Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm

Hyunseo Lee; Ian Paik Choe; Jioh In; Han Sol Kim

doi:doi:10.11648/j.ajdmkd.20200502.11

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Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm

Hyunseo Lee, Ian Paik Choe, Jioh In, Han Sol Kim

Published in American Journal of Data Mining and Knowledge Discovery (Volume 5, Issue 2)

Received: 6 July 2020 Accepted: 21 July 2020 Published: 19 September 2020

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Abstract

With recent advancements in social media and technology as a whole, online news sources have increased. Therefore there has been a higher demand of people wanting a convenient way to find recent, relevant and updated online news articles and posts from social media platforms. In the current status quo, many people feel comfortable with their main source of news being social media articles. Unfortunately, receiving news via social media platforms and unverified online sites has aroused many problems, one of which being fake news (news which contain incorrect or biased facts and statements). Many individuals all around the world are vulnerable and subject to fake news and becoming victims of propaganda and/or being misinformed. To solve this world-wide complication, we used word preprocessing skills to digest the content of articles, and used several mathematical vectors to pinpoint the legitimacy of a news article. To establish an accurate system, words used in examples of fake news and real news were collected using Python. Verifying fake and real news is an important process that all news should go through as it can result in immense consequences. Data on real news and fake news were collected from Kaggle. We had the conclusion that the trained machine learning algorithms showed high accuracy of distinguishing which indicates our research was successful.

Published in	American Journal of Data Mining and Knowledge Discovery (Volume 5, Issue 2)
DOI	10.11648/j.ajdmkd.20200502.11
Page(s)	20-26
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Fake News, Preprocessing Data, Data Analysis, Text Mining, Machine Learning

References

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[7]	How Social Media Has Changed How We Consume News. (n.d.). Retrieved June 20, 2020, from https://www.forbes.com/sites/nicolemartin1/2018/11/30/how-social-media-has-changed-how-we-consume-news/#3eea114f3c3c.
[8]	Hopkins, J. (n.d.). Research Guides: Fake News: Develop Your Fact-Checking Skills: What Kinds of Fake News Exist?
[9]	Types of ’Fake News’ and Why They Matter \| Ogilvy. (n.d.). Retrieved June 20, 2020, from https://www.ogilvy.com/ideas/5-types-fake-news-why-they-matter.
[10]	Engle, M. (n.d.). LibGuides: Fake News, Propaganda, and Bad information: Learning to Critically Evaluate Media Sources.: What Is Fake News?
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APA Style

Hyunseo Lee, Ian Paik Choe, Jioh In, Han Sol Kim. (2020). Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm. American Journal of Data Mining and Knowledge Discovery, 5(2), 20-26. https://doi.org/10.11648/j.ajdmkd.20200502.11

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ACS Style

Hyunseo Lee; Ian Paik Choe; Jioh In; Han Sol Kim. Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm. Am. J. Data Min. Knowl. Discov. 2020, 5(2), 20-26. doi: 10.11648/j.ajdmkd.20200502.11

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AMA Style

Hyunseo Lee, Ian Paik Choe, Jioh In, Han Sol Kim. Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm. Am J Data Min Knowl Discov. 2020;5(2):20-26. doi: 10.11648/j.ajdmkd.20200502.11

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@article{10.11648/j.ajdmkd.20200502.11,
  author = {Hyunseo Lee and Ian Paik Choe and Jioh In and Han Sol Kim},
  title = {Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {5},
  number = {2},
  pages = {20-26},
  doi = {10.11648/j.ajdmkd.20200502.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20200502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20200502.11},
  abstract = {With recent advancements in social media and technology as a whole, online news sources have increased. Therefore there has been a higher demand of people wanting a convenient way to find recent, relevant and updated online news articles and posts from social media platforms. In the current status quo, many people feel comfortable with their main source of news being social media articles. Unfortunately, receiving news via social media platforms and unverified online sites has aroused many problems, one of which being fake news (news which contain incorrect or biased facts and statements). Many individuals all around the world are vulnerable and subject to fake news and becoming victims of propaganda and/or being misinformed. To solve this world-wide complication, we used word preprocessing skills to digest the content of articles, and used several mathematical vectors to pinpoint the legitimacy of a news article. To establish an accurate system, words used in examples of fake news and real news were collected using Python. Verifying fake and real news is an important process that all news should go through as it can result in immense consequences. Data on real news and fake news were collected from Kaggle. We had the conclusion that the trained machine learning algorithms showed high accuracy of distinguishing which indicates our research was successful.},
 year = {2020}
}

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TY - JOUR
T1 - Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm
AU - Hyunseo Lee
AU - Ian Paik Choe
AU - Jioh In
AU - Han Sol Kim
Y1 - 2020/09/19
PY - 2020
N1 - https://doi.org/10.11648/j.ajdmkd.20200502.11
DO - 10.11648/j.ajdmkd.20200502.11
T2 - American Journal of Data Mining and Knowledge Discovery
JF - American Journal of Data Mining and Knowledge Discovery
JO - American Journal of Data Mining and Knowledge Discovery
SP - 20
EP - 26
PB - Science Publishing Group
SN - 2578-7837
UR - https://doi.org/10.11648/j.ajdmkd.20200502.11
AB - With recent advancements in social media and technology as a whole, online news sources have increased. Therefore there has been a higher demand of people wanting a convenient way to find recent, relevant and updated online news articles and posts from social media platforms. In the current status quo, many people feel comfortable with their main source of news being social media articles. Unfortunately, receiving news via social media platforms and unverified online sites has aroused many problems, one of which being fake news (news which contain incorrect or biased facts and statements). Many individuals all around the world are vulnerable and subject to fake news and becoming victims of propaganda and/or being misinformed. To solve this world-wide complication, we used word preprocessing skills to digest the content of articles, and used several mathematical vectors to pinpoint the legitimacy of a news article. To establish an accurate system, words used in examples of fake news and real news were collected using Python. Verifying fake and real news is an important process that all news should go through as it can result in immense consequences. Data on real news and fake news were collected from Kaggle. We had the conclusion that the trained machine learning algorithms showed high accuracy of distinguishing which indicates our research was successful.
VL - 5
IS - 2
ER -

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Author Information

Hyunseo Lee

Seoul International School, Gyeonggi-do, South Korea
Ian Paik Choe

Seoul Foreign School, Seoul, South Korea
Jioh In

Princeton International School of Mathematics and Science, New Jersey, the United States
Han Sol Kim

Fayston Preparatory School, Gyeonggi-do, South Korea

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Plain Text BibTeX RIS

APA Style

Hyunseo Lee, Ian Paik Choe, Jioh In, Han Sol Kim. (2020). Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm. American Journal of Data Mining and Knowledge Discovery, 5(2), 20-26. https://doi.org/10.11648/j.ajdmkd.20200502.11

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ACS Style

Hyunseo Lee; Ian Paik Choe; Jioh In; Han Sol Kim. Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm. Am. J. Data Min. Knowl. Discov. 2020, 5(2), 20-26. doi: 10.11648/j.ajdmkd.20200502.11

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AMA Style

Hyunseo Lee, Ian Paik Choe, Jioh In, Han Sol Kim. Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm. Am J Data Min Knowl Discov. 2020;5(2):20-26. doi: 10.11648/j.ajdmkd.20200502.11

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@article{10.11648/j.ajdmkd.20200502.11,
  author = {Hyunseo Lee and Ian Paik Choe and Jioh In and Han Sol Kim},
  title = {Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {5},
  number = {2},
  pages = {20-26},
  doi = {10.11648/j.ajdmkd.20200502.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20200502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20200502.11},
  abstract = {With recent advancements in social media and technology as a whole, online news sources have increased. Therefore there has been a higher demand of people wanting a convenient way to find recent, relevant and updated online news articles and posts from social media platforms. In the current status quo, many people feel comfortable with their main source of news being social media articles. Unfortunately, receiving news via social media platforms and unverified online sites has aroused many problems, one of which being fake news (news which contain incorrect or biased facts and statements). Many individuals all around the world are vulnerable and subject to fake news and becoming victims of propaganda and/or being misinformed. To solve this world-wide complication, we used word preprocessing skills to digest the content of articles, and used several mathematical vectors to pinpoint the legitimacy of a news article. To establish an accurate system, words used in examples of fake news and real news were collected using Python. Verifying fake and real news is an important process that all news should go through as it can result in immense consequences. Data on real news and fake news were collected from Kaggle. We had the conclusion that the trained machine learning algorithms showed high accuracy of distinguishing which indicates our research was successful.},
 year = {2020}
}

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