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SOCIAL MEDIA AND MISLEADING

1

Mrs. P. SWETHA, 2

AKSHITA SANGANKAR,3

DHARAVATH VINOD

KUMAR,4

LAKSHANA.CH,5

PITTA SHIVANI

1

Assistant Professor, Dept.of CSE, Teegala Krishna Reddy Engineering College, Meerpet, Hyderabad,

2,3,4,5BTech Student, Dept.of CSE, Teegala Krishna Reddy Engineering College, Meerpet, Hyderabad

akshitasangamkar@gmail.com ,vinoddharavath09@gmail.com,chalvadilakshana@gmail.com

,shivanireddy821@gmail.com

ABSTRACT: In this paper, we present a resource allocation mechanism to incentivize

misinformation filtering among strategic social media platforms, and thus, to indirectly prevent

the spread of fake news. We consider the presence of a strategic government and private

knowledge of how misinformation affects the users of the social media platforms. As a result,

individuals have developed a tendency to form their opinions based on the believability of

presented information rather than its truthfulness. Our proposed mechanism strongly implements

all generalized Nash equilibria for efficient filtering of misleading information in the induced

game, with a balanced budget. We also show that for quasi-concave utilities, our mechanism

implements a Pareto efficient solution.

Keywords: Sociall media, misleading, customer opinions, Nash equilibria.

I. INTRODUCTION

For the last few years, political

commentators have been indicating

that we live in a post-truthera, wherein

the deluge of information available on

the internet has made it extremely

difficult to identify facts. As a result,

individuals have developed a tendency

to form their opinions based on the

believabilityof presented information

rather than its truthfulness. This

phenomenon is exacerbated by the

business practices of social media

platforms, which often seek to

maximize the engagementof their

users at all costs. In fact, the

algorithms developed by platforms for

this purpose often promote conspiracy

theories among their users.

The sensitivity of users of social

media platforms to conspiratorial ideas

makes them an ideal terrain to conduct

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political misinformation campaigns.

Such campaigns are especially

effective tools to disrupt democratic

institutions, because the functioning of

stable democracies relies on common

knowledgeabout the political actors

and the process, they can use to gain

public support. The trust held by the

citizens of a democracy on common

knowledge includes: (i) trust that all

political actors act in good faith when

contesting for power, (ii) trust that

elections lead to a free and fair transfer

of power between the political actors,

and (iii) trust that democratic

institutions ensure that elected

officials wield their power in the best

interest of the citizens. In contrast,

citizens of democracies often have a

contested knowledgeregarding who

should hold power and how they

should use it. The introduction of

alternative factscan reduce the trust on

common knowledge about democracy,

especially if they become accepted

beliefs among the citizens. Such

disruptions on the trust on common

knowledge can be found in the 2016

U.S. elections and Brexit Campaign in

2016, where the spread of

misinformation through social media

platforms resulted in a large number of

citizens mistrusting the results of

voting. To tackle this growing

phenomenon of misinformation, in this

paper, we consider a finite group of

social media platforms, whose users

represent the citizens in a democracy,

and a democratic government. Every

post in the platforms is association

with a parameter that captures its

informativeness, which can take

values between two extremes: (i)

completely factual and (ii) complete

misinformation. In our framework,

posts that exhibit misinformation can

lead to a decrease in trust on common

knowledge among the users. In

addition, social media platforms are

considered to have the technologies to

filter, or label, posts that intend to

sacrifice trust on common knowledge.

Thus, the government seeks to

incentivize the social media platforms

to use these technologies and filter any

misinformation included in the posts.

Motivated by capitalistic values, we

induce a misinformation

filteringgameto describe the

interactions between the social media

platforms and the government. In this

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game, each platform acts as strategic

player seeking to maximize their

advertisement revenue from the

engagement of their users. User

engagement is a metric that can be

used to quantify the interaction of

users with a platform, and

subsequently, how much time they

spend on the platform. Recent efforts

reported in the literature on

misinformation in social media

platforms have indicated that

increasing filtering of misinformation

leads to decreasing of user

engagement. There are many possible

reasons for this phenomenon. First,

filtering reduces the total number of

posts propagating across the social

network. Second, the users whose

opinions are filtered may perceive this

action as dictatorial censorship, and as

a result, they may choose to express

their opinions in other platforms.

Finally, misinformation tends to elicit

stronger reactions, e.g., surprise, joy,

sadness, as compared to factual posts,

which may increase user engagement.

Thus, each platform is reluctant to

filter misinformation.

In our framework, we consider that the

government is also a strategic player,

whose utility increases as the trust of

the users of social media platforms on

common knowledge increases.

Consequently, increasing filtering of

misinformation by the social media

platforms increases the utility of the

government. Thus, the government is

willing to make an investment to

incentivize the social media platforms

to filter misinformation. In our

approach, we use mechanism design to

distribute this investment among the

platforms optimally, and in return,

implement an optimal level of

filtering.

Mechanism design was developed for

the implementation of system-wide

optimal solutions to problems

involving multiple rational players

with conflicting interests, each with

private information about preferences.

Note that this approach is different

from traditional approaches to

decentralized control with private

information because the players are

not a part of the same time, but in fact,

have private and competitive utilities.

The fact that Mechanism design

optimizes the behavior of competing

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