Covid-19 Fake News Detection on Twitter Based on Author Credibility
Using Information Gain and KNN Methods
Dublin Core
Title
Covid-19 Fake News Detection on Twitter Based on Author Credibility
Using Information Gain and KNN Methods
Using Information Gain and KNN Methods
Subject
twitter; fake news, COVID-19; credibility; KNN; information gain
Description
Twitter is one of the social media that is used as a tool to share various kinds of information about various kinds of things
that are of concern to social media users. One of the information shared is information about COVID-19, which is known
that the COVID-19 pandemic is currently spreading throughout the world at a very alarming rate. COVID-19 is an infectious
disease caused by SARS-COV-2. The World Health Organization (WHO) claims that the spread of COVID-19 is supported
by the spread of false/fake news. So to find out the truth of the news, a COVID-19 fake news detector is needed so that users
don't fall for the hoaxes circulating. This study aims to classify COVID-19 news on Twitter based on author credibility.
Credibility in question is a person's perception of the validity of information and is a multidimensional concept that is used
as a means of receiving information to assess the source of communication. The method used in this research is Information
Gain and KNN. KNN (K-Nearest Neighbor) is a supervised learning algorithm that works by classifying a set of data based
on classified training data. Information Gain is used to ranking the most influential attributes, and KNN is used to classify
data based on learning data taken from the nearest neighbors. The research consists of 6 main stages, namely data collection
(crawling data), data preprocessing, feature extraction, feature selection, data split into training data and testing data, KNN
stage, and data evaluation stage. The research carried out succeeded in obtaining an accuracy value of 91%, a correlation
value between credibility and hoax of 0.115, and a p-value <0.005.
that are of concern to social media users. One of the information shared is information about COVID-19, which is known
that the COVID-19 pandemic is currently spreading throughout the world at a very alarming rate. COVID-19 is an infectious
disease caused by SARS-COV-2. The World Health Organization (WHO) claims that the spread of COVID-19 is supported
by the spread of false/fake news. So to find out the truth of the news, a COVID-19 fake news detector is needed so that users
don't fall for the hoaxes circulating. This study aims to classify COVID-19 news on Twitter based on author credibility.
Credibility in question is a person's perception of the validity of information and is a multidimensional concept that is used
as a means of receiving information to assess the source of communication. The method used in this research is Information
Gain and KNN. KNN (K-Nearest Neighbor) is a supervised learning algorithm that works by classifying a set of data based
on classified training data. Information Gain is used to ranking the most influential attributes, and KNN is used to classify
data based on learning data taken from the nearest neighbors. The research consists of 6 main stages, namely data collection
(crawling data), data preprocessing, feature extraction, feature selection, data split into training data and testing data, KNN
stage, and data evaluation stage. The research carried out succeeded in obtaining an accuracy value of 91%, a correlation
value between credibility and hoax of 0.115, and a p-value <0.005.
Creator
Nanda Ihwani Saputri1
, Yuliant Sibaroni2
, Sri Suryani Prasetiyowati3
, Yuliant Sibaroni2
, Sri Suryani Prasetiyowati3
Publisher
Universitas Telkom
Date
06-02-2023
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Nanda Ihwani Saputri1
, Yuliant Sibaroni2
, Sri Suryani Prasetiyowati3, “Covid-19 Fake News Detection on Twitter Based on Author Credibility
Using Information Gain and KNN Methods,” Repository Horizon University Indonesia, accessed June 29, 2025, https://repository.horizon.ac.id/items/show/9356.
Using Information Gain and KNN Methods,” Repository Horizon University Indonesia, accessed June 29, 2025, https://repository.horizon.ac.id/items/show/9356.