Research on Scientific Research Communication Mode Analysis and
System Design Based on New Media Big Data
Yuhan Zhang
*
and Hongmin Pan
Shandong Institute of Commerce and Technology, Jinan, China
Keywords: New Media, Scientific Research, Information System, Big Data.
Abstract: The development of new media has made the dissemination of scientific research information more diverse
and faster. At the same time, the application of big data technology also provides more accurate and
comprehensive data support for scientific research dissemination. However, there are still some problems in
the current scientific research dissemination system, such as inaccurate information dissemination and
incomplete data analysis, which need to be further explored and solved. By analyzing the influence of new
media big data on the dissemination mode of scientific research work in vocational colleges, this paper designs
a scientific research work communication system of higher vocational colleges based on new media big data
to improve the communication effect and quality of scientific research work. The system includes four
modules: data collection, data analysis, data display and data application. The application effect of the system
has been verified in practice and remarkable results have been achieved. In short, the analysis of scientific
research communication mode and system design research based on new media big data are conducive to
improving the dissemination effect and influence of scientific research results, and promoting the
popularization of scientific knowledge and scientific and technological progress. In the future, we will
continue to conduct in-depth research, constantly improve the system and model, and make greater
contributions to the cause of scientific research and communication.
1 INTRODUCTION
In the information age of the 21st century, big data
has become an important topic for all walks of life.
With the rapid development of new media and the
continuous advancement of big data technology, the
mode of scientific research dissemination is also
undergoing profound changes (Rivas, J. G., 2019). In
this context, how to use new media big data to
improve the dissemination effect of scientific
research in vocational colleges and universities has
become a problem worth studying.
The era of new media is characterized by the
dominance of big data. The business world has
witnessed significant transformations as a result of
this era, and the media industry recognizes the
tremendous impact of big data. Consequently, the
media sector has been actively expanding resource
utilization and bolstering data analysis capabilities
through collaborative platform initiatives (O'Keefe,
C. M.- Julpisit, A.). However, whether it is in print,
television, online, or social media, the utilization of
big data is still in its exploratory phase.
In the new media era, the feedback model
represents a revolutionary shift in production.
Everything revolves around data, which is
extensively mined and analyzed to generate valuable
information. Traditional methods such as interviews
and writing alone can no longer meet the demands of
news production. Therefore, proficiency in data
technology has become an essential skill(Zhao, W. B.,
2019).In this "era of big data," obtaining news leads
requires enhanced professionalism. Media
organizations leverage specialized technologies and
tools to extract valuable leads from vast amounts of
information. They establish their own data research
centers or collaborate with database news teams to
delve deeper into news stories. This enables them to
acquire more valuable leads and expand the breadth
of their news coverage. While the era of big data has
provided us with an abundance of high-quality
information, it has also presented the challenge of
information overload (Xin, C. C., 2020). Balancing
this overwhelming volume of information with
personalized and customized user needs has become
a crucial concern. Regardless of how good the content
may be, its true value cannot be realized if it fails to
426
Zhang, Y. and Pan, H.
Research on Scientific Research Communication Mode Analysis and System Design Based on New Media Big Data.
DOI: 10.5220/0012285300003807
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2023), pages 426-430
ISBN: 978-989-758-677-4
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

effectively connect with the specific requirements of
users.
In the age of information intelligence, traditional
media must prioritize the concept of "content is king"
and emphasize the importance of service amidst
information overload. Specifically, they need to focus
on effectively matching information with users to
meet their specific needs and preferences (Weller, K.,
2015). Content is king, and personalized news
customization is realized. The proliferation of online
information and the limited attention span of
audiences determine that users will only select useful
content based on their habits and interests. This
implies that personalized news has the potential to
attract larger audiences and is crucial for the future
survival of the media. Big data analysis plays a vital
role in achieving intelligent information matching,
thereby enhancing the realization of information
value and facilitating intelligent production,
dissemination, and matching of information.
Utilizing vast amounts of user data, analyzing reading
habits, establishing relevant connections, and
offering personalized news customization is an
emerging trend in media development. For instance,
"big data" technology enables the digitization of
elusive elements like public opinions, attitudes, and
emotions, thereby improving the accuracy of online
public opinion research (Liu, L.-Chen, C.). Moreover,
with the extensive development of social connections
on the internet, the study of public opinion can be
examined from various dimensions, including social
relations. This multi-dimensional approach holds
significant value for enhancing public opinion
research and services. In the "era of big data," it is
imperative to establish the concept of "big public
opinion."
With the rapid development of new media, the
dissemination of scientific research information has
undergone tremendous changes, and the application
of big data technology has also provided new ideas
and tools for scientific research communication.
2 ANALYSIS OF RESEARCH
DISSEMINATION PATTERNS
Scientific research dissemination refers to the
dissemination of scientific research results through
various media to promote the popularization of
scientific knowledge and scientific and technological
progress. In the era of new media, the way of
scientific research dissemination has undergone great
changes, mainly reflected in the diversification of
communication channels, the enrichment of
communication content, and the interaction of
communication methods. In the field of scientific
research, the effective dissemination and
dissemination of scientific knowledge and results is
essential (De Winter, J.- Levine, F. J.).
The main communication channels include (Post,
R.- Saeed-Ul, H.): (1) Publication: Publish research
results in authoritative academic journals to keep
peers and academia abreast of the latest research
progress. Ensure that the paper is of high quality,
clear in content, and easy to understand. (2)
Participate in academic conferences: actively
participate in academic conferences at home and
abroad, display research results through reports,
posters and other forms, communicate with peers, and
strive for more cooperation and support. (3) Establish
cooperative relations: Establish cooperative
relationships with other research teams, enterprises
and institutions to jointly promote the application and
transformation of scientific research results. (4)
Social media promotion: use social media platforms
(such as Twitter, LinkedIn, WeChat, Weibo, etc.) to
share scientific research results and progress and
expand influence. (5) Write popular science articles:
present scientific research results to the public in an
easy-to-understand way to improve scientific literacy.
You can submit articles to popular science magazines,
websites or personal blogs. (6) Cooperation with the
media: cooperate with the news media and science
and technology media to issue scientific research
press releases to attract more attention. (7) Hold
public lectures and seminars: Regularly hold public
lectures, seminars and other activities, invite
interested public, enterprise and government
representatives to participate and share scientific
research results. (8) Education popularization:
Participate in science education activities, such as
school lectures, science festivals, etc., introduce
scientific research results to students and teachers,
and stimulate their interest in science. (9) Production
of video and audio: production of video and audio
materials related to scientific research results, and
posting them to online platforms (such as YouTube,
Youku, Himalaya, etc.) to attract more audiences. (10)
Apply for awards and honors: Actively apply for
various scientific research awards and honors to
improve the popularity and influence of scientific
research achievements.
In short, effective dissemination and promotion of
scientific research results requires the comprehensive
use of multiple channels and means. In this process,
establishing good cooperative relations, using new
Research on Scientific Research Communication Mode Analysis and System Design Based on New Media Big Data
427

media platforms and participating in social activities
are all important means.
In terms of research methodology, this study uses
a combination of questionnaire survey and case
analysis. Through surveys and interviews, we learned
about their needs and preferences for the mode of
dissemination of scientific work. At the same time,
we also analyze the application cases of new media
big data in the dissemination of scientific research
work, and summarize the impact of new media big
data on the communication mode of scientific
research work.
Through research and analysis, we find that the
impact of new media big data on the dissemination
mode of scientific research work in vocational
colleges and universities is mainly reflected in the
following aspects(Romano, R., 2016): first, new
media big data can improve the speed and breadth of
scientific research work, so that more people can
understand scientific research results in time;
Secondly, new media big data can provide richer and
intuitive communication content, so that scientific
research results can be displayed more vividly and
vividly; Finally, new media big data can accurately
target the audience and make the dissemination of
scientific research work more targeted and effective.
Through the analysis of a large number of
literature and actual research, we find that the main
problem of the current scientific research
communication system is that the communication
channels are too scattered, which makes the
dissemination effect of scientific research results less
than ideal. Lack of intelligent and personalized
services, lack of effective analysis and utilization of
data; the uneven quality and unattractiveness of the
content communicated (Shi, Y. K., 2022);
communication methods are not interactive enough to
enable real-time interaction with audiences.
Therefore, we need to design a scientific research
dissemination system based on new media big data to
provide more intelligent and personalized services
and improve the efficiency of data utilization.
In view of these problems, we propose a scientific
research dissemination model based on new media
big data. First, through big data analysis, accurately
locate the audience group and choose the appropriate
communication channel for scientific research results;
Secondly, through data analysis, optimize the
communication content and improve the
attractiveness and quality of the content; Finally,
through social media interaction, real-time interaction
with the audience can be achieved to improve the
communication effect.
3 SYSTEM DESIGN
In terms of system design, we propose the following
schemes: first, establish a big data analysis platform
to realize big data analysis of scientific research data
and audience data; Secondly, design an intelligent
content production system, and automatically
generate high-quality scientific research content
according to the data analysis results; Finally,
establish a social media interaction platform to
achieve real-time interaction and feedback with the
audience.
The design of the system needs to start from the
following aspects: first, establish a comprehensive
database, including scientific researchers, scientific
research institutions, scientific research projects and
other information; Secondly, intelligent algorithms
are designed to comprehensively analyze data and
realize personalized recommendations and services;
Finally, develop efficient data storage and processing
technology to ensure the stability and reliability of the
system.
Based on the above analysis, we design a
scientific research work dissemination system for
vocational colleges and universities based on new
media big data. The system includes four modules:
data collection, data analysis, data display and data
application, as shown in Figure 1.
Data
Collection
Data
Analysis
Data
Display
Data
Application
Figure 1. The system composition.
The data acquisition module is responsible for
collecting scientific research information on various
new media platforms and providing basic data for
data analysis. The data analysis module analyzes the
collected data and extracts useful information to
provide support for data display and data application.
The data display module displays the analyzed data in
an intuitive and easy-to-understand way, which is
convenient for users to understand the situation of
scientific research. The data application module
applies data to the dissemination of scientific research
work according to the needs of users, so as to improve
the communication effect and quality.
ANIT 2023 - The International Seminar on Artificial Intelligence, Networking and Information Technology
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Research
project
management
Application
Layer
Research
credit
assessment
Statistics of
scientific
research results
Data
management
Management
Layer
Credit
management
Security
management
Run
monitoring
Rights
management
Nodes
management
Network
Service
Service
Layer
Database Open API
Figure 2. The overall system deployment structure diagram.
The overall architecture of the system is divided
into three layers, which are the service layer, the
management layer, and the application layer, as
shown in Figure 2. The underlying service layer
provides basic support for system operation, mainly
including data layer, network layer, and open API.
Database layer: As the core module of the system,
it encapsulates the underlying data block and related
basic data encryption and timestamp and other basic
data and basic algorithms, and has a scientific
research related data input terminal, which stores the
input scientific research results to the server, records
and backs up the scientific research data, and
facilitates retrieval and query. University research
related data is usually large, complete data is stored
in the database, the introduction of data analysis
module, by obtaining data from the database and
performing related data analysis.
Network layer: The network layer encompasses
several components, including the P2P networking
mechanism, data dissemination mechanism, and data
verification mechanism. It also incorporates an
automatic networking mechanism where nodes
maintain communication by upholding a shared data
structure.
Open API: Defines the structure, parameters,
return values, description, and other information of
the API, and can automatically generate API
documents based on this information, so that
developers and users can quickly understand the
usage and limitations of the API. At the same time,
Open API simplifies the API development and
management process, and also improves the
reusability and maintainability of APIs. Through this
platform, developers can communicate with other
departments and project teams more easily, reducing
the time consumption caused by offline meetings and
frequent verbal communication. Through the basic
information defined by the API, test cases can be
automatically generated to help developers
implement and test APIs more quickly. API version
management makes API evolution and upgrade more
flexible and controllable. Open API is very simple to
use, its emergence promotes the development and use
of APIs, breaks down data silos, allows enterprises to
quickly achieve dataization, and also provides the
foundation and support for the continuous evolution
and upgrade of API.
The management layer includes various data
service interfaces such as scientific research results
metadata management, scientific research results
identification management, scientific research credit
retrieval services, scientific research results
traceability, scientific research data collection, as
well as core service functions such as contract
management, security management, operation
monitoring, node management, and authority control,
providing services for upper-layer business
applications and external systems.
The application layer builds scientific research
credit management and service functions for colleges
and universities, mainly including scientific research
results declaration, scientific research achievement
reporting, scientific research credit assessment,
scientific research credit retrieval, scientific research
achievement early warning, and scientific research
information statistics.
When implementing the system, we need to pay
attention to the following aspects: first, the security
and privacy of the data need to be guaranteed.
Secondly, it is necessary to avoid the problems of
information overload and information pollution.
Finally, it is necessary to continuously improve the
intelligence and personalization level of the system to
provide users with better services.
The application effect of the system has been
verified in practice and remarkable results have been
achieved. First of all, the system can monitor the
dissemination of scientific research work in real time,
and timely find and solve problems in communication.
Secondly, the system can provide personalized
communication services according to user feedback
and data analysis results to meet the needs of different
users. Finally, the application of the system improves
the dissemination effect and quality of scientific
research, and provides strong support for the
development of scientific research in vocational
colleges.
In short, the scientific research dissemination
system based on new media big data is of great
significance and value. We need to analyze the
current problems, design intelligent algorithms,
Research on Scientific Research Communication Mode Analysis and System Design Based on New Media Big Data
429

develop efficient data storage and processing
technologies, improve the security and privacy of the
system, and provide better support for scientific
research communication.
4 CONCLUSION
In summary, the analysis of the communication mode
and system design of scientific research in vocational
colleges and universities based on new media big data
is a work of great significance. Through the research
of this paper, we find that the impact of new media
big data on the communication mode of scientific
research work is mainly reflected in the
communication speed, content richness and audience
targeting. In view of these problems, we design a
scientific research work dissemination system based
on new media big data, and verify its effectiveness
and feasibility through practice. In the future, we will
continue to study the application of the system in
other fields and make more contributions to
promoting digital development.
REFERENCES
Rivas, J. G., Carrion, D. M., Tortolero, L., Veneziano, D.,
Esperto, F. Scientific social media, A new way to
expand knowledge. What do urologists need to know?
Actas Urologicas Espanolas(J), 2019, 43(5), 269-276.
https://doi.org/10.1016/j.acuro.2018.12.003
O'Keefe, C. M., & Head, R. J. Application of logic models
in a large scientific research program. Evaluation and
Program Planning (J), 2011, 34(3), 174-184.
https://doi.org/10.1016/j.evalprogplan. 2011.02.008
Julpisit, A., & Esichaikul, V. A collaborative system to
improve knowledge sharing in scientific research
projects. Information Development (J), 2019, 35(4),
624-638. https://doi.org/10.1177/026 66669 18779240
Zhao, W. B., Yin, Z. X., Fan, T. R., & Luo, J. S. Research
on influence spread of scientific research team based on
scientific factor quantification of big data. International
Journal of Distributed Sensor Networks (J), 2019,
15(4). https://doi.org/10.1177/1550147719842158
Xin, C. C., & He, C. H. Research on university scientific
research patent management information system based
on BS mode. Journal of Intelligent & Fuzzy Systems
(J), 2020, 38(2), 1371-1379.
https://doi.org/10.3233/jifs-179500
Weller, K. Accepting the challenges of social media
research. Online Information Review (J), 2015, 39(3),
281-289. https://doi.org/10.1108/oir-03-2015-0069
Liu, L., & Luan, J. Survey analysis and discussion on
cultivating scientific research quality among
undergraduates in medical colleges. Pharmacology
Research & Perspectives (J), 2023, 11(3).
https://doi.org/10.1002/prp2.1095
Chen, C., Zhe, C., Zheng, Y. Y., Xiong, X., Xiao, T., & Lu,
X. F. Evaluation of Scientific Research in Universities
Based on the Theories for Sustainable Competitive
Advantage. Sage Open (J), 2023, 13(2).
https://doi.org/10.1177/21582440231177048
De Winter, J., & Kosolosky, L. The Epistemic Integrity of
Scientific Research. Science and Engineering Ethics
(J), 2013, 19(3), 757-774.
https://doi.org/10.1007/s11948-012-9394-3
Levine, F. J., & Iutcovich, J. M. Challenges in studying the
effects of scientific societies on research integrity.
Science and Engineering Ethics (J), 2003, 9(2), 257-
268. https://doi.org/10.1007/s11948-003-0012-2
Post, R. Constitutional Restraints on the Regulations of
Scientific Speech and Scientific Research. Science and
Engineering Ethics (J), 2009, 15(3), 431-438.
https://doi.org/10.1007/s11948-009-9133-6
Saeed-Ul, H., Imran, M., Gillani, U., Aljohani, N. R.,
Bowman, T. D., & Didegah, F. Measuring social media
activity of scientific literature: an exhaustive
comparison of scopus and novel altmetrics big data.
Scientometrics(J), 2017, 113(2), 1037-1057.
https://doi.org/10.1007/s11192-017-2512-x
Romano, R., & Davino, C. Assessing scientific research
activity evaluation models using multivariate analysis.
Statistics and Its Interface (J), 2016, 9(3), 303-313.
https://doi.org/10.4310 /SII .201 6.v9.n 3.a5
Shi, Y. K., Wang, D. C., & Zhang, Z. M. Categorical
Evaluation of Scientific Research Efficiency in Chinese
Universities: Basic and Applied Research.
Sustainability (J), 2022, 14(8).
https://doi.org/10.3390/su14084402
ANIT 2023 - The International Seminar on Artificial Intelligence, Networking and Information Technology
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