Classification of Hoax/Non-Hoax News Articles on Social Media using an Effective Deep Neural Network

Kaliyar, Rohit Kumar; Fitwe, Kedar; Rajarajeswari, P.; Goswami, Anurag

doi:10.1109/iccmc51019.2021.9418282

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…Social media are an information medium that spreads news faster [1]. However, not all information scattered in them is valid and factual [2]. News can come from all over the world through online social media.…”

Section: Introductionmentioning

confidence: 99%

Detecting Fake News on Social Media Combined with the CNN Methods

Fauzy

Setiawan

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Social media platforms are created to facilitate human social life as technology develops. Twitter is one of the most popular and frequently used social media for exchanging information. This social media platform disseminates real-time and complete information. Unfortunately, there are not a few tweets that contain false information or are often referred to as hoaxes. Those hoaxes that existed on Twitter are very troubling for society. Fake news or hoaxes can cause misunderstandings in receiving information. Therefore, this research aimed at developing a system that can detect hoaxes on Twitter to anticipate their spread, which can be detrimental to related parties. The system being developed uses a deep learning approach with the Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and Global Vectors (GloVe). The results of this study display the fake news detected by the system using the CNN method with baseline, BERT, and GloVe. The data have been adjusted to the keywords related to fake news and spread on online media, such as Hoax or Not from Detik.com, CekFakta from Kompas.com, etc. The results show the highest accuracy of 98.57% using CNN with a split ratio of 90:10, baseline unigram-bigram, BERT, and Top10 corpus tweet+IndoNews with an increase of 4.7%.

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Section: Introductionmentioning

confidence: 99%

Detecting Fake News on Social Media Combined with the CNN Methods

Fauzy

Setiawan

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Facts show that the rapid spread of hoaxes comes from reliable information, resulting in a very complex problem [37], [75], [76]. Many people are getting active access to COVID-19 and health-related information during this pandemic period, causing increased anxiety levels due to the incorrect information from social media [70], [71], [73]. This affects the government's ability to reduce illness and death from the disease due to people's wrong beliefs about the virus [14], [77].…”

mentioning

confidence: 99%

Hoax behavior tendencies among Indonesian students: An analysis during the COVID-19 pandemic

Afdal

Fikri

Neviyarni

et al. 2023

IJERE

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<span lang="EN-US">The objective of this study was to analyze the hoax behavior tendencies among Indonesian students during the pandemic, viewed from the aspects of analytical thinking, basic science knowledge, trust in news sources, satisfaction, and action. A total of 633 male and female students from Indonesian universities were sampled used a simple random sampling technique. Data were collected through the hoax tendency behavior scale (HTBS) on 30 questions using a Likert scale. Then, the data were analyzed descriptively to evaluate the hoax behavior tendencies in adolescents during the pandemic, which the results revealed to be in the medium category. Therefore, the analytical thinking skills of adolescents need to be improved, as 31.75% and 2.53% were in the low and very low categories. The study showed that adolescents with an average age of 20 years spreading hoaxes were motivated by basic knowledge of science, trust in news sources, satisfaction, and action. However, there was no significant influence of gender, parental income, and areas of residence, such as rural, urban, and suburban, on those that exhibit hoax behaviors.</span>

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Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study

Limam

Aqil

Yafooz

et al. 2021

Studies in Computational Intelligence

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This last decade, the amount of data exchanged on the Internet and more specifically on social media networks is growing exponentially. Fake News phenomenon has become a major problem threatening the credibility of these social networks. Machine Learning (ML) techniques represent a promising solution to deal with this issue. For that, several solutions and algorithms using Machine Learning have been proposed in literature in the recent time for detecting fake news generated by different digital media platforms. This chapter aims to conduct a systematic mapping study to analyze and synthesize studies concerning the utilization of machine learning techniques for detecting fake news. Therefore, a total number of 76 relevant papers published on this subject between 1 January 2010 and 30 June 2021 were carefully selected. The selected articles were classified and analyzed according to eight criteria: channel and year of publication, research type, study domain, study platform, study context, study category, feature, and machine learning techniques used to handle categorical data. The results showed that most of the selected papers use both features text/content and linguistic to design machine learning models. Furthermore, SVM technique, and Deep Neural Network (DNN) technique were the most binary classification algorithms used to combat fake news.

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Classification of Hoax/Non-Hoax News Articles on Social Media using an Effective Deep Neural Network

Cited by 9 publications

References 23 publications

Detecting Fake News on Social Media Combined with the CNN Methods

Detecting Fake News on Social Media Combined with the CNN Methods

Hoax behavior tendencies among Indonesian students: An analysis during the COVID-19 pandemic

Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study

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