DOI : 10.17577/IJERTCONV14IS040015- Open Access

- Authors : Sanya Ruhela, Prakhar Kumar, Asmita Suri, Akshita Saini, Dr. Mohd Salman Khan
- Paper ID : IJERTCONV14IS040015
- Volume & Issue : Volume 14, Issue 04, ICTEM 2.0 (2026)
- Published (First Online) : 24-05-2026
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License:
This work is licensed under a Creative Commons Attribution 4.0 International License
Versona: – An Integrated Ecosystem of Professional and Social Networking for Indian Youth
Sanya Ruhela¹, Prakhar Kumar², Asmita Suri³, Akshita Saini, Dr. Mohd Salman Khan
1Department of Computer Science & Engineering (DS), Moradabad Institute of Technology,
Moradabad, India
a) sanyaruhela@gmail.com
2Department of Computer Science & Engineering (DS), Moradabad Institute of Technology,
Moradabad, India
b)kumarprakhar700@gmail.com
3Department of Computer Science & Engineering (DS), Moradabad Institute of Technology,
Moradabad, India
c)asmitasuri5@gmail.com
4Department of Computer Science & Engineering (DS), Moradabad Institute of Technology,
Moradabad, India
d)akshitasaini574@gmail.com
5Department of Computer Science & Engineering (DS), Moradabad Institute of Technology,
Moradabad, India
e)salmank64@gmail.com
ABSTRACT
The rapid adoption of digital platforms by Indian youth for entertainment, academics, and career guidance has led to fragmented and inefficient digital usage patterns.
This study presents Versona, a youth centirc hybrid Web and Android platform developed to integrate social interaction, college students and professionals within a single system. The system employs a dual mode feed system (entertainment driven and career focussed) and a hybrid backend architecture combining firebase and fast API for managing real-time services, user interactions and AI based processing.
KEYWORDS: Digital networking systems, youth-centric applications, dual feed content, automated resume parsing, content recommendation mechanisms, hybrid backend design, Web and mobile
platforms, AI-assisted professionals, scalable college, social systems.
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INTRODUCTION
The everyday digital activities of Indian Youth rely on multiple independent applications, [10]-[14] for social interactions, academic collaboration, and career exploration which requires the users to switch frequently between these platforms which causes inefficient digital workflows.
This study introduces Versona, youth based holistic networking system developed to centralize community engagement, content sharing and career- based services within a single platform.
The development of Versona is driven by the urgent need to address the diverse
digital workflows and siloed platform offerings currently impacting Indian youth.
Most contemporary digital platforms tend to focus either on entertainment or professional networking, [1],[5]offering limited holistic support for Indian college students and early-career users. The need for AI-based features and wide-ranging use of mobile technologies, highlights the need for a unified digital innovation, fostering the establishment of Versona for entertainment and professional networking platform [15],[17].
The study examines the motivation, system design and implementation findings to validate the feasibility of an integrated youth networking system.
Taken together, these factors highlight the development of Versona as a hybrid networking platform.
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LITERATURE REVIEW
Prior research demonstrates that user- centric content structuring significantly improves engagement and interaction levels across social platforms [1], [17],[25] which separates both entertainment content from goal-driven or professional content enabling users to combine more efficiently with digital platforms. Research also highlights that combined backend architectures into real-time data handling with standard application interfaces can easily handle high user concurrency system performance [19], [22], [24].
Recent progress in artificial intelligence reflect that automated methods for content filtering assist in minimizing inappropriate interactions and improve overall platform safety. In the same way, AI-based resume parsing supports the efficient extraction of user skills and experience, which improves the overall accuracy of job-matching processes [7], [15], [29]. While many current platforms leverage technology to
easily handle specific purposes either entertainment based or professional driven services. Few studies have explored systems that integrate dual factors within a single environment for young users [6], [23]. This limitation forms the basis for proposing Versona, a holistic social professional networking platform designed for Indian youth.
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CHALLENGES AND LIMITATIONS
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Combining both social and professional networking within a single ecosytem increases the system and design complexity.
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Efficient management is required for supporting real-time features along with AI-driven services to maintain performance.
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Data Privacy and content management represent key challenges in youth-focused networking platform.
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The execution of AI-driven elements is influenced by quality and range of training data.
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Analysis of system was restricted to initial implementation, and large scale execution may outline further challenges.
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RESEARCH METHODOLOGY
Versona is an integrated social-professional networking platform that was designed, implemented, and evaluated using a methodical approach. To guarantee trustworthy research results, the methodology places a strong emphasis on performance evaluation, organised development, and feasibility analysis.
4.1. Requirement analysis and problem identification
The first step was to identify issues related to Indian youths dispersed use of various digital platforms for professional
networking, academic communication, and social engagement were identified. To understand constraints relevant to content accuracy, integration, and scalability, a qualitative analysis of current platforms was carried out. These findings led to the definition of functional requirements that would direct the creation of the suggested system.
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System Design Approach
In order to facilitate both real time interaction and complex analytics services, a modular system design was created based on the communicated specifications. The design placed a strong emphasis on scalability, effective data flow between system components, and separation of concerns. Architectural choices were taken to guarantee adaptability and ease of future improvements.
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Implementation Plan
During the implementation phase, an integrated backend infrastructure and a cross-platform frontend were developed to support Web and Android users. While an asynchronous API layer managed AI- driven features like resume parsing, content moderation, and real-time services were used for user authentication, content feeds, and messaging.
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AI-Based Service Integration
In order to improve platform functionality and user experience, artificial intelligence algorithms were included. These included relevancebased content prioritisation, automatic content moderation to enhance platform security, and resume information extraction for professional networking assistance. In order to reduce performance overhead, the AI modules were made to function independently of real-time services.
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System Evaluation and Validation
Functional testing and performance observation were used to evaluate the responsiveness, scalability, and usability of the suggested system. System latency, user interaction patterns, and processing efficiency were among the metrics examined under normal usage circumstances.
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- h2>PROPOSED SYSTEM ARCHITECTURE
This section illustrates the overall workflow that is used to process and distribute user interactions with the aim to demonstrate the planned system architecture of Versona. In order to facilitate professional relationships and real-time social interactions the structure describes how frontend components interact with the backend services layers. The high-level architecture framework and hybrid backend strategy used to efficiently manage immediate tasks and AI-driven computations are described in the following subsections.
Figure 1:Workflow of Verosna
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Architectural Overview
Versona is implemented using a layer- based architecture that combines social and professional networking within a single ecosystem. The system is composed of a frontend layer for user interactions and a
backend layer for data processing and a data management layer for storage.
This layered design allows for scalability, maintainability and real time responses.
Figure 2: Architectural Overview
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Hybrid Backend Approach
The platform uses a hybrid backend architecture that divides the computational chores from immediate operations. While an autonomous API layer controls AI-based processing like content filtering, and resume analysis and real time services that help in regulate interactions and content feeds. This section enables the systems efficiency and flexible growth of system elements.
Figure 3: Hybrid Backend Architecture
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ALOGORITHM AND IMPLEMENTATION
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Dual Feed Ranking Logic
The proposed approach separates the content into professional and fun-based using a double feed evaluation approach. A ranking score is determined for every content based on the user interactions, behaviour and personal significance . This approach minimizes the amount of data while improving the value of the content.
The weighted formula is used to figure out every content rating score: Ri=Vi+Ei+Pi
Symbol
Explanation
Role
Vi
Content view duration
Reflects viewing interest
Pi
User Content relevance
factor
Personalization
Weight Factor
Controls
Weights
Interaction
Coefficient
Adjusts
Engagement
Relevance Influence
Factor
Tunes Personalization
Table 1: Dual Feed Content Prioritisation Considerations
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Parsing Pipeline for AI Resumes
In order to retrieve organised professional data from the uploaded documents, the resume parsing component uses AI methods. The extraction of text and preliminary processes are the first steps that are then followed by the recognition of entities to retrieve data about the background knowledge, expertise and skills. After the extraction process, the
information is then arranged for expert matching.
A grading system is applied to determine entity relevance.
Se=k=1nwkfk, where fk =feature frequency wk=matching weights
Steps
Description
Results
1
Conversion
of text
Raw Text
Information
2
Cleaning and
Refinement of Text
Normalized Text
3
Entity
Detection
Skills and
Experience
4
Data Organization
Structured Professional and Corporate
Profile
Table 2: Phases of AI-Powered Resume Information Extraction
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Design of Database Schema
In order to deliver flexible storage of information and immediate retrieval, a document based database structure is designed. In order to offer reliable data access, growth exclusive collections have been created for users, posts, chats as well as expert data.
The formula that evaluates the average query response time:
Tq=n1i=1nti, where ti=execution time of each query
User
User Profiles, details
User
Personaliza tion
Content
Entertainment and Professional Posts
Feed Curation
Message
Single and group conversations
Real-Time
Chat System
Career
Skills, Resumes, Notes,
Career
Based Features
Table 3: Data Storage Component Organisation in the Versona Platform
Figure 4: Feature Usage Distribution
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PERFORMANCE ANALYSIS
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Latency Analysis
Component Type
Average Latency(ms)
Loading of Content
Feed
280
Delivery of
Messages
150
Profile Data Fetching
320
AI Resume Parsing
650
In the context of Versona regular operations such as content loading, chats, messages and AI driven computation is evaluated using system latency. Despite handling data processing tasks, the unique backend structure helped in sustaining low response rates. The latency comparison across the system components is shown:
Data Category
Description
Usage
Table 4: Average Reaction Time for Platform Functions
Figure 5: API Latency Distribution Analysis
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User Engagement Metrics
Theoretical parameters gathered from patterns of communication including posts, likes, comments and official networking behaviour were implemented to evaluate user involvement. Better user interaction contributed from the breaking down of the content into two feed that additionally enhanced accuracy and reduces the overload of information.
Metrics
Pattern Observed
Average Session
Duration
Increased
Interaction Rate
Medium to High
Completion of
Profile
Improved
Feed Relevance
High
Table 5: Measures of User Engagement across Platform Elements
Figure 6: Average API Latency across Network Conditions
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Comparative Analysis
The proposed framework was contrasted to existing ones that give emphasis on workplace interactions or entertainment contact. The analogy illustrates the positive effects combining both features on a single platform.
Features
Curren t Social Media
Platfor ms
Current Professi onal
Platfor ms
Verson a
Social Interacti
on
Yes
Limited
Yes
Career- Oriented
Features
NO
Yes
Yes
College Commun ity
Support
Limited
No
Yes
<>AI Based Services
Limited
Limited
Yes
Unified
Platform
No
No
Yes
Table 6: Versonas Feature Level Comparison with Current Digital Platforms
Figure 7: Comparative Platform Analysis
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TECHNOLOGY STACK AND DESIGN CONSIDERATIONS
Versonas technology was selected by the means of requirement focussed research that emphasis AI deployment, flexibility, and versatility and immediate interactions with the users. A holistic architecture was implemented to take advantage of a single unified backend to reconcile the hosting services with a customised administration ensuring system adaptability as well as the development and effectiveness.
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Frontend Technologies
Due to their compatibility across platforms and component driven architecture, React was chosen for the Web Applications and React Native for Android platforms. This approach ensures native-level efficiency while enabling UI consistency and shorter development cycle and smoother maintenance. Future platform growth is supported by the common design paradigm without demanding an extensive change.
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User Management and Authentication
To manage the identification of the user securely and effectively, Firebase Authentication was adopted. Versona offers multiple sign-in methods suitable for a youth-oriented portal while reducing operational effort and risk to security by leveraging a managed authentication service.
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Real time Communication and Databases
Because of its real-time data management capabilities which prove essential for instant feeds, messages and notifications, Firestore was chosen as the primary data store .Without demanding abrupt transfers, its flexible NOSQL allows for evolving academic, and professional data models.
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Tailored Backend Services
FastAPI was chosen to implement complicated backend services, AI processing, and business logic High- performance API is rendered by its asynchronous architecture, and its Python framework enables simple and easy integration using machine learning and NLP processing frameworks. In addition to this, Fast API is a suitable and best fit for AI-driven functions like managing content and resume parsing.
Web sockets are implemented together with Fast API to enable bi directional, low latency interactions for real time alerts and chat.
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Performance Optimization
Redis serves as an in-memory caching layer to increase response time and reduce database load which is frequently accessed such as feed data and session information. Scalability is improved under significance as a result.
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AI-Integration
Resume analysis, feed customisation, and content filtering are the examples of how Artificial Intelligence boost usability. Because AI services are built as a modular components, they are upgraded continuously without negatively impacting the integrity of the main platform.
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FUTURE RESEARCH DIRECTIONS
The proposed Versona platform offers a number of possibilities for future research and development. Future studies could rely on improving customisation using complex recommendation systems that adapt to shifting user experience. Scalability optimisation is still an essential and a crucial field, especially when it comes to managing greater number of user and
interaction volumes in practical implementations.
Stronger safeguards for privacy, multilingual assistance, and the integration of internship and job recommendation tools can all be investigated further. Deeper insights into system performance, usability, and overall impact can be acquired by conducting long-term user studies and assessing the system under the prolonged functional environments.
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CONCLUSION
The hybrid social-professional networking platform Versona was introduced in this study as a solution to the fragmentation of digital services that young Indians frequently use. The suggested solution combines professional networking, social engagement, and college communities into a single design that is backed by a hybrid backend framework. The platform offers responsive performance while enabling AI- driven features like content moderation and resume analysis by separating real-time services from computationally demanding operations.
Experimental observations indicate that the proposed hybrid architecture effectively supports both social engagement and career-oriented interactions within a unified youth-focused ecosystem. In general, Versona emphasises the potential of intelligent system design and hybrid architectures in creating networking platforms for contemporary digital environments that are youth-focused and scalable.
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REFERENCES
-
Y. Ahn, S. Han, H. Kwak, S. Moon, and
H. Jeong, Analysis of Topological Characteristics of Large-Scale Online Social Networks, in Proc. 16th Int. World Wide Web Conf. (WWW), Banff, Canada, May 2007.
-
S. Amer-Yahia, L. Lakshmanan, and C. Yu, SocialScope: Enabling Information Discovery on Social Content Sites, in Proc. CIDR, Asilomar, CA, USA, 2009.
-
W. Yang and Q. Xu, Analysis of User Behavior in Online Social Media Platforms, in Proc. Int. Joint Conf. on Information, Media and Engineering (ICIME), 2018.
-
S. Gurses and C. Diaz, Two Tales of Privacy in Online Social Networks, IEEE Security & Privacy, vol. 11, no. 3, pp. 29 37, 2013.
-
L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan, Group Formation in Large Social Networks: Membership, Growth, and Evolution, in Proc. 12th ACM SIGKDD, 2006.
-
S. Liao and T. Saeheaw, Digital Media Interaction Models for Youth-Centric Platforms, in Proc. Joint Int. Conf. on Digital Arts, Media and Technology (ECTI DAMT & NCON), 2025.
-
M. Gupta, R. Kumar, A. Sharma, and A.
S. Pai, AI-Driven Personalization in Social Media Applications, in Proc. 14th Int. Conf. on Computing Communication and Networking Technologies (ICCCNT), 2023.
-
S. Buchegger and A. Datta, A Case for P2P Infrastructure for Social Networks: Opportunities and Challenges, in Proc. 6th Int. Conf. on Wireless On-Demand Network Systems and Services, Snowbird, UT, USA, Feb. 2009.
-
S. Buchegger, D. Schiöberg, L.-H. Vu, and A. Datta, PeerSoN: P2P Social Networking Early Experiences and Insights, in Proc. ACM SocialNets Workshop, Nuremberg, Germany, Mar. 2009.
-
A. M. Kaplan and M. Haenlein, Users of the World, Unite! The Challenges and
Opportunities of Social Media, Business Horizons, vol. 53, no. 1, pp. 5968, 2010.
-
G. Appel, L. Grewal, R. Hadi, and A.
T. Stephen, The Future of Social Media in Marketing, Journal of the Academy of Marketing Science, vol. 48, pp. 7995, 2020.
-
R. L. Narayan, The Future of Social Media Networking, Tech. Rep., Nov. 2019. (Introductory reference)
-
Trust Insights, Social Media 2020: Where to Invest Time and Resources, White Paper, Sep. 2019. (Introductory reference)
-
M. Quadros, Five Social Media Trends to Follow, Socialbakers Report, May 2020. (Introductory reference)
-
S. Mili and R. Singh, Generative AI in Social Media Marketing: Revolutionizing Interaction and Content Production, Journal of Emerging Technologies and Innovative Research (JETIR), Apr. 2024.
-
M. Alghizzawi et al., Short Video Addiction and Well-Being: A Pilot Study, in Proc. IEEE Global Digital Health Conf., Sep. 2024.
-
S. Milli et al. Engagement-Based Ranking Algorithms and the Amplification of Divisive Content, PNAS Nexus, vol. 3, no. 2, Apr. 2024.
-
H. S. Almarashdi et al., Multimedia Interaction and Engagement Modeling in Social Platforms, in Proc. IEEE Int. Conf. on Multimedia Computing, Networking and Applications (MCNA), 2024.
-
V. Bibhu et al., Social Network Analysis Techniques for Online Communities, in Proc. IEEE Int. Conf. on Tools with Artificial Intelligence (ICTAI), 2021.
-
M. B. Abisado et al., User Engagement Metrics in Online Learning and Social Platforms, in Proc. IEEE PIC, 2024.
-
B. Chobthamdee and C. Sukwan, Digital Behavior Patterns of STEM Learners in Online Networks, in Proc. Int. STEM Education Conf., 2022.
-
A. Dezhboro et al., Computational Models for Social Network Dynamics, IEEE Trans. on Computational Social Systems, 2025.
-
T. Oktavia et al., Smart Society Platforms and Social Connectivity, in Proc. Int. Conf. on ICT for Smart Society (ICISS), 2016.
-
U. Tanuja, H. L. Gururaj, and V. Janhavi, Scalable Communication Frameworks for Social Applications, in Proc. COMSNETS, 2019.
-
M. Hegde, I. McCulloh, and J. Piorkowski, Network Structure and Community Detection in Social Platforms, in Proc. IEEE/ACM ASONAM, 2019.
-
D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent Dirichlet Allocation, Journal of Machine Learning Research, vol. 3, pp. 9931022, 2003.
-
R. Burke, Hybrid Recommender Systems: Survey and Experiments, User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331370, 2017.
-
C. Rudin, Stop Explaining Black Box Machine Learning Models, Nature Machine Intelligence, vol. 1, pp. 206215, 2019.
-
A. Mishra and R. Singh, Sentiment Analysis in Social Media Using Deep Learning Techniques, in Proc. Int. Conf. on Innovative Computing (ICSES), Dec. 2024.
-
X. Li et al., Intelligent Short Video Recommendation System Based on Deep Learning, in IEEE Conf. Publications, Apr. 2024.
