Study on the Application of New Media Technology in the Digital Protection and Dissemination of Traditional Drama-Taking Sichuan Opera as an Example

The art of Sichuan opera originated from the folk art form in Sichuan, and has gradually formed a unique performing style and artistic characteristics through historical precipitation and inheritance. It is well known for its passionate singing, exquisite performance skills and rich and diverse performance forms [1-4]. The repertoire of Sichuan opera is rich and varied, the storyline is vivid and interesting, and the acting skills of the actors are extremely demanding, requiring the mastery of the basic skills of singing, reciting, acting and fighting [5-7]. The uniqueness of Sichuan opera lies in its special singing voice and innovative performance forms, making it a treasure of Chinese opera art [8-9].

However, with the change of the times and the development of the society, the traditional drama represented by the art of Sichuan opera is facing many challenges and dilemmas. On the one hand, due to the process of urbanization and the rise of new media, the audience’s aesthetic needs have changed, and the inheritance of traditional Sichuan opera art among the younger generation faces difficulties [10-13]. On the other hand, the inheritance of the art of Sichuan opera requires long-term professional training and experience accumulation, but the traditional Sichuan opera troupes lack of inheritance of talent loss is serious. These problems make the inheritance and development of the art of Sichuan opera extremely difficult [14-17]. In the era of new media technology, digitalization has brought new opportunities for the preservation and dissemination of most traditional cultures, including Sichuan opera and other dramas [18-20].

The advantages of digital preservation and dissemination of traditional culture are mainly reflected in the following: digital preservation and dissemination not only enables traditional culture to transcend the limitations of time and space, no longer confined to a specific region and time [21-22], but also enables traditional culture to be more rich and diversified, and attracts more audiences by presenting it through audio, video and other forms, and facilitates interactions and interactions to enhance the effect of preservation and dissemination. It can also facilitate interaction and interaction to involve the audience and enhance the effect of protection and dissemination [23-26].

Literature [27] examined the digital preservation and dissemination of non-heritage. Taking martial arts as an example, it points out that there are problems such as imperfect development, and proposes measures to improve the protection and dissemination of NRM with digital preservation. Literature [28] describes the study of digital dissemination of NRM traditional culture in the meta-universe as a future trend in the integration of cultural heritage and digital technology, which injects new vitality into cultural heritage and brings more opportunities and challenges. Based on analyzing the current situation of digital games, literature [29] proposed digital games as a way to protect and disseminate Chinese NRM. And based on the light game design of WeChat platform, it demonstrates the effective integration of non-heritage elements game design by taking Hangzhou traditional food culture as an example. Literature [30] explored the application of new media interactive art in the protection and dissemination of traditional culture. Through literature collection and data analysis, it reveals that the application of new media interactive art is conducive to the restoration of traditional cultural resources and can preserve and disseminate cultural resources. Literature [31] describes the application of digital virtual image technology in the preservation and dissemination of cultural heritage. It is not only conducive to increasing the economic value of cultural heritage, but also expands the utilization space of cultural heritage, and is able to realize the protection and transmission of cultural heritage. Literature [32] explored the protection of cultural heritage under VR, taking cultural heritage sites as an example. Virtual reality intervention in cultural heritage digitization was mentioned, expanding the types of images in visual culture. Literature [33] proposes the use of digital twin technology to build a digital village scene based on the many challenges faced by ethnic-specific non-heritage. This approach breaks the physical space limitation of non-heritage dissemination, which is conducive to the protection and development of non-heritage, thus helping rural revitalization. Literature [34] developed a digital platform for museums. The platform is implemented and demonstrated in an interactive virtual tour of the museum, aiming to create a holistic interactive experience for both physical and web visitors. Literature [35] outlines the digitization of cultural heritage information resources and the construction of a resource-sharing platform for digital heritage conservation and dissemination, and develops a prediction of the direction of digitization.

In summary, digitization can play an important role in the protection, dissemination and development of traditional culture, but Sichuan opera and other dramas, which are also traditional cultures, are not reflected in the protection and dissemination of traditional culture by digitization, exposing the fact that Chinese drama, as represented by Sichuan opera, is gradually going into decline. This is not only a loss in the field of theater, but also a loss in the excellent traditional culture of China, and a loss in the world cultural heritage, so the research on the digital preservation and dissemination of Sichuan opera and other traditional dramas is of great historical significance.

The article firstly gives an overview of the relevant theories of complex networks, then on the basis of the SCIR model, comprehensively considering the regulation of traditional drama dissemination by the relevant departments and the phenomenon of secondary dissemination by immunized users, it proposes the direct immunization probability for the influence of social factors on traditional drama dissemination, and also proposes the transition probability of the transition from immunized to contacted state based on the social reinforcement effect, and constructs a model of traditional drama dissemination based on the DR- SCIR-based single-layer social network traditional drama transmission model. Subsequently, we will analyze the static topological characteristics of the social network using the complex network technology, and select the typical Sichuan local Sichuan opera topic “Shu Feng Ya Yun” as the research material for traditional drama dissemination, construct the connection matrix according to the connection between the topic and the users, and conduct the node importance metrics according to the connection between the nodes of the topic and the topic of the relevant individuals and official users in the dissemination of the information. According to the connection between the topic and users, the connection matrix is constructed, and the nodes of individuals, official users and topics in the information dissemination of the topic are characterized by the node importance index, and the topological characteristics of the whole social network are analyzed. Finally, the model of this paper is simulated to analyze the influence of opinion leaders, user viewpoint interaction and topic interest on traditional drama communication and the influence of topic interest on traditional drama communication.

2

Introduction to complex networks

A complex network is a network consisting of a large number of interconnected nodes whose connections and structure usually exhibit a certain degree of complexity and non-uniformity. In general, when representing a network in the form of a graph, each node represents an entity or individual in the network, while edges represent connections or relationships between nodes. A network containing N vertex and M edges can be abstracted as a graph G = (V, E), with the number of vertices N = |V| and the number of edges noted as M = |E|, where V = {v₁, v₂, v₃, …, v_N}, the set of edges $E \subseteq {(ν_{i}, ν_{j}); ν_{i}, ν_{j} \in V, i \neq j}$ $$E \subseteq \left\{ {({\nu_i},{\nu_j});{\nu_i},{\nu_j} \in V,i \ne j} \right\}$$. According to whether the edges in the graph are directed or not and whether they are weighted or not, the relationship between the four types of graphs is shown in Fig. 1.

The adjacency matrix is a two-dimensional array in which the rows and columns correspond to the nodes in the network, and the elements of the matrix indicate whether there are connections or edges between the nodes. For an undirected graph, when there is an edge between node i and node j, the elements of the adjacency matrix at (i, j) and (j, i) have the value l. For a directed graph, when there is a directed edge between node i and node j, the element of the adjacency matrix at (i, j) has the value l. If the edges in the network have a weight, the value of the matrix element can be set to the corresponding weight value.

The adjacency matrix A = (a_ij) of Fig. G = (V, E) is a matrix of N × N. The elements on row i and column j are defined as follows: 1)

Weighted directed graphs (1) $a_{i j} = {\begin{matrix} w_{i j}, & If there are edges with weights w_{i j} that point from vertex i to vertex j; \\ 0, & If there is no edge from vertex i to vertex j . \end{matrix}$ $${a_{ij}} = \left\{ {\begin{array}{*{20}{c}} {{w_{ij}},}&{{\text{If there are edges with weights }}{w_{ij}}{\text{ that point from vertex }}i{\text{ to vertex }}j{\text{;}}} \\ {0,}&{{\text{If there is no edge from vertex }}i{\text{ to vertex }}j{\text{.}}} \end{array}} \right.$$

2)

Weighted undirected graphs (2) $a_{i j} = {\begin{matrix} w_{i j}, & If the edge between vertex i and vertex j has a value of w_{i j}; \\ 0, & If there is no edge between vertex i and vertex j . \end{matrix}$ $${a_{ij}} = \left\{ {\begin{array}{*{20}{c}} {{w_{ij}},}&{{\text{If the edge between vertex }}i{\text{ and vertex }}j{\text{ has a value of }}{w_{ij}}{\text{;}}} \\ {0,}&{{\text{If there is no edge between vertex }}i{\text{ and vertex }}j{\text{.}}} \end{array}} \right.$$

3)

Unprivileged directed graphs (3) $a_{i j} = {\begin{matrix} 1, & If there are edges from vertex i to vertex j; \\ 0, & If there is no edge from vertex i to vertex j . \end{matrix}$ $${a_{ij}} = \left\{ {\begin{array}{*{20}{c}} {1,}&{{\text{If there are edges from vertex }}i{\text{ to vertex }}j{\text{;}}} \\ {0,}&{{\text{If there is no edge from vertex }}i{\text{ to vertex }}j{\text{.}}} \end{array}} \right.$$

4)

Unprivileged undirected graphs (4) $a_{i j} = {\begin{matrix} 1, & If there is an edge between vertex i and vertex j; \\ 0, & If there is no edge between vertex i and vertex j . \end{matrix}$ $${a_{ij}} = \left\{ {\begin{array}{*{20}{c}} {1,}&{{\text{If there is an edge between vertex }}i{\text{ and vertex }}j{\text{;}}} \\ {0,}&{{\text{If there is no edge between vertex }}i{\text{ and vertex }}j{\text{.}}} \end{array}} \right.$$

In computing, the selection of an appropriate representation depends on the size of the network, the degree of sparsity, the mode of access, and the operations required, so it is important to select the appropriate method for representing and processing the network on a case-by-case basis.

2.1

Basic Network Topology Properties

2.1.1

Degree

In graph theory, degree is the number of connections between a node (or vertex) and other nodes (or vertices). Specifically, the degree of a node is the number of edges directly connected to it [36]. Generally, k_i is used to denote the degree value of node i, and $k_{i}^{i n}$ $$k_i^{in}$$ and $k_{i}^{o u t}$ $$k_i^{out}$$ denote the outgoing and incoming degrees of node i, respectively. The average of the degree values of all nodes in the network is called the average degree of the network and is denoted as k.

For an undirected network consisting of N nodes, the degree of node i is calculated as: (5) $k_{i} = \sum_{j = 1}^{N} a_{i j}$ $${k_i} = \sum\limits_{j = 1}^N {{a_{ij}}}$$

For a directed network consisting of N node, the degree of node i is given by: (6) $k_{i} = k_{i}^{i n} + k_{i}^{o u t}$ $${k_i} = k_i^{in} + k_i^{out}$$

Among them. (7) $k_{i}^{i n} = \sum_{j = 1}^{N} a_{j i}$ $$k_i^{in}\: = \sum\limits_{j = 1}^N {{a_{ji}}}$$ (8) $k_{i}^{o u t} = \sum_{j = 1}^{N} a_{i j}$ $$k_i^{out}\: = \sum\limits_{j = 1}^N {{a_{ij}}}$$

For a network consisting of N node. (9) $〈 k 〉 = \frac{1}{N} \sum_{i = 1}^{N} k_{i}$ $$\langle k\rangle = \frac{1}{N}\sum\limits_{i = 1}^N {{k_i}}$$

2.1.2

Average path length

The average path length is the average of the shortest path lengths between all pairs of nodes in the graph. To compute the average path length generally follow these steps:

Step 1: For each pair of nodes in the graph, calculate the shortest path length between them. This can be done by using the shortest path algorithm in graph theory.

Step 2: Sum all the shortest path lengths to get the total path length.

Step 3: Divide the total path length by the number of node pairs to get the average path length: (10) $L = \frac{1}{\frac{1}{2} N (N - 1)} \sum_{i > j} d_{i j}$ $$L = \frac{1}{{\frac{1}{2}N(N - 1)}}\sum\limits_{i > j} {{d_{ij}}}$$

Where N denotes the number of nodes in the network and d_ij denotes the length of the shortest path between node i and node j. In addition, the longest of the shortest paths between any two nodes in the network is denoted as D and is called the diameter of the network, i.e: (11) $D = \max_{i, j} d_{i j}$ $$D = {\max_{i,j}} \ {d_{ij}}$$

2.1.3

Clustering coefficients

The clustering coefficient is a measure of the degree of aggregation of nodes in a network. The local clustering coefficient portrays the probability that any two neighbors of a node are also neighbors of each other, and is defined as the ratio of the number of closed triangles formed between the neighbors of a node to the number of connectors between all possible pairs of neighbors of that node.

For an unweighted undirected network with the number of nodes N, the clustering coefficient C_i for node i is calculated as: (12) $C_{i} = \frac{E_{i}}{(k_{i} (k_{i} - 1)) / 2} = \frac{2 E_{i}}{k_{i} (k_{i} - 1)}$ $${C_i} = \frac{{{E_i}}}{{({k_i}({k_i} - 1))/2}} = \frac{{2{E_i}}}{{{k_i}({k_i} - 1)}}$$

where E_i represents the number of edges that actually exist between the k_i neighboring nodes of node i.

The global tight clustering coefficient is used to measure the degree of clustering of the entire network and is defined as the average of the local tight clustering coefficients of all nodes: (13) $C = \frac{1}{N} \sum_{i = 1}^{N} C_{i}$ $$C = \frac{1}{N}\sum\limits_{i = 1}^N {{C_i}}$$

2.1.4

Degree distribution

The degree distribution refers to the distribution of degrees (i.e., the number of edges connected to a node) of individual nodes in a network. For an undirected network consisting of N node, P(k) is: (14) $P (k) = \frac{n_{k}}{N}$ $$P(k) = \frac{{{n_k}}}{N}$$

where n_k is the number of nodes with degree value k.

2.1.5

Degree of relevance

The degree distribution only provides the distribution of node degrees and does not address the connectivity between nodes. Therefore, we will next examine the correlation between node degrees in a network, i.e., degree correlation. Degree correlation is a concept that describes the correlation between the degrees of nodes in a network. It measures which types of nodes in the network tend to be connected by nodes with higher degrees of connectivity and which types of nodes tend to be connected by nodes with lower degrees of connectivity. Degree correlation is usually quantified by the degree correlation coefficient.

2.1.6

Node Importance

The importance of a node is the degree to which a node in a network influences the structure and function of the network. The importance of a node can be measured by a variety of metrics that typically reflect the degree of centrality, influence, or role of the node in the network.

Degree centrality is the number of connections (degrees) a node has in the network. For a node with a degree value of k_i in a network with a number of nodes of N the normalized degree centrality value of the node is: (15) $D C_{i} = \frac{k_{i}}{N - 1}$ $$D{C_i} = \frac{{{k_i}}}{{N - 1}}$$

Proximity centrality is the reciprocal of the average shortest path from a node to other nodes. The proximity centrality of node i is generally denoted by CC_i, i.e.: (16) $C C_{i} = \frac{1}{d_{i}} = \frac{N}{\sum_{j = 1}^{N} d_{i j}}$ $$C{C_i} = \frac{1}{{{d_i}}} = \frac{N}{{\sum\limits_{j = 1}^N {{d_{ij}}} }}$$

Where d_ij denotes the distance from node i to node j.

The median centrality is the frequency with which a node acts as a bridge in the shortest path in the network. For a network with number of nodes N, the meso-centrality of node i is: (17) $B C_{i} = \frac{2}{N^{2} - 3 N + 2} \sum_{s \neq i \neq t} \frac{n_{s t}^{i}}{g_{s t}}$ $$B{C_i} = \frac{2}{{{N^2} - 3N + 2}}\sum\limits_{s \ne i \ne t} {\frac{{n_{st}^i}}{{{g_{st}}}}}$$

where g_st is the number of shortest paths from node s to node t, and $n_{s t}^{i}$ $$n_{st}^i$$ is the number of shortest paths passing through node i out of the g_st shortest paths from s to node t.

2.2

Complex network modeling

2.2.1

Rule networks

Regular networks are a special type of complex networks with highly ordered and regular connection patterns between nodes. Three kinds of coupling networks are shown in Fig. 2 (Figs. a~c are global coupling network, nearest neighbor coupling network and star coupling network, respectively).

2.2.2

Stochastic networks

The counterpart of the regular network is the random network. In this model, each pair of nodes in the network is connected with a certain probability, and the connections are independently and identically distributed. Random networks are generated in two ways: 1)

ER random graph with fixed number of edges G(N, M).

Step 1, Determine the number of nodes N: First determine the number of nodes in the network N.

Step 2, Determine the number of edges M: Determine the required number of edges M i.e. the total number of connected edges in the network.

Step 3, Randomly connect edges: Randomly select two nodes u and v from the network and if there is no edge between u and v and they are not the same node, add an edge between them. Keep repeating the following steps until there are M edges in the graph.

2)

ER random graph with fixed probability of connected edges G(N, p).

Step 1, Determine the number of nodes N: First determine the number of nodes in the network N.

Step 2, Determine the connecting edge probability p: Determine the desired connecting edge probability p, which denotes the probability that an edge exists between any two nodes.

Step 3, Select the pair of nodes that do not have a connected edge, generate a random number and if a random number falls between 0 and p, add an edge between them and the algorithm ends when all pairs of nodes are considered and possibly connected [37].

Because the random network has randomness in the generation, so G(N, M) algorithm or G(N, p) algorithm, even if the input parameters are the same to get the structure of the network there are differences, the number of nodes N = 10, the number of edges M = 10, the figure a, b, c, respectively, represents three times the generation of a different structure of the random graph shown in Figure 3. When the number of nodes N=10, the probability of connecting edges p=0.3, the graphs a, b, and c represent the random graphs of different structures generated three times as shown in Fig. 4.

2.2.3

Small World Network

The main construction algorithms for small-world networks are the Watts-Strogatz (WS) model and the Newman-Watts (NW) model. The construction algorithm for a WS small-world network is to create a ring network with N node, each node being connected to its k neighboring nodes. For each edge, randomly reconnect to another node in the network with probability p, which may be a neighbor of the original node or any randomly chosen node. This results in a network with small-world properties, i.e., it retains the local connectivity of regular networks and the short path lengths of random networks. The algorithm for the construction of a NW small-world network is to generate a ring network with N nodes, each node is connected to its neighboring K/2 (K is even) nodes, and edges are added with probability p between randomly selected N*K/2 pairs of nodes, with no re-edges and no self-loops.

2.2.4

Scale-free networks

Scale-free networks are a commonly used model of scale-free networks. Scale-free networks have a power law distribution of node degree distribution, while most nodes are less connected, highly clustered, nodes in the network tend to form dense subgroups or communities, and the average shortest path between any two nodes in the network is shorter.

The algorithm for the construction of BA network is as follows: 1)

Initially, m node is generated and they are connected to form an initial graph.

2)

Each time a new node is added, the node connects to an existing node with a probability that is proportional to the degree of the node. Specifically, the probability that a new node connects to an existing node is related to the degree k of that node, i.e., the probability of connecting to a node i is: (18) $Π (i) = \frac{k_{i}}{\sum_{j} k_{j}}$ $$\Pi (i) = \frac{{{k_i}}}{{\sum\limits_j {{k_j}} }}$$

Where k_i is the degree of node i and $\sum_{j} k_{j}$ $$\sum\nolimits_j {{k_j}}$$ is the sum of degrees of all existing nodes. 3)

Repeat step 2 until the network reaches the desired size.

3

Social network-based traditional drama communication model

3.1

SCIR Traditional Theater Communication Model

In order to study the propagation law of traditional drama information, the researchers introduced the contact state C(Contacted) on the basis of SIR model, constructed the SCIR traditional drama propagation model, and redefined the meanings of the four states of network users: 1)

Susceptibility state S, which represents that the user has not yet been exposed to the information.

2)

The contact state C, representing the state in which the user has acquired traditional theater information but has not yet decided whether or not to disseminate the information.

3)

Dissemination state I, representing the state that the user has disseminated the traditional theater.

4)

Immunization state R, representing the state in which the user is already aware of the information but will not disseminate it.

Assuming that the total number of users remains constant during the propagation of the traditional theater, the total number of nodes in the corresponding SCIR network N remains constant, and

That is, at any moment t, S(t) + C(t) + I(t) + R(t) = N is satisfied.

The differential equation of the SCIR propagation model is: (19) ${\begin{matrix} \frac{d S (t)}{d t} = - (P_{S C} + P_{S I}) S (t) I (t) \\ \frac{d S (t)}{d t} = P_{S C} S (t) I (t) - P_{C R} C (t) - P_{C I} C (t) \\ \frac{d S (t)}{d t} = P_{C I} C (t) + P_{S I} S (t) I (t) - P_{I R} I (t) \\ \frac{d S (t)}{d t} = P_{C R} C (t) + P_{I R} I (t) \end{matrix}$ $$\left\{ {\begin{array}{*{20}{c}} {\frac{{dS(t)}}{{dt}} = - ({P_{SC}} + {P_{SI}})S(t)I(t)} \\ {\frac{{dS(t)}}{{dt}} = {P_{SC}}S(t)I(t) - {P_{CR}}C(t) - {P_{CI}}C(t)} \\ {\frac{{dS(t)}}{{dt}} = {P_{CI}}C(t) + {P_{SI}}S(t)I(t) - {P_{IR}}I(t)} \\ {\frac{{dS(t)}}{{dt}} = {P_{CR}}C(t) + {P_{IR}}I(t)} \end{array}} \right.$$

where P_SC denotes the probability of being exposed to a traditional drama but not deciding whether to disseminate that traditional drama or not, i.e., the internal exposure rate. P_SI denotes the probability of being exposed to a traditional drama and immediately disseminating that traditional drama, i.e., the direct forwarding rate. P_CI denotes the probability that a contact state user disseminates with the context, i.e., the indirect forwarding rate. P_CR indicates the probability that a contact state user does not spread and turns into an immunizer, i.e., the indirect immunization rate. P_IR denotes the probability that the spreader is no longer interested in traditional drama and thus becomes an immunizer, i.e., the forwarding immunization rate. Where each probability satisfies 0 ≤ P_SC, P_SI, P_CI, P_CR, P_IR ≤ 1.

The state transfer rule for the process of spreading with a situation is defined as follows: 1)

After being exposed to the traditional drama, a part of the susceptible state users will transform to the contact state C with probability P_SC, and the other part will transform to the propagation state I with probability P_SI.

2)

After exposure to the traditional drama, a part of the users in the contact state will transform to the propagation state I with probability P_CI, and the other part will transform to the immunization state R with rate P_CR.

3)

Propagation state I transitions to immunization state R with probability P_IR

4)

Once a node is in immune state R, then its state will not change.

3.2

Design of mechanisms for social enhancement effects

The social reinforcement effect is a phenomenon in which an individual’s repeated prompting by peers prior to making an opinion adoption or behavioral decision can have a cumulative effect on the user, thus influencing the final decision. Social psychology shows that, on the one hand, information that conforms to people’s subjective desires, impressions or prejudices is easily spread. On the other hand, information that triggers shock and panic among the public is also prone to be widely disseminated if it is not intervened in a timely manner. In view of the different characteristics of traditional drama, Wang Hui et al. classified the social reinforcement effect into positive social reinforcement effect and negative social reinforcement effect.

This paper imposes a social reinforcement effect on the immunized users, and at the same time, considering the actual situation of traditional drama transmission, sets the initial conditions of the social reinforcement effect, so that when the immunized users receive the same traditional drama information from their friends for a certain number of times, the immunized users may get out of the immunized state and thus re-transmit the traditional drama, and the probability of transmission for the positive social reinforcement effect increases with the number of contacts until the upper limit is reached. The probability of spreading for positive social reinforcement effect will increase with the number of contacts until it reaches the upper limit of the spreading probability. The probability of a negative social reinforcement effect decreases as the number of exposures increases until it decreases to zero.

It is assumed that the probability that the social reinforcement effect causes the immune user to shift to the contact state is p(m), and m represents the cumulative amount, i.e., the number of times the engagement is received. The threshold value of the cumulative amount m is also considered, and p(m) exists when the cumulative number of times m ≥ 3 is present, then p(m) is represented as: (20) $p (m) = | (λ - C U) \cdot e^{- | r | (m - 3) / 1000} + C U |, m \geq 3$ $$p(m) = |(\lambda - CU) \cdot {e^{ - |r|(m - 3)/1000}} + CU|,\:m \ge 3$$

where p(3) = λ denotes the initial propagation probability. U is the upper limit of propagation probability, considering that the immune users themselves have a certain resistance to the dissemination of traditional drama information, even under the action of social strengthening effect, the probability of their re-forwarding will not be very high, so the upper limit of propagation probability is U ∈ (0, 0.3]. r represents the factor of social strengthening effect, C is a function of r, and r ≥ 0 represents the positive effect of social strengthening effect, which is represented by r⁺, and at this time C = 1. r < 0 represents the negative impact of the social reinforcement effect, denoted by r⁻ this time C = 0.

In summary, the social reinforcement effect p(m) for both positive and negative traditional theater can be expressed as follows: (21) $p (m) = {\begin{matrix} λ e^{- | r | m - 3 / | 1000} & r < 0, m \geq 3 \\ (λ - U) e^{- | r | m - 3 / | 1000} + U & r \geq 0, m \geq 3 \end{matrix}$ $$p(m) = \left\{ {\begin{array}{*{20}{c}} {\lambda {e^{ - |r|m - 3/|1000}}}&{r < 0,m \ge 3} \\ {(\lambda - U){e^{ - |r|m - 3/|1000}} + U}&{r \ge 0,m \ge 3} \end{array}} \right.$$

3.3

Traditional Drama Control Strategies Based on Direct Immunization

3.3.1

Theory of immune strategies

Common immunization strategies in complex network theory include the following three: random immunization, target immunization, and acquaintance immunization.

Random immunization, also known as uniform immunization, is based on the idea that a certain number of nodes are randomly selected as immune nodes in the network. For scale-free networks, the immunization threshold g_ε for random immunization is: (22) $g_{c} = 1 - \frac{< k >}{λ < k^{2} >}$ $${g_c} = 1 - \frac{{ < k > }}{{\lambda < {k^2} > }}$$

where λ denotes the propagation rate. From Eq. As the network size increases, the immunization critical value g_c → 1.

The idea of target immunization, also known as selection immunization, is to select the nodes with large degree in the network to be immunized, and when the nodes with large degree are immunized, the edges connected to them will be removed from the network, which makes the propagation pathway greatly reduced [38]. For scale-free networks, the immunization threshold g_ε for target immunization is: (23) $g_{c} \propto e^{- 2 / m λ}$ $${g_c} \propto {e^{ - 2/m\lambda }}$$

Acquaintance immunity, also known as near-neighbor immunity, has an immunity threshold g_c of acquaintance immunity for scale-free networks: (24) $g_{c} = 1 - \sum_{k} P (k) v_{p c}^{k - 1} (v_{p c} + p k μ_{p c})$ $${g_c} = 1 - \sum\limits_k P (k)v_{pc}^{k - 1}({v_{pc}} + pk\:{\mu_{pc}})$$ (25) $μ_{p c} = p < \frac{e^{- p / k}}{k} >$ $${\mu_{pc}} = p < \frac{{{e^{ - p/k}}}}{k} >$$

Where, P(k) represents the degree distribution of the node. ν_μκ represents the probability that no neighbor node of a node with degree k is selected as an immune node in Np iterations. p represents the proportion of randomly selected nodes at the initial time.

3.3.2

Direct immunization strategies

In this paper, direct immunization is imposed on susceptible users based on random immunization. The infection mechanism of direct immunization is denoted as: (26) $S + I \overset{P_{s s}}{\to} R + I$ $$S + I\xrightarrow{{{P_{ss}}}}R + I$$

This paper summarizes the social factors that influence the effectiveness of direct immunization into the following categories: 1)

The intensity of intervention by relevant departments

When the relevant departments take restrictive measures on the dissemination of traditional drama, the greater the intensity of intervention means the higher the importance of traditional drama, which prompts more network users to believe in the real information, which leads to the existence of more immune users in the initial stage when direct immunization begins to work.

2)

Credibility of real information

The credibility of the government and other relevant departments as the source of real information is an important guarantee of the credibility of information, and the media, as the half-stage of information dissemination, is the medium linking the government and the masses, which is the catalyst for enhancing the credibility of information.

In order to control for traditional drama transmission, this paper intervenes in the model using social factors and takes into account the effect of noise interference by defining the direct immunization rate P_SR as: (27) $P_{S R} = {\begin{matrix} 0 & 0 \leq t < T \\ α β (1 - e^{- \frac{β}{α}}) & t \geq T \end{matrix}$ $${P_{SR}} = \left\{ {\begin{array}{*{20}{c}} 0&{0 \le t < T} \\ {\alpha \:\beta (1 - {e^{ - \frac{\beta }{\alpha }}})}&{t \ge T} \end{array}} \right.$$

Where α represents the intervention strength, i.e., the density of nodes that are in the immunization state R at the initial moment of adopting the immunization strategy, α ∈ [0, 1]. β represents the credibility of the information, and β ∈ [0, 1]. T represents the intervention time of the relevant authorities. e^−β/a represents the interference of noise, which is proportional to the intervention strength α and inversely proportional to the credibility of the information β.

3.4

DR-SCIR Social Network Traditional Drama Communication Model

Considering the factor of user activity in social networks, certain users may temporarily disengage from the network without always staying online, which triggers the problem of real-time changes in the number of online users in social networks, this paper introduces the concepts of the addition rate and the offline rate. Among them, the addition rate is defined as the percentage of the number of newly online users in the network to the total number of users at a certain moment, and the offline rate is defined as the percentage of the number of offline users to the total number of users at a certain moment. And, considering the influence of social reinforcement effect on the spread of traditional drama in social networks, this paper imposes social reinforcement effect for immunized users. Meanwhile, considering the existence of direct immunization, the relevant departments can control the spread of traditional drama in social networks by taking certain measures, such as releasing real information, to regulate the spread of traditional drama and guide the direction of traditional drama. Specifically, it is to act the social reinforcement effect on the immune user, when the immune user repeatedly receives the traditional drama with probability P_RC to transform into the contact state [39]. The direct immunization effect is applied to the infected users, so that when the authorities release the real information, the infected users will turn to the immune state with probability P_SR when they first contact the traditional drama information, so as to control the spread of traditional drama and reduce the social impact of negative traditional drama.

Taking the above considerations into account, the DR-SCIR traditional drama propagation model is established, and the DR-SCIR traditional drama propagation model is shown in Figure 5.

Where, A denotes the rate of addition of susceptible state users in the network. μ denotes the offline rate of each type of user. P_SR denotes the probability that exposure to a traditional drama will not spread that traditional drama, defined as the direct immunization rate. P_RC denotes the probability of recirculation by an immunized user. The individual state transfer probabilities of the model satisfy 0 ≤ A, μ, P_SC, P_SR, P_CI, P_CR, P_IR ≤ 1.

Since there are user addition rate A and offline rate μ in the network, the number of real-time online users in the network is: (28) $| 1 + A - μ | N (t) = S (t) + C (t) + I (t) + R (t)$ $$|1 + A - \mu |N(t) = S(t) + C(t) + I(t) + R(t)$$

Therefore, the differential equation of the DR-SCIR traditional theater propagation model can be expressed as: (29) ${\begin{matrix} \frac{d S (t)}{d t} = A - (P_{S C} + P_{S I} + P_{S R}) S (t) I (t) - μ S (t) \\ \frac{d S (t)}{d t} = P_{S C} S (t) I (t) + P_{R C} R (t) - (P_{C R} + P_{C I} + μ) C (t) \\ \frac{d S (t)}{d t} = P_{C I} C (t) + P_{S I} S (t) I (t) - P_{R} I (t) - μ I (t) \\ \frac{d S (t)}{d t} = P_{C R} C (t) + P_{S R} S (t) I (t) + P_{I R} I (t) - P_{R C} R (t) - μ R (t) \end{matrix}$ $$\left\{ {\begin{array}{*{20}{c}} {\frac{{dS(t)}}{{dt}} = A - ({P_{SC}} + {P_{SI}} + {P_{SR}})S(t)I(t) - \mu S(t)} \\ {\frac{{dS(t)}}{{dt}} = {P_{SC}}S(t)I(t) + {P_{RC}}R(t) - ({P_{CR}} + {P_{CI}} + \mu )C(t)} \\ {\frac{{dS(t)}}{{dt}} = {P_{CI}}C(t) + {P_{SI}}S(t)I(t) - {P_R}I(t) - \mu I(t)} \\ {\frac{{dS(t)}}{{dt}} = {P_{CR}}C(t) + {P_{SR}}S(t)I(t) + {P_{IR}}I(t) - {P_{RC}}R(t) - \mu R(t)} \end{array}} \right.$$

The initial values of the model are: (30) $S_{k} (0) = 1 - C_{k} (0) - I_{k} (0) - R_{k} (0) > 0$ $${S_k}(0) = 1 - {C_k}(0) - {I_k}(0) - {R_k}(0) > 0$$

Of these, C_k(0), I_k(0), R_k(0) ≥ 0.

4

Social network topology characterization

4.1

Data preparation and processing

There are a lot of reports about “Shu Feng Ya Rhythm” on the Internet, so we only collect data on June 1, 2023 here. Secondly, this study will mainly rely on the relevant data from the following networks: the public opinion information network of news media in the social network platforms, which is mainly crawled by the top-ranked news media. Forums and community websites in public network platforms, mainly Sina Weibo, Today’s Headlines, etc. These official microblogs will also reprint and comment on relevant hot topics on the Internet. The official microblog users and their participation in topics and time are shown in Table 1. The excerpts in the table are the official microblog users and their participation in topics and time in one time period.

Table 1.

Weibo official users and their participation topics and time

User name	Topic	Time
Sichuan daily	Rhymes	6.1
Lunar period	Rhymes	6.1
People’s data	Rhymes	6.1
Sichuan favorite	Rhymes	6.1
Yu Yang intellectual property	Rhymes	6.1
Climbing daily	Rhymes	6.1
People’s Daily	Rhymes	6.1
Sichuan local treasure	Rhymes	6.1
Sichuan radio station	Rhymes	6.1
Daily up	Rhymes	6.1
Live in China	Rhymes	6.1
Sichuan TV	Rhymes	6.1
Ancient point	Rhymes	6.1

In this paper, the microblogging users who participate in the discussion of “Shu Feng Ya Yun” program topics are regarded as the nodes in the network, and the microblogging information dissemination process is regarded as the interconnection relationship between nodes through the forms of liking, commenting and retweeting between the users. After that, we searched for microblogging topics and established the relevant neighbor matrix and organized it. Immediately after that, we start to analyze the overall social network structure as well as centrality, structural holes, and cohesive subgroups.

4.2

Overall analysis of social network structure

From the “Shu style and elegant rhyme” program related topics selected 50 high attention, discussion number of topics, after deleting duplicates and low degree of relevance of the user based on the final determination of the 20 events concerned nodes, the relationship between the data as a link, and to construct a 0-1 directed adjacency matrix adjacency matrix, 0 indicates that the two users topic There is no association relationship, and 1 indicates that there is an association relationship between two user topics. Then the adjacency matrix is imported into the social network analysis software for metrics measurement. The results of visualizing the social network structure of Shu Feng Ya Yun topics and users are shown in Figure 6. As can be seen from the figure, the connection between Shu Feng Ya Yun topics and users is intricate, yet closely connected. It is characterized by a network of nodes and complex relationships. The official media in the key center position are People’s Daily, Punch News, China Information Network, Panzhihua Daily, etc. The topics in the key center position are topics such as Shu Feng Ya Rhyme, Behind Shu Feng Ya Rhyme Out of the Circle, and Why Shu Feng Ya Rhyme can be on fire. From this, we are able to see that there is a close connection with many topics as well as the related user media, indicating the total amount of traditional drama of information dissemination and the formation of many topics.

Next, we will look at the overall network structure indicators of social network communication. The network structure indicators of Shu Feng Ya Yun topic information dissemination are shown in Table 2. Measured by social network analysis software, the network density of Shu Feng Ya Yun topic social network dissemination is 0.03925, with a total of 645 connecting lines connected by 50 topics and users’ attention nodes, with an average distance of 2.5 and a clustering coefficient of 0.362. The results show that, as a whole, indicate that the interaction between the various nodes in the dissemination of information in social networks is relatively low, and the overall aspect of the cohesive situation needs to be strengthened. The meaning of the average distance is the shortest way between two nodes can pass through no more than three nodes and establish a communication relationship with them, the nodes have the ability to communicate with each other easily. From the cohesive subgroup density data, the composition of network information dissemination does not show the existence of high cohesive groups. It can be seen that the current social network information dissemination is easier to bring efficient information dissemination and effective communication.

Table 2.

Topic information dissemination network structure index

Index	Density	No.of Ties	Mean distance	Clustering coefficient
Result	0.03925	645.0000	2.5	0.362

4.3

Centrality analysis

4.3.1

Degree Centrality

Shu Feng Ya Yun topic social network intermediate centrality indexes (excerpts of the first 10) are shown in Table 3. The data shows that in terms of degree centrality, the People’s Daily has the highest in-degree value, and the Central Committee of the Communist Youth League, Sichuan Satellite Television, and Surfing News have higher in-degree values, which indicates that these official users are paid more attention to by other users in the information dissemination network, and they belong to the core node users who have a large dissemination influence and a wide dissemination audience. In terms of the center of the out-degree value, we can see that the out-degree value of Sichuan Satellite TV, Panzhihua Daily, Sichuan Daily, etc. is higher, indicating that these official users have access to a larger amount of information on the topic of Shu Style and Elegant Rhyme and have access to a variety of channels. The study found that in the entry degree value of the People’s Daily, the Communist Youth League Central Committee, Sichuan Satellite TV and other official microblogging on the news dissemination of the Shu Feng Ya Yun program topic of information dissemination, traditional drama dissemination and dissemination of the public opinion guidance of the relatively high degree of activity, played the network structure of the key role of the opinion leader, demonstrated a stronger ability to guide the dissemination of information and from the interception of a typical representative of the number of degrees of centrality, and see that the communication impact of each official microblog shows differences.

Table 3.

The centrality index of social network on the topic

Serial number	Node name	Indegree	Outdegree
1	People’s Daily	57	42
2	Sichuan TV	34	56
3	The center of the communist youth league	44	41
4	thepaper.cn	29	36
5	CCTV network	32	44
6	People’s Daily	23	36
7	Sichuan radio	19	50
8	Sichuan daily	22	55
9	Climbing daily	21	52
10	Sichuan news	21	39

4.3.2

Intermediate centrality

The intermediate centrality indicators (excerpts of the top 20) of the Shu Feng Ya Yun topic social network are shown in Table 4. The data in the above table shows that the intermediate centrality of official users such as People’s Daily, Sichuan Satellite Television, and the Central Committee of the Communist Youth League is the highest, indicating that these official media occupy relatively rich information dissemination resources in the network topology, and control the dissemination flow and dissemination process of the topic well, and play a great role as a bridge to promote the overall network information dissemination process. We found that the maximum value of the relative intermediate centrality between the 50 Shu Feng Ya Yun high attention and users is 10.400, the minimum value is 2.115, and the standard deviation is 3.7, which shows that the communication control power in the social network presents a significant difference. And the intermediate centrality potential of information dissemination of the whole topic is 0.325, this figure indicates that the network relevance of the topic is high, the information is interchangeable and the communication is relatively close. And the official microblog of People’s Daily has the highest intermediate centrality, which indicates that it is the core node, and many nodes have no connection with each other, but because of the existence of official users such as People’s Daily and Sichuan TV, it makes the nodes that have no relationship with each other establish a relationship. As an authoritative website for news release, it plays a very important role in the topic of Shu Feng Ya Yun, without this node, a lot of paths will be changed, and it may also affect the dissemination of information to a certain extent.

Table 4.

The betweenness centrality index of the social network on the topic

Serial number	Node name	Degree	Nrmdegree
1	People’s Daily	64	10.411
2	Sichuan TV	59	9.08
3	The center of the communist youth league	55	9.027
4	thepaper.cn	52	8.091
5	CCTV network	39	7.67
6	People’s Daily	41	7.22
7	Sichuan radio	34	5.908
8	Sichuan daily	33	6.092
9	Climbing daily	29	6.211
10	Sichuan news	31	5.875
11	Sina variety	28	5.834
12	resight	29	5.579
13	Sina video	23	5.116
14	Live broadcast	22	5.07
15	Shu jing	23	4.807
16	Audio-visual China	19	4.76
17	dynamicnews	17	4.53
18	Sichuan TV	16	4.303
19	Chinese dance	15	4.225
20	China news network	14	3.974

The intermediate centrality between users of the Shu Feng Ya Yun topic is shown in Figure 7.

4.3.3

Proximity to centrality

We measured the proximity centrality of social network analysis on the topic of Shu Feng Ya Rhyme by ucinet software, and the proximity centrality measures (excerpts of the first 10) in social networks are shown in Table 5. From the results of the social network proximity centrality of the Shu Feng Ya Yun topic in the table, it can be seen that the entire topic in the process of information dissemination, “live”, “China Dance Soul” official users in the entire process of information dissemination, the information resources are in the recipient is relatively more. It suggests that information resources are not easily held by individuals or small groups of actors throughout traditional theater networks. The low proximity centrality of official microblogs such as “Sichuan Satellite Television”, “Surge News” and “Panzhihua Daily” indicates that they are very independent in accepting and disseminating information, and they are able to control the resources on the network for reporting about the topic and are not controlled by many other nodes. It is able to control the resources on the network and is not controlled by many other nodes, and when it releases information, all other nodes can accept it effectively and smoothly.

Table 5.

Proximity centrality measures in social networks

Serial number	Node name	Infarness	Outfarness
1	Sichuan TV	52	56
2	thepaper.cn	72	58
3	Climbing daily	71	92
4	People’s Daily	69	98
5	The center of the communist youth league	73	63
6	Sina variety	74	103
7	Shu jing	85	126
8	Sichuan TV	86	116
9	Chinese dance	120	100
10	Live broadcast	130	102

4.4

Structural Hole Analysis

The structural hole analysis indicators of Shu Feng Ya Yun topic social network are shown in Table 6. As can be seen from the data in the table, the highest of the effective size indicators is Sichuan TV, and the lowest of the restricted system indicators is Panzhihua Daily. The results of the study show that these official microblogs have a relatively strong ability to occupy structural holes, which helps to build a diffractive information dissemination network structure and further enhances the network’s characteristics of robustness and toughness. In addition, we find that these official microblogs have a lot of flexibility and discourse power in the process of topic information dissemination as well as mastering information resources, which is conducive to the promotion of the overall social network dissemination of the ability to dominate and follow.

Table 6.

Structural hole analysis indicators of the social network

Serial number	Node name	Effective scale	Limitation
1	Xinhuanet	30.567	0.08
2	China Daily	33.034	0.075
3	China broadcasting network	31.298	0.093
4	Sichuan TV	32.104	0.068
5	Climbing daily	22.256	0.054
6	People’s Daily	30.225	0.072
7	The center of the communist youth league	32.861	0.108
8	Sina variety	17.56	0.112
9	Tianya	13.866	0.111
10	Live broadcast	12.386	0.156

4.5

Analysis of cohesive subgroups

The cohesive subgroups in the social network topology are shown in Fig. 8, and the information dissemination of the topic of Shu Feng Ya Rhyme, for example, is clustered into eight subgroups with different numbers and different locations in this social network dissemination. The relationship networks, discussion contents, and topic attributes within these topics have strong similarities. The users in each subgroup interacted with each other frequently, which effectively attracted the activity of other subgroups. The discourse expression and information commonality among the topics promoted for discussion also facilitated users’ identification and categorization. In addition, we can also find that some clustered subgroups have more subgroups, which to some extent indicates that the overall communication power needs to be improved, and is also conducive to the dissemination of official guiding media, which is more oriented.

5

Influence of User Information Interaction Behavior on Traditional Drama Communication

5.1

Influence of Opinion Leaders on Traditional Theater Communication

In order to analyze the effect of opinion leaders on traditional drama propagation in single-tier social networks, comparison experiments are conducted by taking λ =0 (without considering opinion leaders) and λ =0.1 (considering opinion leaders) with the same values of other parameters, and the effect of opinion leaders on drama propagation is shown in Fig. 9. Where (a) and (b) denote the density of I and R state nodes, respectively. As can be seen from the figure, the peak value of I users is larger after considering opinion leaders, which is because, when users interact with opinion leaders whose status and influence differ greatly from their own, their own opinion system about their own views is easy to be shaken and changed by other views, and then turn to disseminate traditional drama information. Therefore, when exploring the actual traditional drama propagation law, the impact caused by the difference of opinion leaders on the online traditional drama propagation should be considered. From the figure, it can also be seen that when the traditional drama propagation reaches stabilization, the difference between the user values of each state with and without opinion leaders is not significant, which indicates that the case of opinion leaders taken into account will make the traditional drama relaxation time increase, but it will not affect the traditional drama reaching the final value when it reaches stability.

5.2

Impact of Viewpoint Interaction Behavior on Traditional Drama Communication

For the influence of viewpoint interaction behavior on traditional drama communication, this paper will carry out research from two aspects, discussing the influence of viewpoint interaction threshold and opinion compromise parameter on traditional drama communication respectively. Firstly, to carry out the research on the impact of viewpoint interaction threshold on traditional drama communication, in order to analyze the impact of viewpoint interaction threshold ϕ on the traditional drama communication on the network, assuming that ϕ is taken as 0.1, 0.2, 0.3, 0.4, and 0.5 respectively, the trend of the various types of nodes in the process of traditional drama communication is shown in Fig. 10. Where (a) and (b) denote the density of I and R state nodes, respectively.

As can be seen from Fig. (a), as the viewpoint interaction threshold ϕ increases, the peak value of the propagating user also increases, and the time consumed by the immunized user to reach stability also increases. When the viewpoint interaction threshold ϕ = 0.5, the peak value of the propagating user is the highest and the immune user consumes the longest time to reach stability. When the viewpoint interaction threshold ϕ = 0.1, the propagating user has the lowest peak and the immune user takes the shortest time to reach stability. This indicates that increasing the viewpoint interaction threshold results in a wider range of traditional drama propagation, an increase in the peak value of propagating users, and a longer time for traditional drama to reach stability. This is because increasing the viewpoint interaction threshold ϕ makes it easier for users to interact with each other, which in turn makes it easier for users to change their own viewpoints from the contact state to the propagation state. However, as time increases, when the traditional drama tends to stabilize, there is not much difference in the values of users in each state, which indicates that viewpoint interaction threshold ϕ can only increase its spreading range, and cannot make the traditional drama spread in the social network for a long time.

Secondly, the research on the influence of opinion interaction compromise parameter on traditional drama communication is carried out. In order to study the influence of opinion interaction compromise parameter μ on traditional drama communication on the network, the values of opinion interaction compromise parameter μ are 0.1, 0.2, 0.3, 0.4 and 0.5 respectively, while the other parameters take the same value, and the influence of opinion interaction compromise parameter on traditional drama communication is shown in Fig. 11, where (a) and (b) represent the density of the nodes of I and R states, respectively.

As can be seen from Figs. (a) and (b), the peak value of the propagating users increases sequentially as μ increases. As μ increases, the time for immune users to reach stability increases sequentially, and when they finally reach stability, the state of each user is basically the same. This is because the opinion interaction compromise parameter determines the degree of adherence of users to their own views in the whole network, when the opinion interaction compromise parameter is small, users will not easily change their own views after contact with other users, but will express their own views, while when the opinion interaction compromise parameter is large, users in the social network will be more likely to compromise on other views, and then transformed into dissemination users. At the same time, it can be seen that when the traditional drama reaches stability, the value of the users in each state has little relationship with the value of the opinion interaction compromise parameter, this is because the opinion interaction compromise parameter can only change the user’s idea temporarily, and can not decide whether the user continues to disseminate the traditional drama or not, so in the actual dissemination of the traditional drama, the influence of the opinion interaction compromise parameter on the dissemination of the traditional drama needs to be taken into account.

5.3

Influence of Topic Interest Level on the Communication of Traditional Drama

In order to investigate the effect of topic interest degree on traditional drama propagation in single-layer social networks, the values of δ are 0.1, 0.2, 0.3, 0.4 and 0.5 without considering the effect of other factors on traditional drama propagation when the other parameters take the same value. The effect of topic interest degree on traditional drama propagation is shown in Fig. 12, where (a) and (b) represent propagation and immunization states, respectively Node density of users.

From the figure, it can be seen that the peak and the value at reaching stability are lowest for propagating users when δ =0.1 and highest for immunized users when they reach stability. Peak and reach stable values are highest for propagating users when δ =0.5 and lowest for immunized users when they reach stable. With the increase of time, the value of each state user when reaching stability changes with the change of topic interest, i.e., the value of each state user is closely related to the topic interest degree, and the topic interest degree will make the spreading user spread the traditional drama information for a long period of time, and even if it reaches stability, there are still the spreaders of traditional drama topics in the whole social network. This suggests that the interaction between users’ opinions can only change their ideas for a short time, while the degree of interest in traditional drama topics is the core of their dissemination of traditional drama. Therefore, in the process of traditional drama dissemination, if we push the topics of interest that are related to traditional drama, we can promote the dissemination of traditional drama, and on the contrary, if we push the topics of interest that are irrelevant to traditional drama, we can divert the public’s attention and make this kind of traditional drama information quickly disappears.

6

Conclusion

This paper focuses on the digital protection and dissemination of traditional drama, using the basic theory of complex networks, combined with the dynamics of communication, to explore the study of traditional drama dissemination model of complex social networks under the technical support of new media. The main findings of this paper are as follows:

Taking the 2023 “Sichuan Satellite TV Shufeng Yayun Program” as the research material of Sichuan opera communication, after analyzing the network structure index of Shufeng Yayun topic information dissemination, the network density of Shufeng Yayun topic social network communication is 0.03925, the number of connection lines of the topic and the user’s attention node is 645, the average distance is 2.5, and the clustering coefficient is 0.362. It can be concluded that the interaction between the various nodes in the dissemination of information in social networks is relatively low, and the overall aspect of the cohesive situation needs to be strengthened.

After simulation and analysis of the proposed DR-SCIR social network traditional drama propagation model, it is found that the opinion leader, the viewpoint interaction threshold and the opinion compromise parameter all affect the scope of traditional drama propagation to a certain extent, so that the time for traditional drama to reach stabilization is increased, but they do not have much effect on the final value of the traditional drama when it is stabilized; the topic interest degree is the core of determining the propagation of traditional drama. When the regulation parameter δ = 0.1 that regulates the users’ interest in the topic of traditional drama, the peak value of the spreading users and the value at the time of reaching stability are the lowest, and the value at the time of reaching stability of the immune users is the highest.

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Study on the Application of New Media Technology in the Digital Protection and Dissemination of Traditional Drama-Taking Sichuan Opera as an Example

Di Wu

Published Online: Sep 26, 2025

Received: Jan 10, 2025

Accepted: May 06, 2025

DOI: https://doi.org/10.2478/amns-2025-1048

KeywordsComplex network, DR-SCIR, Propagation model, Traditional drama

© 2025 Di Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Complex network, DR-SCIR, Propagation model, Traditional drama