METAVERSE : THE NEW INTERNET PRODIGY

A Seminar Report

Submitted By
Om Raykar PRN Number 72246665L
UNDER THE GUIDANCE OF
Prof Surabhi Kaul
in partial fulfilment for the award of the degree

of

BACHELOR OF ENGINEERING

IN

INFORMATION TECHNOLOGY ENGINEERING

Zeal College of Engineering and Research Narhe, Pune

Department Of Information Technology Year

2024-25

SAVITRIBAI PHULE PUNE UNIVERSITY: PUNE

 ACKNOWLEDGEMENT

I am sincerely express our wholehearted thanks to the principal Dr.Ajit Kate , Zeal College of Engineering
and Research Narhe, Pune. for his constant encouragement and moral support during this project.

I owe my sincere thanks to Prof Balaji Chaugule, Head of the Department of Information Technology
Engineering, Institute of Zeal College of Engineering and Research Narhe, Pune. for furnishing every essential
facility for doing this project.

I sincerely thank my guide Prof Jyotsna Nanajkar , Department of Information Technology Engineering,
Zeal College of Engineering and Research Narhe, Pune. for his valuable help and guidance throughout the
project.

It gives me a great pleasure in presenting my seminar report on “Metaverse : The new internet prodigy”
 Abstract

The Digital Future of the World is on the verge of being revolutionized. The Technology, wisdom, and

internet thinking of Metaverse will extend the impacts on all aspects of life including education

,economy,politics, life, and culture. This study is anoverview of the metaverse applications in the near

future. The outcomes of this review are aimed to understand the usage and importance of metaverse in

various fields by using augmented and virtual realities. Finally, we propose the application of Metaverse in

education, economy, politics, and entertainment because of its huge future implications.

We also discuss the turnover of classic internet to modern metaverse systems as metaverse gradually grows

to become the new internet surpassing W3 technology

 Keywords

1) Metaverse
2) Artificial intelligence
3) Internet of things
4) Virtual Reality
5) Extended reality
6) Blockchain
7) Cyber world
8) Internet
9) Technological Intervention
10) Augmented Reality
 Contents

1 INTRODUCTION 1
1.1 Metaverse 1

1.2 Objectives 2

1.3 Motivation 2

2 LITERATURE REVIEW 3
3 METAVERSE 4

3.1 What is metaverse 4

3.2 Characteristics of metaverse 4
3.3 Traditional internet 5
3.4 Difference between W3 and 5
metaverse

4 METAVERSE : THE NEW

INTERNET PRODIGY 6
4.1 Problem Statement 6

5 FEATURE EXTRACTION 7
5.1 Blockchain 7
5.1.1 Definition 7
5.1.2 Function 8
5.2 6G Internet 8
5.2.1 Definition 9
5.2.2 Function 9
5.3 Transformation 9

6 MODEL CONSTRUCTION 10
6.1 Transformation Process Model 10
6.1.1 Process Explanation 10
6.1.2 Execution 10
11
11

11

7 CHALLENGES IN EXECUTION 12
7.1Challenges 12

8 COMPONENTS NEEDED 13
8.1 Virtual Augmented Reality 13
8.2 High-Bandwidth Networks 13
8.3 AI and IoT 13
14
14

9 EVALUATION METRICS
15

10 RELATED AREA 16
10.1 Spatial computing 16
10.2 Digital Twins 16
10.3 Edge Computing 16

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Conclusion
 18
References

List of figures
 15
9.1 Evaluation Matrix
 Chapter 1 Introduction

The Digital Future of the World is on the verge of being revolutionized. The Technology,
wisdom, and internet thinking of Metaverse will extend the impacts on all aspects of life
including education, economy, politics, life, and culture. This study is an overview of the
metaverse applications in the near future. The outcomes of this review are aimed to understand
the usage and importance of metaverse in various fields by using augmented and virtual
realities. Finally, we propose the application of Metaverse in education, economy,
politics, and entertainment because of its huge future implications. The term metaverse came
from Neal Stephenson’s Novel “Snow Crash” (Joshua, J., 2017). Moreover, the entertainment
movie ready player one gave the idea of a virtual world; that could be accessed with headsets.
Metaverse is a 3D virtual fictional environment where users can create their fictional
representation and fictional environment. The metaverse integrates various technologies like
artificial intelligence, virtual reality, computer vision, blockchain, and the internet of
things (Yousefpour et al., 2019).

1.1 Metaverse
The concept of the Metaverse is an example of Extended Reality, the reality which tries to move
from reality or creates a different reality altogether. Metaverse is a digital world where people
can meet and interact virtually. The phenomenon of
meeting digitally is the same as getting to know others in reality. It aims to run a universe
parallel to the virtual world with an environment similar to the digital one. Virtual reality is
the base of the metaverse, which is further based upon the extended reality transforming the
physical world into a networked one. Comprehensive reality has the potential to
enable engrossing telepresence, which can reduce our difficult tasks. Vast reality technology
can facilitate healthcare, education, work, and entertainment (Xi et al., 2022).Figure 1: Concept
of Extended Reality, Source: Shutter Stock In this study, we have discussed the overall impact
the Metaverse will have on society. We have also studied the patterns in which it should be
implemented in our society. The importance, usefulness, and implication of this interactive
world is the Digital Future of today. The culture of today is digital culture, human is digital
human, and the world is Metaverse. Finally, we describe the futuristic framework for its
implementation of Metaverse in the future.

1
.

1.2 Objective
The objective of the metaverse is to create an immersive, interconnected virtual environment
that simulates real-life experiences. It aims to facilitate social interactions, commerce, and
creative expression through avatars, allowing users to engage in activities like shopping,
gaming, and attending events in a shared digital space. The metaverse seeks to enhance user
presence and interactivity, bridging the gap between physical and digital realms while
fostering a decentralized economy where users can create and own content.

1.2 Motivation
The motivation behind the metaverse is to create an immersive, interconnected virtual space
that simulates real-life experiences and interactions. It aims to enhance social connectivity,
allowing users to engage in activities such as shopping, gaming, and collaboration through
customizable avatars.

Additionally, the metaverse seeks to empower users by decentralizing control, enabling them
to create, own, and trade digital assets without central authority. This vision promotes
community building, economic opportunities, and a new digital frontier for brands to engage
with consumers innovatively

2
 Chapter 2

Literature Review

SANG-MIN PARK received the Ph.D. degree in computer engineering from the Department
of Computer Science and Engineering, Korea University, in 2016. His current research interests
include natural language processing, cognitive science, sentiment analysis, causal inference,
multi-modal analysis, personalized service, generative model, and reinforcement learning.

VU TUAN TRUONG received the B.Eng. degree in electrical and computer engineering
from
Hanoi University of Technology and Technology (HUST), Vietnam, in 2021. He is currently
pursuing the M.Sc. degree with the Institut National de la Recherche Scientifique (INRS),
University of Quebec, Montreal, QC, Canada. His research interests include blockchain and
enabling technologies for metaverse.

Faiza Khalid is currently working on her Ph.D. Dissertation at Riphah International University,
Rawalpindi. Her Topic is ‘Metaverse and
Digital Acculturation: A Media Ecological Analysis’. She did her MS from International
Islamic University Islamabad, Pakistan in 2014. She is
in Teaching Profession since 2009. She has a diverse experience in teaching, PR, and
Administration in Fatima Jinnah Women University,
National College of Arts, International Islamic University Islamabad, and many other private
organizations. She is currently working as a
Lecturer in the Department of Media and Communication at the National University of Modern
Languages, Islamabad, Pakistan..

3
4
 Chapter 3

Metaverse

3.1 Definition

The term metaverse is a three-dimensional virtual reality occupied by avatars of real people.
This study was aimed at exploring the development status of the metaverse from various
perspectives of network organization such as virtual reality object connection, virtual world
conjunction, and management technology. This paper collected data from
different countries and examine the status of the metaverse and predict the problems and
challenges it can face (Ning et al, 2021).
Literature illustrated that metaverse is shaping the future of consumer research and practice
which described that future research suggested that metaverse will be beneficial to advertising,
digital marketing, branding, value creation, and consumer behavior. These times, people mostly
do online shopping which saves their time and energy. So in the future metaverse will change
the marketing and business community (Belk, Buhalis, Flavian & Lartey, 2022). In the context
of avatar engagement and new biometrics along the customer journey, the interactive and
immersive elements of the metaverse will produce a significant level of data while research on
application design develops and evaluates design artifacts to engage users

5
and promote user consumption through the patterns discovered from consumer behavior
studies, consumer behavior research empirically evaluates the consumer behavioral responses
(e.g., level of acceptance and purchase intention) impacted by certain design artifacts (Flavián,
Ibáñez-Sánchez, & Orús, 2019).

3.2 Characteristics of Metaverse

The metaverse has several defining characteristics:
Immersive Experiences: Utilizes VR and AR technologies to create engaging environments
where users can interact as if they were in the physical world12.
Digital Avatars: Users create personalized avatars to represent themselves, facilitating social
interactions and self-expression13.
Interconnectivity: Virtual worlds are seamlessly linked, allowing users to navigate between
different environments without disruption34.
Decentralization: Built on blockchain technology, it empowers users with control over their
digital assets and experiences, reducing reliance on centralized authorities15.
Persistence: The metaverse continues to exist and evolve even when users are offline,
ensuring a dynamic and ongoing experience.

3.3 Traditional Internet

The traditional internet is a global network of interconnected computers that facilitates

communication and information sharing using standardized protocols. Key characteristics
include:
Decentralization: Control is distributed across multiple networks rather than centralized in
one authority, allowing for diverse access points and governance.
Accessibility: Available 24/7, enabling users to access vast amounts of information from
anywhere with an internet connection.
Interoperability: Different devices and networks can communicate using common protocols
like TCP/IP, ensuring seamless data exchange.
Dynamic Nature: The internet is constantly evolving, with new content and services added
regularly, making navigation sometimes complex.
Public and Collaborative: It encourages user-generated content and collaboration, allowing
anyone to share information widely

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3.Difference Between W3 and Metaverse

Web3 and the metaverse represent distinct yet interconnected concepts within the digital
landscape:

Web3: Focuses on decentralization, utilizing blockchain technology to empower users with

control over their data and digital assets. It aims to create a more democratic internet where

individuals can own and govern their online experiences without centralized authority 12.
Metaverse: Envisions an immersive, interactive virtual environment where users can engage

in social interactions, gaming, and commerce through avatars. It encompasses 3D spaces that
blend augmented and virtual reality, facilitating shared experiences across different platforms
14.

While Web3 provides the infrastructure for decentralized applications, the metaverse serves
as a dynamic platform for user engagement and interaction

Chapter 4

METAVERSE : THE NEW INTERNET PRODIGY

4.1 Problem Statement

 The integration of enhanced social activities and neural-net methods requires a new
definition of Internet suitable for the present, different from the previous technology.
 We summarize the limitations and directions for implementing the immersive
Metaverse as social influences, constraints, and open challenges.
 Therefore, see how metaverse has the capability of becoming the new internet.

7
 Chapter 5

Feature Extraction

5.1 Blockchain

Blockchain, as its name suggested, is a chain of consecutive blocks linked together. Each

block includes two parts, which are block header and block body. In general, the body

of a block contains a certain amount of data. If these data are financial transactions (e.g.,

sending cryptocurrency from one node to another node), the blockchain can be

considered as a ledger, while the native currency being traded is called cryptocurrency. That

is why blockchain technology sometimes referred to as Distributed Ledger technology.

On the other hand, the block header often contains at least three fields. The first one is the

Merkle root, which is the root hash of the Merkle tree whose leaves are all transactions

8
in the body of the block [32]. The second field is the

hash of the previous block’s header, while the third one is the time stamp, which estimates the

time when a block is created. This general blockchain structure is shown in

5.1.1 Linguistic Based

The metaverse significantly influences language learning, particularly in enhancing

vocabulary acquisition and retention. Studies show that immersive environments foster

engagement and community feeling among learners, leading to improved educational

outcomes. For instance, research highlights substantial gains in vocabulary learning when

students use metaverse technologies compared to traditional methods, emphasizing the

importance of interactive and gamified approaches123. However, challenges exist, including

the need for effective instructional design and the limitations of current research on metaverse

applications in language education45. Overall, the metaverse offers promising avenues for

innovative language teaching strategies.

5.1.2 Visual Based

The metaverse is revolutionizing visual experiences across various domains.

Visual Effects and World-Building: VFX companies are crucial in creating immersive
environments, leveraging advanced technologies like photogrammetry to recreate realistic
worlds, enhancing storytelling and user engagement1.
Interactivity and Collaboration: Virtual and augmented reality enable users to interact in lifelike
settings, bridging the gap between digital and physical spaces. This enhances remote
collaboration, making meetings feel more natural

5.2 Social Context Feature

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The metaverse transforms social interactions by creating immersive, 3D environments where
users can engage through avatars, enhancing the sense of presence and connection.
Key Features:
Real-Time Interaction: Users can socialize, attend events, and collaborate in a shared space,
making online interactions feel more personal and immediate13.

Global Networking: It breaks geographical barriers, allowing individuals to connect with

others worldwide, fostering diverse social circles14.5.2.1 User Based
5.2.2 Post Based

People express their emotions or opinions towards fake news through social media posts, such
as skeptical opinions, sensational reactions, etc. Thus, it is reasonable to extract post-based
features to help and potential fake news via reactions from the general public as expressed in
posts. Post-based features focus on identifying useful information to infer the veracity of news
from various aspects of relevant social media posts. These features can be categorized as post
level, group level, and temporal level. Post level features generate feature values for each post.
The aforementioned linguistic-based features and some embedding approaches for news
content can also be applied for each post.

5.2.3 Network Based

The metaverse offers a unique user-based experience that enhances social interaction and
engagement through immersive environments.

Key Features:
Avatar Customization: Users create personalized 3D avatars, facilitating more authentic
interactions and self-expression in virtual spaces 3.
Real-Time Collaboration: Enhanced communication tools allow users to work together
seamlessly across global distances, making teamwork more engaging and efficient

10
 Chapter 6

Model Construction

In the previous section, we introduced features extracted from different sources, i.e., news
content and social context, for metaverse. In this section, we discuss the details of the model
construction process for several existing approaches. Specifically we categorize existing
methods based on their main input sources as: News Content Models and Social Context
Models.

6.1 News Content Model

In this subsection, we focus on news content models, which mainly rely on news content
features and existing factual sources to classify fake news. Specifically, existing approaches
can be categorized as Knowledge-based and Style-based.

6.1.1 Knowledge Based

The metaverse is reshaping journalism and news content through immersive, interactive
experiences.
Key Aspects:

11
Content Innovation: The integration of virtual reality (VR) and augmented reality (AR)
allows for the creation of engaging, 3D news stories that enhance user experience and
understanding12.
New Journalistic Roles: Journalists may evolve into hybrid roles, utilizing avatars and AI to
report news in immersive environments, changing the nature of news delivery and audience
engagement2

6.1.2 Style Based

Content Innovation: The integration of virtual reality (VR) and augmented reality (AR) allows
for the creation of engaging, 3D news stories that enhance user experience and
understanding12.
New Journalistic Roles: Journalists may evolve into hybrid roles, utilizing avatars and AI to
report news in immersive environments, changing the nature of news delivery and audience
engagement2Hypothesis) and various forensic tools including Criteria-based Content Analysis
and Scientific-based Content Analysis have been developed.

Objectivity-oriented approaches capture style signals that can indicate a decreased objectivity
of news content and thus the potential to mislead consumers, such as hyperpartisan styles and
yellow-journalism. Hyperpartisan styles represent extreme behavior in favor of a particular
political party, which often correlates with a strong motivation to create fake news.

6.2 Social Context Based

The nature of social media provides researchers with additional resources to supplement and
enhance News Content Models. Social context models include relevant user social
engagements in the analysis, capturing this auxiliary information from a variety of perspectives.
We can classify existing approaches for social context modeling into two categories: Stance-
based and Propagation-based. Note that very few existing fake news detection approaches have
utilized social context models. Thus, we also introduce similar methods for rumor detection
using social media, which have potential application for fake news detection.

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6.2.1 Stance Based
Stance-based: Stance-based approaches utilize users' viewpoints from relevant post contents to
infer the veracity of original news articles. The stance of users' posts can be represented either
explicitly or implicitly. Explicit stances are direct expressions of emotion or opinion, such as
the \thumbs up" and \thumbs down" reactions expressed in Facebook.

6.2.2 Propagation Based

Propagation-based: Propagation-based approaches for fake news detection reason about the
interrelations of relevant social media posts to predict news credibility. The basic assumption
is that the credibility of a news event is highly related to the credibilities of relevant social
media posts.
Chapter 7

Assessing Metaverse

Accessing the metaverse can be straightforward and varies based on the desired experience.

Here are the primary methods:

Web Browser: Many platforms, like Decentraland and Cryptovoxels, can be accessed directly
through a web browser on a computer or mobile device, making it easy for casual users to

explore virtual worlds without special equipment 12.

VR Headset: For a fully immersive experience, using a VR headset like the Meta Quest 2
enhances interaction within 3D environments. This method is ideal for enthusiasts seeking
deeper engagement 13.

AR Glasses: Augmented reality glasses offer a blend of real and virtual experiences, though
this technology is still developing

Chapter 8

Softwares Involved

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Key software technologies involved in the metaverse include:

Blockchain: Provides decentralized infrastructure, enabling secure ownership and

transactions through NFTs and cryptocurrencies12.

Augmented Reality (AR) and Virtual Reality (VR): Essential for creating immersive
experiences, allowing users to interact in 3D environments124.

Artificial Intelligence (AI): Enhances user interaction via avatar creation, natural language
processing, and personalized experiences23.

Computing: Reduces latency by processing data closer to users, crucial for real-time
interactions in virtual spaces24.

Conclusion

With the increasing popularity of social media, more and more people consume news from
social media instead of traditional news media. However, social media has also been used to
spread fake news, which has strong negative impacts on individual users and broader society.
In this article, we explored the fake news problem by reviewing existing literature in two
phases: characterization and detection. In the characterization phase, we introduced the basic
concepts and principles of fake news in both traditional media and social media. In the detection
phase, we reviewed existing fake news detection approaches from a data mining perspective,
including feature extraction and model construction. We also further discussed the datasets,
evaluation metrics, and promising future directions in fake news detection research and expand
the field to other applications.

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 Bibliography/References

[1] Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu, “Fake News Detection
on Social Media: A Data Mining Perspective” arXiv:1708.01967v3 [cs.SI], 3 Sep 2017
[2] Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu, “Fake News Detection
on Social Media: A Data Mining Perspective” arXiv:1708.01967v3 [cs.SI], 3 Sep 2017
[3] M. Granik and V. Mesyura, "Fake news detection using naive Bayes classifier," 2017
IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON),
Kiev, 2017, pp. 900-903.
[4] Fake news websites. (n.d.) Wikipedia. [Online]. Available:
https://en.wikipedia.org/wiki/Fake_news_website. Accessed Feb. 6, 2017
[5] Cade Metz. (2016, Dec. 16). The bittersweet sweepstakes to build an AI that destroys
fake news.
[6] Conroy, N., Rubin, V. and Chen, Y. (2015). “Automatic deception detection: Methods
for finding fake news” at Proceedings of the Association for Information Science and
Technology, 52(1), pp.1-4.
[7] Markines, B., Cattuto, C., & Menczer, F. (2009, April). “Social spam detection”. In
Proceedings of the 5th International Workshop on Adversarial Information Retrieval
on the

15
Web (pp. 41-48)
[8] Rada Mihalcea , Carlo Strapparava, The lie detector: explorations in the automatic
recognition of deceptive language, Proceedings of the ACL-IJCNLP
[9] Kushal Agarwalla, Shubham Nandan, Varun Anil Nair, D. Deva Hema, “Fake News
Detection using Machine Learning and Natural Language Processing,” International Journal of
Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-6, March
2019
[10] H. Gupta, M. S. Jamal, S. Madisetty and M. S. Desarkar, "A framework for real-time
spam detection in Twitter," 2018 10th International Conference on Communication
Systems & Networks (COMSNETS), Bengaluru, 2018, pp. 380-383 [11] M. L. Della
Vedova, E. Tacchini, S. Moret, G. Ballarin, M. DiPierro and L. de Alfaro, "Automatic
Online Fake News Detection Combining Content and Social Signals," 2018 22nd
Conference of Open Innovations Association (FRUCT), Jyvaskyla, 2018, pp. 272-279.
[12] C. Buntain and J. Golbeck, "Automatically Identifying Fake News in Popular Twitter
Threads," 2017 IEEE International Conference on Smart Cloud (SmartCloud), New York,
NY, 2017, pp. 208-215.
[13] S. B. Parikh and P. K. Atrey, "Media-Rich Fake News Detection: A Survey," 2018 IEEE
Conference on Multimedia Information Processing and Retrieval (MIPR), Miami, FL,
2018, pp. 436-441
[14] Scikit-Learn- Machine Learning In Python
[15] Dataset- Fake News detection William Yang Wang. " liar, liar pants on _re": A new
benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648, 2017.
[16] Shankar M. Patil, Dr. Praveen Kumar, “Data mining model for effective data analysis of
higher education students using MapReduce” IJERMT, April 2017 (Volume-6, Issue-4).
[17] Aayush Ranjan, “ Fake News Detection Using Machine Learning”, Department Of
Computer Science & Engineering Delhi Technological University, July 2018.
[18] Patil S.M., Malik A.K. (2019) Correlation Based Real-Time Data Analysis of Graduate
Students Behaviour. In: Santosh K., Hegadi R. (eds) Recent Trends in Image Processing
and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information
Science, vol 1037. Springer, Singapore.
[19] Badreesh Shetty, “Natural Language Processing (NLP) for machine learning” at
towardsdatascience, Medium.
[20] NLTK 3.5b1 documentation, Nltk generate n gram .

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[21] Ultimate guide to deal with Text Data (using Python) – for Data Scientists and Engineers
by Shubham Jain, February 27, 2018 .
[22] Understanding the random forest by Anirudh Palaparthi, Jan 28, at analytics vidya.
[23] Understanding the random forest by Anirudh Palaparthi, Jan 28, at analytics vidya.
[24]Shailesh-Dhama,“Detecting-Fake-News-with-Python”, Github, 2019 .
[25] Aayush Ranjan, “ Fake News Detection Using Machine Learning”, Department Of
Computer Science & Engineering Delhi Technological University, July 2018.
[26] What is a Confusion Matrix in Machine Learning by Jason Brownlee on November 18,
2016 in Code Algorithms From Scratch.

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