PERFORMANCE EVALUATION OF FAKE NEWS DETECTION USING MACHINE LEARNING ALGORITHM

CHAPTER ONE

INTRODUCTION

1.2 Background to the Study

Fake news detection topic has gained a great deal of interest from researchers around the world. When some event has occurred, many people discuss it on the web through the social networking. They search or retrieve and discuss the news events as the routine of daily life. Some type of news such as various bad events from natural phenomenal or climate is unpredictable. When the unexpected events happen there are also fake news that are broadcasted that creates confusion due to the nature of the events. Hunt Allcott and Matthew Gentzkow (2017)

Very few people knows the real fact of the event while the most people believe the forwarded news from their credible friends or relatives. These are difficult to detect whether to believe or not when they receive the news information. So, there is a need of an automated system to analyze truthfulness of the new.

Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of “fake news”, i.e., low quality news with intentionally false information. Meital Balmas(2014).

The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users’ social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Alessandro Bessi and Emilio Ferrara(2016).

Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

• Statement of the Problem

Misinformation, otherwise (now popularly referred to as) “fake news” has become a ready tool for the spread of hate speech, thus deepening ethnic and religious differences in a multi-ethnic and multi-religious country like Nigeria. Although there have been studies carried out on the effect of fake news on the Nigerian political landscape. However, the Performance evaluation of fake news detection using machine learning algorithm has not been given required attention. It is therefore imperative to carry out a study on the ways to manage fake news peddled on social media with the aid of artificial intelligence. If not adequately dealt with, the menace of fake news is capable of aggravating the rising insecurity in different parts of Nigeria.

In view of the afore-stated, it is pertinent to note that failure to stem the tide of fake news in Nigeria may potentially create many more conflicts around the country. At the end of this study, it is hoped that implementable recommendations will be proffered to serve as a basis for further studies into curbing the menace of fake news in Nigeria.

• Aims and Objectives

The main objective of this study is on the Performance evaluation of fake news detection using machine learning algorithm.

The specific objectives of the are highlighted below:

1. To come up with a solution that can be utilized by users to detect and filter out the sites containing false and misleading information.
2. To find the patterns that correlate with a piece of news which are potentially fake
3. To examine how machine learning algorithms can be used for identification of fake new
   • Research Question
4. What are the solution that can be utilized by users to detect and filter out the sites containing false and misleading information?
5. How do we find the patterns that correlate with a piece of news which are potentially fake?
6. How can machine learning algorithms be used for identification of fake news?

1.5 Scope of the Study

The central ideas of  this research is on Performance evaluation of fake news detection using machine learning algorithms. The study chooses online social media such as facebook and twitter  among the social media platform because of there sensitiveness in tackling the issues of fake news.

1.6 Significance of the Study

In today’s online social networks there have been a lot of problems like fake news, online impersonation, etc. Till date, no one has come up with a feasible solution to these problems. In this project we intend to give a framework with which the automatic detection of fake news can be done so that the social life of people become secured and by using this automatic detection technique we can make it easier for the sites to manage the huge number of news, which cant be done manually.

1.7 Limitations of the Study

Financial Constraints: The researcher was with limited funds, she cannot visit all the areas to get responses from respondents but she was able to get good information concerning the research topic.

Time Constraints: The researcher was involved in other departmental activities like seminars, attendance of lectures et.c which limited her time for the research but the researcher was able to meet up with the time assigned for the completion of the research work

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