Team sigmoid at CheckThat!2021 Task 3a: Multiclass fake news detection with Machine Learning

Abdullah Al Mamun Sardar; Shahalu Akter Salma; Md Sanzidul Islam; Md Arid Hasan; Touhid Bhuiyan

Team sigmoid at CheckThat!2021 Task 3a: Multiclass fake news detection with Machine Learning

Abdullah Al Mamun Sardar, Shahalu Akter Salma, Md Sanzidul Islam, Md Arid Hasan, Touhid Bhuiyan

Research output: Contribution to conference › Conference paper

Abstract

Fake news is affecting our lives since the internet has become popular. Particularly, in this era of social media it is very easy to spread and be affected by fake news. In this work we have developed machine learning models which can classify a news claim into four classes. This work has been done under the competition of CheckThat!2021 task-3a. We have conducted our experiment on Check that lab's dataset. Our work has been done only on linguistic features. We have experimented both with traditional Machine Learning algorithms and Deep Learning algorithms. LSTM outperformed other traditional machine learning algorithms and with Adam optimizer LSTM gave a f1-macro score of 26.07%.

Original language	English
Pages	612-618
Number of pages	7
Publication status	Published - 1 Jan 2021
Externally published	Yes
Event	CEUR Workshop Proceedings - Duration: 1 Jan 2021 → …

Conference

Conference	CEUR Workshop Proceedings
Period	1/01/21 → …

Keywords

Fake news detection
LSTM
Multinomial Naïve bayes
Support vector machine

Cite this

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title = "Team sigmoid at CheckThat!2021 Task 3a: Multiclass fake news detection with Machine Learning",

abstract = "Fake news is affecting our lives since the internet has become popular. Particularly, in this era of social media it is very easy to spread and be affected by fake news. In this work we have developed machine learning models which can classify a news claim into four classes. This work has been done under the competition of CheckThat!2021 task-3a. We have conducted our experiment on Check that lab's dataset. Our work has been done only on linguistic features. We have experimented both with traditional Machine Learning algorithms and Deep Learning algorithms. LSTM outperformed other traditional machine learning algorithms and with Adam optimizer LSTM gave a f1-macro score of 26.07%.",

keywords = "Fake news detection, LSTM, Multinomial Na{\"i}ve bayes, Support vector machine",

author = "{Al Mamun Sardar}, Abdullah and Salma, {Shahalu Akter} and Islam, {Md Sanzidul} and Hasan, {Md Arid} and Touhid Bhuiyan",

year = "2021",

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note = "CEUR Workshop Proceedings ; Conference date: 01-01-2021",

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AU - Al Mamun Sardar, Abdullah

AU - Salma, Shahalu Akter

AU - Islam, Md Sanzidul

AU - Hasan, Md Arid

AU - Bhuiyan, Touhid

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Fake news is affecting our lives since the internet has become popular. Particularly, in this era of social media it is very easy to spread and be affected by fake news. In this work we have developed machine learning models which can classify a news claim into four classes. This work has been done under the competition of CheckThat!2021 task-3a. We have conducted our experiment on Check that lab's dataset. Our work has been done only on linguistic features. We have experimented both with traditional Machine Learning algorithms and Deep Learning algorithms. LSTM outperformed other traditional machine learning algorithms and with Adam optimizer LSTM gave a f1-macro score of 26.07%.

AB - Fake news is affecting our lives since the internet has become popular. Particularly, in this era of social media it is very easy to spread and be affected by fake news. In this work we have developed machine learning models which can classify a news claim into four classes. This work has been done under the competition of CheckThat!2021 task-3a. We have conducted our experiment on Check that lab's dataset. Our work has been done only on linguistic features. We have experimented both with traditional Machine Learning algorithms and Deep Learning algorithms. LSTM outperformed other traditional machine learning algorithms and with Adam optimizer LSTM gave a f1-macro score of 26.07%.

KW - Fake news detection

KW - LSTM

KW - Multinomial Naïve bayes

KW - Support vector machine

M3 - Conference paper

SP - 612

EP - 618

T2 - CEUR Workshop Proceedings

Y2 - 1 January 2021

ER -

Team sigmoid at CheckThat!2021 Task 3a: Multiclass fake news detection with Machine Learning

Abstract

Conference

Keywords

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Cite this