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1. Base paper Title: Mitigating Influence of Disinformation Propagation Using Uncertainty-
Based Opinion Interactions
Modified Title: Reducing the Impact of Misinformation Spread Through Opinion Interactions
Based on Uncertainty
Abstract
For decades, the spread of disinformation in online social networks (OSNs) has been a
serious social issue. Disinformation via social media can easily mislead people’s beliefs toward
or against an event that may mislead their behaviors based on the misbeliefs. The game theory
approaches have been proposed under dynamic settings to limit the adverse influences of
disinformation. It is a challenge to expand the users’ game strategies from the spreading
decisions to the possible opinion updating choices. This work proposes a game-theoretic
opinion framework that can formulate dynamic opinions by a belief model called Subjective
Logic (SL) and provide opinion updates on five types of users’ interactions on OSN platforms.
The opinions are updated based on user choices and user types through the game interactions
among legitimate users, attackers, and a defender in an OSN. Via the extensive simulation
experiments, the effectiveness of the opinion models of five decision-makers (DMs) is
analyzed in terms of users believing or disbelieving disinformation in an epidemic model with
parameter optimization. Our results show that while homophily-based DMs (H-DMs)
introduce the highest opinion polarization, uncertainty-based DMs (U-DMs) can effectively
filter untrustworthy users propagating disinformation.
Existing System
THANKS to the popularity of online social networks (OSNs) and their highly advanced
features, communications via social media or OSNs become part of our daily life. In various
OSN platforms, people exchange their opinions without high confidence or share them without
going through any verification process. It is well known that disseminating false information,
including unverified rumors, misinformation, or disinformation, can easily destroy individuals’
reputations or lives. In this work, we use the terms false information or disinformation
interchangeably where it refers to false information propagated with malicious intent [24]. As
a result, manipulating public opinions toward sensitive issues can easily happen when
disinformation propagates extremely fast. Further, disseminating disinformation can be highly

2. detrimental in affecting critical decision-making processes in our real life at the levels of
individuals, communities, and global society [5], [16], such as in elections, pandemics, health,
or education. In an OSN, a person can take advantage of different activities to connect to other users
and share opinions. The level of a person’s acceptance of a given opinion has been estimated based
on various aspects, such as personality traits (e.g., agreeableness, open-mindedness, and
stubbornness), a tendency to relying on others’ opinions (e.g., herding), homophily (e.g., like-
mindedness), competence (e.g., domain expertise), or confidence (e.g., certainty) [7], [8], [20]. There
has been a rich volume of approaches modeling and simulating the behaviors of OSN users in updating
their opinions and propagating (false) information [41], [42], [43]. Based on OSN user’s bounded
rationality caused by inherent cognitive bias or incapability of humans [1], [17], [32], [37], [40], most
existing game theory diffusion models are grounded by users’ decisions of spreading rumors or not.
To limit disinformation cascades, the incentives and punishments of spreading unverified rumors were
accessed by environment factors, network topology, neighbors’ strategy preferences, and individual
factors.
Drawback in Existing System
 Polarization Reinforcement:
If uncertainty-based opinion interactions are not implemented carefully, they could
inadvertently reinforce existing polarization. People may become more entrenched in
their beliefs if they perceive uncertainty as a weakness rather than an opportunity for
open-minded discussion.
 Algorithmic Bias:
If the underlying algorithms are not designed with fairness and inclusivity in mind,
there is a risk of perpetuating biases. Certain groups may be disproportionately
affected by the uncertainty-based interactions, leading to unintended consequences.
 Effectiveness Challenges:
Assessing and managing uncertainty in opinions is a complex task. It requires
sophisticated algorithms and constant updates to keep up with evolving
disinformation tactics. Achieving a high level of effectiveness in mitigating
disinformation through uncertainty-based interactions may be challenging.
 Technical Challenges:
Developing and maintaining a system that accurately identifies and manages
uncertainty in opinions is technically challenging. It requires continuous improvement
and adaptation to new disinformation tactics.

3. Proposed System
 Natural Language Processing (NLP) Algorithms:
Implement advanced NLP algorithms to analyze text and identify statements with a
degree of uncertainty. These algorithms should be capable of understanding contextual
cues, linguistic nuances, and the overall sentiment of the content.
 Continuous Monitoring and Evaluation:
Establish mechanisms for continuous monitoring and evaluation of the system's
effectiveness. Regularly assess the impact of uncertainty-based interactions on
disinformation trends and user behavior, and make adjustments as needed.
 User Education and Awareness Campaigns:
Launch educational campaigns to raise awareness among users about the presence of
disinformation and the importance of critical thinking. Empower users to be active
participants in verifying information and understanding the role of uncertainty in the
online space.
 Collaboration with Fact-Checkers:
Collaborate with reputable fact-checking organizations to cross-verify information
and enhance the accuracy of the system. Integrating fact-checking data into the
algorithm contributes to a more robust and reliable mitigation strategy.
Algorithm
 Contextual Analysis:
Consider the context in which information is shared. Algorithms should be able to
recognize nuances and contextual cues to better understand the meaning and potential
uncertainty associated with statements.
 Explainability and Transparency:
Design algorithms that are transparent and explainable. Users should have a clear
understanding of why certain information is flagged as uncertain, fostering trust in the
system.

4.  Collaboration with Fact-Checkers:
Collaborate with fact-checking organizations to enhance the accuracy of the
algorithm. Integrating verified fact-checking information can improve the system's
ability to identify and label misinformation.
Advantages
 Promotes Critical Thinking:
Introducing uncertainty encourages users to approach information with a more
critical mindset. It prompts individuals to question and evaluate the reliability of the
information they encounter, fostering a culture of critical thinking.
 Raises Awareness of Disinformation:
By flagging uncertain statements, the system can effectively highlight potential
areas of disinformation. This raises awareness among users about the prevalence of
misinformation and prompts them to be more cautious about the information they
consume and share.
 Encourages Source Verification:
Users are more likely to verify the reliability of information sources when
uncertainty is introduced. This can lead to increased scrutiny of the credibility of news
outlets and websites, discouraging the dissemination of false or misleading
information.
 User Empowerment:
Providing users with information about uncertainty empowers them to make more
informed decisions. Users become active participants in verifying information,
contributing to a collective effort to combat disinformation.
Software Specification
 Processor : I3 core processor
 Ram : 4 GB
 Hard disk : 500 GB
Software Specification
 Operating System : Windows 10 /11
 Frond End : Python
 Back End : Mysql Server
 IDE Tools : Pycharm