|---|
| Test value | 1.842 | 0.062 | 0.886 | 0.872 | 0.957 | 0.942 |
| Reference value | <3 | <0.08 | >0.80 | >0.80 | >0.80 | >0.80 |
The path coefficients were considered statistically significant at 95% confidence level when CR>2, p<0.05. The significance results are shown in Table 9, from which it can be seen that all the paths except the path from control to intention and the path from control to behavior indicate significant correlation (p>0.001),indicating that hypotheses H3 and H5 are not valid.
Standard path coefficient and significance
| Path relation | Standardized path coefficient | Standard error S.E | Critical ratio C.R. | P | Validation |
|---|---|---|---|---|---|
| Intention <--- Attitude | 0.293 | 0.129 | 2.182 | 0.025* | Set up |
| Intention <--- Subjective specification | 0.409 | 0.121 | 3.218 | 0.001** | Set up |
| Intention <---Controlling force | -0.178 | 0.08 | -1.998 | 0.051 | Out of reach |
| Intention <---Self-efficacy | 0.412 | 0.149 | 2.661 | 0.006** | Set up |
| Behavior <---Controlling force | 0.033 | 0.109 | 0.322 | 0.752 | Out of reach |
| Behavior <---self-efficacy | 0.476 | 0.188 | 2.458 | 0.016 | Set up |
| Behavior <--- Intention | 0.373 | 0.106 | 5.03 | *** | Set up |
Note: *** indicates significant at 0.001 level of significance
Based on the theory of planned behavior, the influencing factors of the audience’s sharing behavior of popular music are studied, and according to the results of the path coefficient, there is a significant correlation between the audience’s intention to share pop music and their behavior. However, the path coefficient of self-efficacy (0.476) was higher than that of other factors, indicating that self-efficacy was the main factor influencing the audience’s sharing of pop music, followed by the path coefficient of subjective norms, which was only higher than self-efficacy, indicating that social support was positively correlated with individual information sharing intention.