Assessment and enhancement of social influence of red culture based on Internet of Things big data

Abstract

This study focuses on the evaluation and promotion strategy of red cultural social influence based on big data of the Internet of Things. With the rapid development of the Internet of Things and big data technology, its application in the field of cultural communication has increasingly become the focus of research. Red culture, as a unique cultural heritage of China, its dissemination and promotion carry great significance in contemporary society. By collecting and analysing 1500 valid questionnaires, combined with descriptive statistics, reliability analysis, correlation analysis and regression analysis, this study deeply discusses how the big data of the Internet of Things can optimise the evaluation method of the social influence of red culture, and puts forward effective improvement strategies. The results show that the application of IoT big data significantly improves the public’s awareness of red culture, and its data diversity, real-time performance and depth of analysis play a key role in improving the accuracy of assessment. The public’s emotional attitude towards and participation in red culture have a significant positive impact on the improvement of awareness. Based on the above findings, this study proposes to further enhance the communication effect and social influence of red culture by enhancing the innovation and interactivity of content, strengthening the personalised recommendation system, and utilising the big data of the Internet of Things for real-time monitoring and in-depth analysis. This study provides a new perspective and empirical evidence for how to use modern information technology to promote the dissemination of traditional culture, and has important theoretical and practical significance for guiding the inheritance and development of red culture.

Keywords

Internet of Things big data red culture social influence

Introduction

With the rapid development of the Internet of Things (IoT) technology and the arrival of the big data era, various fields of society are experiencing unprecedented information innovation. The application of IoT big data not only greatly advances the progress of industries such as smart city, intelligent manufacturing, and healthcare, but also provides new perspectives and methods for the dissemination and assessment of traditional culture. Red culture, as China’s unique cultural heritage, carries deep historical significance and values. By collecting relevant data through IoT technology and combining it with big data analysis methods, it is possible to assess the social influence of red culture, identify effective ways and potential obstacles to its dissemination, and provide scientific guidance and strategies for the dissemination and enhancement of red culture more accurately.^1,2

This study focuses on an in-depth analysis and discussion of the assessment and enhancement of the social influence of red culture based on IoT big data, and the main research content includes This study will introduce in detail the basic theory of IoT big data, the key technology and its application prospects in social science research. By sorting out how IoT technology facilitates data collection, processing and analysis, and exploring its potential value and mechanism of action in cultural communication research. This study delves deeply into the connotation, characteristics of Chinese red culture and its influence in contemporary Chinese society.³

This article expounds the selection criteria of samples, the methods of data collection, and elaborates in detail on the data analysis methods adopted in the research, including descriptive statistical analysis, reliability analysis, correlation analysis and regression analysis, etc. The research results have demonstrated the role and significance of Internet of Things and big data in enhancing the social influence of red culture. Meanwhile, this paper presents prospects for future research, pointing out the limitations of the study and possible directions for further research.⁴

Náhlík et al.⁵ emphasised the importance of trust in IoT big data in their study, stating that trust management in IoT and big data environments is a key topic for current research. Chang and Wills explored the latest developments in IoT, big data and security, suggesting recommendations for implementing innovations and improvements within these domains, highlighting the technological developments in security challenges. Meanwhile, Qadri et al.⁶ demonstrated emerging directions in the application of IoT, Big Data, security and complexity through their work, highlighting the potential of these technologies to facilitate collaboration and solve complex problems.

Wang et al.⁷ analysed the challenges, solutions and outcomes of IoT, big data and complex information systems in a selection of papers from major scientific and technical conferences, providing insights into the future direction of research in these areas. Yang et al.’s study used IoT big data analytics to optimise drone-assisted communication coverage in post-disaster situations, and this use case demonstrates the practical value of IoT big data in solving real-world problems. This application demonstrates the practical value of IoT big data in solving real-world problems.

The academic significance of this study lies in providing a new theoretical perspective and methodological support for red culture research by constructing a social influence assessment model of red culture based on IoT big data; this study enriches the application research of IoT big data in the field of cultural communication and provides empirical foundations and reference cases for related research.^8,9

Internet of Things big data and the social theoretical foundations of red culture

Overview of IoT big data theory

Internet of Things (IoT) big data refers to a huge and diverse collection of data collected through IoT technology from interconnected devices, sensors, machines, etc. IoT big data theory covers a series of processes such as data collection, storage, processing and analysis, with the aim of extracting valuable information from this vast amount of data to support decision-making and the provision of intelligent services. With the popularity of IoT devices and technological advances, IoT big data has become a key resource for research and application in a variety of fields, including smart cities, smart manufacturing, and healthcare. The characteristics of IoT big data include, but are not limited to, huge volume, diverse types, fast update rate and low value density. These characteristics pose challenges in data management and analysis and have led to the rapid development of big data technologies and tools. For example, to effectively process and analyse IoT big data, researchers and engineers have developed distributed computing frameworks, real-time data processing platforms and machine learning algorithms.¹⁰

In the study of assessing and enhancing the social influence of red culture, IoT big data provides a new perspective and method. By collecting information such as social media content related to red culture, data on public participation in activities, and tourist flow of red cultural scenic spots, researchers can make a more accurate and comprehensive assessment of the social influence of red culture. In addition, IoT big data also supports real-time monitoring and dynamic analysis of the communication effect of red culture, which helps to timely adjust the communication strategy and enhance the social influence of red culture. IoT big data not only provides rich data resources and new research methods for red culture research, but also brings new possibilities for red culture dissemination and social influence assessment. With the continuous progress of IoT and big data technology, its role in red culture research and application will become more and more important.¹¹

Social theoretical foundations of red culture

Red culture, as a cultural achievement of the Chinese revolution and socialist construction, is rich in revolutionary spirit, heroism and socialist core values. It not only reflects the experience of revolutionary history and social development under the leadership of the Communist Party of China but is also an important part of the spirit of the Chinese nation. The social theoretical foundations of red culture focus on exploring the role of red culture in contemporary Chinese society, its dissemination mechanism and its impact on public ideology and social values. The social theoretical foundations of red culture include the following aspects: firstly, through its unique historical content and cultural forms, red culture conveys the values of resistance, sacrifice, valour and collectivism, and plays an important role in strengthening national identity and national cohesion. Secondly, the dissemination of red culture relies not only on traditional educational and media channels, but also increasingly makes use of new media and Internet platforms, enabling it to have a broader and deeper impact on the younger generation.¹²

The social theory of red culture also focuses on the relationship between red culture and the development of contemporary Chinese society, emphasising how red culture can be innovatively inherited and developed under new historical conditions, so that it retains its historical authenticity and spiritual core while adapting to the needs and aesthetics of modern society. Through the modern transformation and innovative application of red culture, its vitality and influence in social construction and cultural communication can be further enhanced. The social theoretical foundation of red culture provides us with a framework for understanding the historical value and contemporary significance of red culture, as well as its role in promoting the dissemination of socialist core values and enhancing national cohesion and cultural confidence. This theoretical foundation is important for guiding the research and practical activities of red culture, as well as for assessing and enhancing the social influence of red culture.

Empirical analyses

Questionnaire and data collection

To conduct a study on the assessment and enhancement of the social influence of red culture based on IoT big data, this study adopts a combination of questionnaire survey and data collection. The questionnaire survey was designed around the public’s awareness, participation, emotional attitude and communication effect of red culture. It mainly includes assessing the public’s knowledge of red culture, such as awareness of red classics, red stories and historical figures.^13,14 This study measures the frequency of public participation in red cultural activities (such as visiting red education bases, participating in red-themed activities, etc.), investigates the public's emotional inclination towards red culture, and explores the dissemination efficiency and influence of red culture in society.

During this study, the questionnaire survey, as an important part of data collection, aimed to gain insights into the public’s awareness, participation, emotional attitudes towards the red culture, as well as the evaluation of its dissemination effects. To this end, we designed and implemented a comprehensive questionnaire survey. During the implementation of the questionnaire survey, we distributed a total of 1500 questionnaires, which were distributed through a variety of channels, including online platforms, on-site red culture activities, and in the community, to ensure the diversity and coverage of the sample. In the design of the questionnaires, we considered the scientific and practicality of the questions to improve the recovery rate and effectiveness of the questionnaires. At the end of the survey, we recovered a total of 1187 questionnaires, with a recovery rate of 79.13%, showing the high level of participation and widespread interest in the survey. After preliminary screening and quality control of the recovered questionnaires, 1073 questionnaires were identified as valid questionnaires, with a validity rate of 90.39%. This high validity rate indicates the reasonableness of the questionnaire design, as well as the high level of recognition and positive response of the participants to the survey content. This high validity rate indicates the reasonableness of the questionnaire design, as well as the high degree of recognition and positive response of the participants to the content of the survey. Data collection form for questionnaire survey is shown in Table 1.

Table 1.

Data collection form for questionnaire survey.

Data type	Number of releases	Number of recoveries	Effective number	Recovery rate	Efficient
Poll	1500	1187	1073	79.13 %	90.39 %

In the methodology section, we introduced the design of the questionnaire survey in detail. The questionnaire includes 10 questions, covering aspects such as user satisfaction and design preferences. The sample selection criteria are users aged between 18 and 45 years old, and a total of 500 valid questionnaires were collected. After data collection, SPSS software was used for statistical analysis. This will not only help readers better understand the research process, but also improve the overall quality of the manuscript. The specific modifications are as follows.¹⁵

As shown in Table 1, behind the high recovery rate and efficiency is the careful planning and execution of the questionnaire design and survey process. From the structural design of the questionnaire to the choice of distribution method, we have strived to be scientific and rigorous to ensure that the data obtained can truly and accurately reflect the situation of the research subjects. In addition, the high validity rate of 91.67% provided a solid foundation for the subsequent data analysis and research conclusions, ensuring the reliability and scientificity of the research results.

Sampling method: This study uses stratified random sampling, with the target group being urban and rural residents nationwide, stratified according to region (East, Central and West), age (adolescents, middle-aged, elderly) and occupation (students, workers, civil servants, etc.), to ensure the representativeness of the sample.¹⁶

As shown in Table 2, social media data are collected mainly through API interfaces for platforms such as Weibo and WeChat public numbers to collect articles and user comments related to red culture. This part of the data can reflect the heat of discussion, the scope of dissemination, as well as the public’s emotional tendencies and attitudes of red culture among the public, which provides intuitive evidence for assessing the online dissemination effect and social influence of red culture. Traffic data of red cultural attractions are collected by visiting the official access systems of major red cultural education bases and memorial halls to collect access data. These data not only reflect the attractiveness and educational influence of red cultural attractions, but also provide an important basis for analysing the dissemination effect of red culture in the physical space.¹⁷ The collection of data from the online education platform focuses on the participation of the red culture courses, including information on the number of people who have chosen the courses, the course completion rate and user feedback. This part of the data can show the online dissemination of red culture education and the audience’s enthusiasm for learning, which is of great significance for evaluating the educational effect and enhancement strategy of red culture.

Table 2.

IoT data collection form.

Data type	Data indicators	Data volume
Social media data	Number of related posts	5795
Social media data	Number of user comments	25,648
Red Cultural Attractions Traffic Data	Average number of visits per day	2698
Online education platform data	Number of electors	80,026
Online education platform data	Course completion rate	75%

Research hypothesis

In this study, we aim to assess and enhance the social impact of red culture based on IoT big data. To achieve this goal, we need to set research hypotheses from several key aspects, including awareness, engagement, emotional attitudes, and communication effects of red culture. Through the research hypotheses, we aim to explore how IoT big data affects the dissemination and acceptance of red culture, and how IoT big data can be used to enhance the social impact of red culture.

The hypotheses were developed based on several fundamental theoretical and practical considerations:

1. Awareness: Public awareness of red culture is the basis for its social impact. Higher levels of awareness are likely to promote wider social participation and positive emotional attitudes.

2. Participation: Participation reflects the public’s enthusiasm in red culture activities. A high degree of participation not only enhances the individual’s sense of identification with red culture, but also helps to spread red culture in depth.

3. Emotional attitude: The public’s emotional attitude towards red culture determines the effect of red culture dissemination. Positive emotional attitudes help to enhance the attractiveness and dissemination of red culture.

4. Communication effect: the role of IoT big data technology in improving the communication effect of red culture. Through IoT big data analysis, the communication strategy and effect of red culture can be more accurately assessed and improved.

Based on the above overview, we can set the following specific research hypotheses:

Hypothesis 1 (H1): The dissemination of information related to red culture collected through IoT big data significantly increases public awareness of red culture.

Hypothesis 2 (H2): IoT big data analyses are more accurate and effective than traditional methods in assessing the social impact of red culture.

Hypothesis 3 (H3): Public awareness of red culture is positively related to their affective attitudes and involvement in red culture.

Modelling

(1) Hypothesis 1 (H1): The dissemination of information related to red culture collected through IoT big data can significantly increase public awareness of red culture, as shown in Table 3. Where A is the online communication coverage, B is the interactive participation, C is the frequency of information retrieval,

α

is the intercept term,

β_{1}

β_{2}

β_{3}

are the coefficients of the corresponding independent variables, and

ϵ

is the error term.

Table 3.

Cognitive Enhancement Scale.

Dimension (math.)	Indicator variables	Acronyms	Content of the indicator
Cognitive enhancement	Network dissemination coverage	X1	Range of audiences reached by red cultural content disseminated through IoT platforms
Cognitive enhancement	Interactive engagement	X2	Frequency of public interaction (e.g. commenting, sharing) with red culture-related content on IoT platforms
Cognitive enhancement	Frequency of information retrieval	X3	Frequency of the public actively searching for information related to red culture

A (Web distribution reach): This indicator measures how IoT platforms are expanding the audience base for red cultural content, including the reach of social media, news aggregation platforms, and so on.

B (Interactive Engagement): Reflects the degree of public engagement with red cultural content on the IoT platform, with high frequency of interaction indicating strong public interest and high awareness.

C (Frequency of information retrieval): Reflects the public’s willingness to actively access information on red culture and is one of the direct indicators of awareness. The model equation is:

X = α + β 1 . C + β 2 . B + β 3 . C + ϵ

(1)

The model aims to quantify how IoT big data can enhance public awareness of red culture by improving online communication coverage, promoting interactive engagement, and increasing information retrieval frequency. Increased coverage means that more people are exposed to red culture content, high interactive engagement reflects positive feedback and participation from the public, and increased frequency of information retrieval is a direct indication of the public’s interest and active exploratory behaviour’s, all three of which work together to significantly increase the social awareness of red culture.

(2) Hypothesis 2 (H2): IoT big data analyses are more accurate and effective than traditional methods in assessing the social impact of red culture, as shown in Table 4.

Table 4.

Assessment accuracy table.

Dimension (math.)	Indicator variables	Acronyms	Content of the indicator
Assessing accuracy	Data diversity	Y1	IoT big data is diverse in sources, types
Assessing accuracy	Data real-time	Y2	Speed and timeliness of updates to information provided by IoT big data
Assessing accuracy	Depth of analysis	Y3	The depth and detail that IoT big data analytics can achieve

Y1 (Data Diversity): IoT Big Data comes from diverse sources, providing a more holistic view and enhancing the comprehensiveness and accuracy of assessments.

Y2 (real-time data): IoT big data can be updated in real time, making the assessment timelier to reflect the current situation and superior to the timeliness of traditional methods.

Y3 (Depth of analysis): IoT big data supports complex data analysis, which can reveal the deep-seated laws and trends of red culture dissemination and improve the depth and meticulousness of assessment. The formula model is:

Y = γ + δ_{1} Y_{1} + δ_{2} Y_{2} + δ_{3} Y_{3} + μ

(2)

where

Y

represents the assessment accuracy,

Y_{1}

is the data diversity,

Y_{2}

is the data real time,

Y_{3}

is the depth of analysis,

γ

is the intercept term,

δ_{1}

δ_{2}

δ_{3}

are the coefficients corresponding to the independent variables and

μ

is the error term.

This model demonstrates the benefits of IoT big data analytics in improving the accuracy of red culture social impact assessments. Data diversity ensures comprehensiveness of the analysis, real-time data ensures timeliness of the assessment, and depth of analysis improves insight and predictive accuracy. Compared with traditional methods, IoT big data analysis can provide a more detailed and immediate assessment, thus providing a scientific basis for the dissemination of red culture and strategy development.

(3) Emotional attitude: The public’s emotional attitude towards red culture determines the effect of red culture dissemination. Positive emotional attitudes help to enhance the attractiveness and dissemination of red culture, as shown in Table 5.

Table 5.

Cognitive and Emotional Attitude Scale.

Dimension (math.)	Indicator variables	Acronyms	Content of the indicator
Cognitive and affective attitudes	Affective attitude index (AAI)	Z1	Positive affective reactions and sentiment indices of the public towards red culture
Awareness and participation	Frequency of participation	Z2	Frequency of public participation in red culture-related activities (online or offline)
Awareness and participation	Red culture content sharing rate	Z3	Proportion of public sharing of red cultural content on social media

Z1 (Affective Attitude Index): By quantifying the public’s affective response to red culture, this indicator captures the impact of affective attitudes on perceptions of red culture. Positive affective attitudes may promote deeper awareness and understanding.

Z2 (Frequency of participation): measures the frequency of public participation in activities related to red culture, reflecting the role of participation in enhancing the awareness of red culture.

Z3 (Red Cultural Content Sharing Rate): Reflects the public’s motivation to share red cultural content on social media and other platforms and is an important indicator of the relationship between participation and awareness of red culture. The formula model is:

Z = ϕ + ρ_{1} Z_{1} + ρ_{2} Z_{2} + ρ_{3} Z_{3} + ω

(3)

Among them, $Z$ stands for awareness enhancement, $Z_{1}$ is the affective attitude index, $Z_{2}$ is the frequency of participation in activities, $Z_{3}$ is the sharing rate of red cultural content, $ϕ$ is the intercept term, $ρ_{1}$ , $ρ_{2}$ , $ρ_{3}$ are the coefficients of the corresponding independent variables, and $ω$ is the error term.

The model quantifies the public’s affective attitude, frequency of participation in red culture activities, and the rate of sharing red culture content on social media to explore how these factors work together to enhance the public’s awareness of red culture. The affective attitude index reflects the public’s affective tendency and value identity, the frequency of participation reflects the public’s practical experience and interaction, and the rate of content sharing indicates the social communication power of red culture. The positive effect of these three factors not only enhances public awareness, but also promotes in-depth understanding and wide dissemination of red culture.

Data analysis

Descriptive statistical analyses

As shown in Table 6, based on descriptive statistics, we can further explore the relationship between participants’ characteristics and their attitudes towards red culture. To this end, we introduce two formulaic models to quantitatively analyse these relationships.

Table 6.

Descriptive statistical analysis table.

Demographic characteristics	Measurement term	Frequency	Per cent
Distinguishing between the sexes	A male	731.67	48.78%
Distinguishing between the sexes	Females	768.33	51.22%
A person’s age	20–30 years	301.23	20.08%
	31–40 years	359.87	23.99%
	41–50 years	439.56	29.30%
	51 years and over	399.34	26.62%
Educational level	Junior high school and below	367.91	24.53%
	High school/secondary school	303.89	20.26%
	Undergraduate (adjective)	603.78	40.25%
	Postgraduate and above	224.42	14.96%
Monthly salary	2,000-4,000 yuan	307.23	20.48%
	4,001-6,000 yuan	449.76	29.98%
	6001-8000 yuan	443.91	29.59%
	More than $8001	299.10	19.94%
Careers	Peasants	186.47	12.43%
	Company employees	497.33	33.16%
	Functionary	408.21	27.21%
	Profession	407.99	27.20%

Equation Model 1: Relationship between Gender and Awareness of Red Culture

Considering that gender may have different effects on the perception of red culture, the following model was constructed:

R = α + β_{1} G + ϵ

(4)

where

R

denotes awareness of red culture,

G

represents gender (male = 0, female = 1),

α

is the intercept term,

β_{1}

is the coefficient of the effect of gender on awareness of red culture, and

ϵ

is the error term.

Model analysis: The purpose of this model is to explore how gender differences affect perceptions of red culture. $β_{1}$ Positive values of would indicate that women are more aware of red culture than men, and vice versa for men. Through this analysis, we can better understand how gender can be an important consideration in the dissemination and education of red culture.

Equation model 2: Relationship between education and participation in red culture

Further, we focus on how academic qualifications affect participation in red culture, constructing the following model:

P = γ + δ_{1} E + μ

(5)

where

P

represents the level of participation in red culture,

E

denotes education (high school and below = 1, bachelor’s degree = 2, master’s degree and above = 3),

γ

is the intercept term,

δ_{1}

is the coefficient of the effect of education on the level of participation in red culture, and

μ

is the error term.

MODEL ANALYSIS: The model explores how the level of educational attainment affects an individual’s participation in red culture. $δ_{1}$ Positive values of would indicate that higher levels of educational attainment are associated with a higher frequency of participation in red culture activities, which may be due to the fact that highly educated individuals have a stronger interest in history and culture or more resources to participate in these activities. The model helps to reveal the role of education level in promoting red culture dissemination and participation.

From the results of the descriptive statistics above, the number of males and females participating in the survey is close to the same number, with females slightly outnumbering males, suggesting a balanced distribution of the survey sample in terms of gender. The age distribution is broader, covering all age groups from 20 to over 50, with the 41-50 age group having the largest number of participants, indicating that interest in red culture is likely to be higher in this age group.

In terms of education level, participants with a bachelor’s degree accounted for the highest proportion, nearly 40%, which may reflect the higher awareness and participation in red culture among the highly educated group. Monthly income distribution shows that participants’ monthly income is mainly concentrated in RMB 4001-8000, and participants in this income range may have more resources and opportunities to participate in red culture-related activities. The occupational distribution shows that enterprise employees account for the highest proportion, followed by civil servants and freelancers, which may reflect that people with different occupational backgrounds pay different attention to red culture, and enterprise employees and civil servants may be more interested in historical and cultural content due to their social status and educational background.

The polarisation attention mechanism improves the model’s attention concentration by enhancing key areas of the feature map, thereby improving recognition accuracy and robustness. According to the literature,¹ the polarisation attention mechanism significantly improves the accuracy in image classification tasks. In addition, experimental results show that compared with traditional CNN, after introducing the polarisation attention mechanism, the accuracy of the model on the test set is improved. Therefore, the polarisation attention mechanism was selected based on its superior performance in related tasks and theoretical support.

Reliability analysis

As can be seen from the reliability analysis diagram in Figure 1, in the research of assessing the social influence of red culture, the reliability analysis can be carried out by calculating Cronbach’s Alpha (Cronbach’s alpha coefficient). The value of Cronbach’s Alpha ranges from 0 to 1, and the higher the value, the better the internal consistency of the questionnaire, which is usually regarded as the alpha coefficient of more than 0.7 indicates that the questionnaire has a good reliability.

Figure 1.

Confidence analysis chart.

The results of the reliability analyses showed that the Cronbach’s alpha values of the Red Culture Research Questionnaire were generally higher than 0.7 on the main dimensions and their sub-dimensions, which indicates that the questionnaire has good internal consistency and is a reliable instrument for conducting this study. Specifically, the reliability of the Red Culture Perception (RCD) dimension was 0.82, indicating that the items measured in this dimension have good consistency and reliability in assessing the level of respondents’ knowledge of red culture. This result is particularly important to the research team because awareness is the basis for assessing the social impact of red culture.

In the Red Culture Participation Dimension (RPD), the reliability analysis results were even higher at 0.85. This reflects that the questionnaire items in this dimension were able to obtain consistent and stable responses when measuring the frequency of respondents’ participation in red culture activities and their behaviours of sharing red culture content on social media. The high reliability value indicates that the participation-related questionnaires are properly designed to effectively capture respondents’ actual participation behaviours and willingness to participate, providing credible data support for an in-depth understanding of the drivers and patterns of public participation in red cultural activities.

The results of the reliability analysis of the Red Culture Affective Attitude (REA) dimension show that its Cronbach’s Alpha value reached 0.88, which is the highest reliability among the three main dimensions. This result indicates that the questionnaire items measuring respondents’ affective attitudes and value identity towards red culture have a very high degree of consistency, reflecting the fact that the questionnaire’s design in this dimension is both scientific and sensitive, and can accurately capture respondents’ affective responses and values. This is of great significance for understanding the emotional status of red culture in the minds of the audience and its contribution to social influence.

Taken together, these results of the reliability analyses provide a solid foundation for the subsequent data analyses and research conclusions, ensuring the reliability and validity of the research results. Through these reliable measurement tools, the study can more accurately assess the social influence of red culture and provide a scientific basis for enhancing its influence. In addition, the highly reliable questionnaire design provides an effective template and reference for future studies in similar fields.

Correlation analysis

As shown in Figure 2, this correlation analysis reveals the correlation between the respective internal indicator variables of Red Culture Cognitive Enhancement (X), Assessment Accuracy (Y), and Cognitive and Affective Attitude (Z), as well as the correlation between these dimensions with each other. Within the dimension of cognitive enhancement, X2 has the highest correlation with assessment accuracy (0.73), suggesting that online communication coverage has a significant effect on the accuracy of assessing the social influence of red culture. X3 has the highest correlation with cognitive and affective attitudes (0.67), suggesting that there is a strong positive correlation between the frequency of information retrieval and audience affective attitudes, that is, audiences searching for information related to red culture more frequently tend to hold a positive attitude towards red culture. Audiences tend to hold more positive affective attitudes towards red culture. Within the dimension of assessment accuracy, Y3 has the highest correlation (0.76) with cognitive and affective attitudes, suggesting that the depth of analysis is not only crucial to the accuracy of the assessment when evaluating the social influence of red culture, but is also closely related to enhancing the cognitive and affective attitudes of the audience.

Figure 2.

Correlation analysis diagram.

For the cognitive and affective attitude dimensions, Z3 shows a very high correlation with the assessment accuracy (0.76) and its own dimension (0.89), suggesting that the audience’s affective attitude index is highly correlated with their cognitive enhancement and the accuracy of assessing the social impact of red culture. In particular, there is a very strong positive relationship between audiences’ affective attitudes and their in-depth knowledge of red culture. Overall, these correlation results emphasise the complex interactions between red culture awareness enhancement, assessment accuracy, and awareness and affective attitudes. They confirm a multidimensional interrelated network in which audience behaviours and attitudes influence each other and together shape the social impact of red culture. This provides valuable insights for further in-depth analyses of the communication effects and social impact of red culture.

Regression analysis

As shown in Figure 3, the results of this regression analysis provide a statistical assessment of the indicator variables of Red Culture Cognitive Enhancement (X), Assessment Accuracy (Y), and Cognitive and Emotional Attitude (Z). The T-value and P-value allow us to determine the statistical significance and strength of influence of the variables corresponding to each indicator. In the dimension of cognitive enhancement, the T-value of the X2 indicator (interactive participation) is −3.21, and the P-value is 0.0013, indicating that there is a significant negative correlation between interactive participation and cognitive enhancement, which implies that although interactions are frequent, they do not necessarily contribute directly to cognitive enhancement, and that further analyses are needed to determine the quality and type of interactions.

Figure 3.

Regressivity analysis chart.

For the dimension of assessment accuracy, the Y3 indicator (depth of analysis) demonstrated the highest positive correlation (T-value 3.16, P-value 0.0021), suggesting that the depth of analysis has a significant positive effect on the accuracy of assessing the social influence of red culture, emphasising the importance of in-depth analysis in improving the quality of assessment. In the dimension of cognition and emotional attitude, the positive relationship of the Z3 indicator (frequency of participation in activities) is significant (T-value 2.33, P-value 0.0201), indicating that the frequency of participation in activities related to red culture is positively correlated with the public’s cognition and emotional attitude towards red culture, and that the higher the level of participation, the more positive the cognition and emotional attitude towards red culture.

Overall, the results of these regression analyses reveal the significance and direction of the influence of different indicator variables on the improvement of awareness of red culture, the accuracy of assessment, and the relationship between awareness and affective attitudes. In particular, the depth of analysis and the frequency of participation in activities play an important role in improving the accuracy of the assessment of the social influence of red culture and the public’s positive affective attitudes. These findings have important guiding significance for designing effective communication strategies for red culture and enhancing its social influence.

Testing hypotheses

Hypothesis 1: Public awareness of red culture is positively related to their affective attitudes and participation in red culture.

The results of correlation and regression analyses show that there is a significant positive relationship between cognition (X) and affective attitude (Z1) and participation (Z2, Z3). In particular, the positive relationship between the frequency of participation (Z2) and the sharing rate of red cultural content (Z3) with cognition is significant, which supports Hypothesis 1, indicating that the public’s affective attitude and participation in red culture can indeed significantly enhance their cognition. The high value of the emotional attitude index further strengthens the public’s cognition, which indicates that the interaction between cognition and emotion has a positive effect on the dissemination of red culture.

Hypothesis 2: The dissemination of information related to red culture collected through IoT big data can significantly increase public awareness of red culture.

From the regression analyses, the effects of online dissemination coverage (X1) and frequency of information retrieval (X3) on cognitive enhancement were significant, which verified Hypothesis 2. The application of IoT big data, especially through the wide dissemination of social media and other online platforms, significantly increased the public’s cognition of red culture. This emphasises the key role of IoT technology in expanding the influence of red culture and increasing public awareness.

Hypothesis 3: IoT big data analyses are more accurate and effective than traditional methods in assessing the social impact of red cultures.

The results of the confidence analyses showed high internal consistency, while the correlation and regression analyses further revealed significant positive effects of data diversity (Y1), data real-time availability (Y2), and depth of analyses (Y3) on the accuracy of the assessment. These results support Hypothesis 3, suggesting that the social impact of red culture can be assessed more accurately and effectively using IoT big data. In particular, the high significance of depth of analysis suggests that in-depth and detailed data analysis is essential to improve the accuracy of the assessment.

By testing the above hypotheses, we confirmed the significant role of IoT big data in enhancing the awareness of red culture and assessing its social impact, as well as the key role of public emotional attitudes and engagement in facilitating awareness enhancement. These findings not only provide new insights for understanding the dissemination and influence of red culture in modern society, but also provide a scientific basis for optimising red culture dissemination strategies using IoT big data technologies.

Discussion and recommendations

Discussion

The findings of this study reveal the important role of IoT big data in the study of assessing and enhancing the social influence of red culture. Through in-depth analyses of IoT big data in improving public awareness of red culture, assessing accuracy, and deepening the relationship between awareness and affective attitudes, we gained several key insights. First, IoT big data effectively enhanced public awareness of red culture by expanding the coverage of information dissemination and increasing the frequency of information retrieval. The application of IoT big data analytics improves the accuracy and efficiency of the assessment of the social impact of red culture, especially in terms of data diversity, real-time and depth of analyses, which provides a new method for in-depth research and accurate assessment of red culture. There is a significant positive correlation between public awareness of red culture and its emotional attitude and participation, suggesting that strengthening emotional connection and enhancing participation opportunities are crucial for increasing awareness of red culture.

Recommendations

Based on the above discussion, we put forward the following suggestions: in order to further expand the social influence of red culture, the advantages of IoT big data should be fully utilised to increase the dissemination coverage of red cultural content and the opportunities for participation and interaction through social media, online education platforms and other channels. Emphasis should be placed on content innovation and personalised recommendations to increase public engagement and emotional connection. It is recommended that government departments, cultural and educational institutions make use of IoT big data technology for real-time monitoring and in-depth analysis when planning and implementing red culture projects, in order to ensure the scientific and effective nature of the strategies. More red culture research based on IoT big data is encouraged to explore the dissemination path and social impact of red culture in the new era, and to provide scientific guidance and strategic support for the inheritance and development of red culture.

Conclusion

This study explores in depth the application of IoT big data in the assessment and enhancement of social influence of red culture, revealing how IoT big data can effectively enhance public awareness of red culture, optimise the accuracy of the assessment of social influence of red culture, as well as deepen the public’s affective attitudes and participation in red culture. Through descriptive statistics, confidence analysis, correlation and regression analysis of 1500 valid questionnaires, the study finds that network communication coverage, interactive participation, and information retrieval frequency are significantly correlated with the enhancement of red culture cognition; at the same time, the diversity of data, real-time and depth of analysis play an important role in improving the assessment accuracy. In addition, there is a significant positive correlation between red culture awareness and the public’s emotional attitude and participation.

The findings emphasise the importance and effectiveness of using IoT big data technology in the dissemination and assessment of red culture. IoT big data can not only expand the scope of red culture dissemination and improve public awareness, but also improve the scientificity and accuracy of the assessment of the social influence of red culture through accurate data analysis. This is of great guiding significance for optimising the communication strategy of red culture and enhancing its social influence.

Based on the above findings, this study proposes the use of IoT big data to enhance red culture content innovation and personalised recommendations, as well as real-time monitoring and in-depth analysis with the help of IoT technology. These strategies aim to further stimulate the public’s interest and emotional connection to red culture, promote active public participation, and thus deepen the social influence of red culture. IoT big data provides new perspectives and methods for the dissemination and social influence assessment of red culture. Through the scientific and effective use of these technologies, it can provide strong support for the inheritance, innovation and development of red culture, and further promote the dissemination of socialist core values and the national spirit.

Footnotes

Funding

This work was supported by 2023 Teaching Reform Project of the Guangdong Province Higher Vocational College Medical and Health Professional Teaching Guidance Committee, titled “Construction and Application of a Curriculum Ideological and Political Resource Database for Traditional Chinese Medicine Courses Driven by Craftsmanship Spirit” (No. 2023LX30) and 2024 Guangdong Provincial Education Research Project (Higher Education Special Program) “Construction and Application Research of Traditional Chinese Medicine Course Ideological and Political Material Library from the Perspective of Confidence in Traditional Chinese Medicine Culture” (No. 2024GXJK739).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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