Harmful Content on Social Media Detection Using by NLP

Iqra Naz; Rehhmat Illahi

doi:doi:10.11648/j.advances.20230402.13

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Harmful Content on Social Media Detection Using by NLP

Iqra Naz, Rehhmat Illahi

Published in Advances (Volume 4, Issue 2)

Received: 30 April 2023 Accepted: 12 June 2023 Published: 13 July 2023

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Abstract

Twitter, Facebook and Instagram are the popular social media platforms that allow people to access and connect to a world by a social network to express share and publish information. While online connection via media platforms is immensely desirable and come an unavoidable fact of daily life, the underbelly of social networks may be seen in the form of harmful/objectionable material. Fake news, rumors, hate speech, hostility, and bullying are examples of documented harmful material that are of major concern to society. Such damaging content hurts a negative impact on one's mental health and leads to financial losses that are rarely recoverable. Screening and filtering of such information is thus an urgent requirement. In this paper, we summarize some popular SM like Facebook WHATSAPP, LinkedIn etc. We use some notation like UGC, ML, and AI etc. In this review paper, focuses on methods for detecting harmful parts through natural language processing. The next phase looks at how to moderate this material.

Published in	Advances (Volume 4, Issue 2)
DOI	10.11648/j.advances.20230402.13
Page(s)	49-59
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), 2023. Published by Science Publishing Group

Keywords

Social Media (SM) Platforms, Detection and Moderation, Natural Language Processing (NLP), Artificial Intelligence (AI). Hate Speech Detection

References

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APA Style

Iqra Naz, Rehhmat Illahi. (2023). Harmful Content on Social Media Detection Using by NLP. Advances, 4(2), 49-59. https://doi.org/10.11648/j.advances.20230402.13

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ACS Style

Iqra Naz; Rehhmat Illahi. Harmful Content on Social Media Detection Using by NLP. Advances. 2023, 4(2), 49-59. doi: 10.11648/j.advances.20230402.13

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AMA Style

Iqra Naz, Rehhmat Illahi. Harmful Content on Social Media Detection Using by NLP. Advances. 2023;4(2):49-59. doi: 10.11648/j.advances.20230402.13

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@article{10.11648/j.advances.20230402.13,
  author = {Iqra Naz and Rehhmat Illahi},
  title = {Harmful Content on Social Media Detection Using by NLP},
  journal = {Advances},
  volume = {4},
  number = {2},
  pages = {49-59},
  doi = {10.11648/j.advances.20230402.13},
  url = {https://doi.org/10.11648/j.advances.20230402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.advances.20230402.13},
  abstract = {Twitter, Facebook and Instagram are the popular social media platforms that allow people to access and connect to a world by a social network to express share and publish information. While online connection via media platforms is immensely desirable and come an unavoidable fact of daily life, the underbelly of social networks may be seen in the form of harmful/objectionable material. Fake news, rumors, hate speech, hostility, and bullying are examples of documented harmful material that are of major concern to society. Such damaging content hurts a negative impact on one's mental health and leads to financial losses that are rarely recoverable. Screening and filtering of such information is thus an urgent requirement. In this paper, we summarize some popular SM like Facebook WHATSAPP, LinkedIn etc. We use some notation like UGC, ML, and AI etc. In this review paper, focuses on methods for detecting harmful parts through natural language processing. The next phase looks at how to moderate this material.},
 year = {2023}
}

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TY  - JOUR
T1  - Harmful Content on Social Media Detection Using by NLP
AU  - Iqra Naz
AU  - Rehhmat Illahi
Y1  - 2023/07/13
PY  - 2023
N1  - https://doi.org/10.11648/j.advances.20230402.13
DO  - 10.11648/j.advances.20230402.13
T2  - Advances
JF  - Advances
JO  - Advances
SP  - 49
EP  - 59
PB  - Science Publishing Group
SN  - 2994-7200
UR  - https://doi.org/10.11648/j.advances.20230402.13
AB  - Twitter, Facebook and Instagram are the popular social media platforms that allow people to access and connect to a world by a social network to express share and publish information. While online connection via media platforms is immensely desirable and come an unavoidable fact of daily life, the underbelly of social networks may be seen in the form of harmful/objectionable material. Fake news, rumors, hate speech, hostility, and bullying are examples of documented harmful material that are of major concern to society. Such damaging content hurts a negative impact on one's mental health and leads to financial losses that are rarely recoverable. Screening and filtering of such information is thus an urgent requirement. In this paper, we summarize some popular SM like Facebook WHATSAPP, LinkedIn etc. We use some notation like UGC, ML, and AI etc. In this review paper, focuses on methods for detecting harmful parts through natural language processing. The next phase looks at how to moderate this material.
VL  - 4
IS  - 2
ER  -

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Author Information

Iqra Naz

Department of Computer Science and Information Technology, Ghazi University, Dera Ghazi Khan, Pakistan
Rehhmat Illahi

Department of Computer Science and Information Technology, Ghazi University, Dera Ghazi Khan, Pakistan

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APA Style

Iqra Naz, Rehhmat Illahi. (2023). Harmful Content on Social Media Detection Using by NLP. Advances, 4(2), 49-59. https://doi.org/10.11648/j.advances.20230402.13

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ACS Style

Iqra Naz; Rehhmat Illahi. Harmful Content on Social Media Detection Using by NLP. Advances. 2023, 4(2), 49-59. doi: 10.11648/j.advances.20230402.13

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AMA Style

Iqra Naz, Rehhmat Illahi. Harmful Content on Social Media Detection Using by NLP. Advances. 2023;4(2):49-59. doi: 10.11648/j.advances.20230402.13

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@article{10.11648/j.advances.20230402.13,
  author = {Iqra Naz and Rehhmat Illahi},
  title = {Harmful Content on Social Media Detection Using by NLP},
  journal = {Advances},
  volume = {4},
  number = {2},
  pages = {49-59},
  doi = {10.11648/j.advances.20230402.13},
  url = {https://doi.org/10.11648/j.advances.20230402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.advances.20230402.13},
  abstract = {Twitter, Facebook and Instagram are the popular social media platforms that allow people to access and connect to a world by a social network to express share and publish information. While online connection via media platforms is immensely desirable and come an unavoidable fact of daily life, the underbelly of social networks may be seen in the form of harmful/objectionable material. Fake news, rumors, hate speech, hostility, and bullying are examples of documented harmful material that are of major concern to society. Such damaging content hurts a negative impact on one's mental health and leads to financial losses that are rarely recoverable. Screening and filtering of such information is thus an urgent requirement. In this paper, we summarize some popular SM like Facebook WHATSAPP, LinkedIn etc. We use some notation like UGC, ML, and AI etc. In this review paper, focuses on methods for detecting harmful parts through natural language processing. The next phase looks at how to moderate this material.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Harmful Content on Social Media Detection Using by NLP
AU  - Iqra Naz
AU  - Rehhmat Illahi
Y1  - 2023/07/13
PY  - 2023
N1  - https://doi.org/10.11648/j.advances.20230402.13
DO  - 10.11648/j.advances.20230402.13
T2  - Advances
JF  - Advances
JO  - Advances
SP  - 49
EP  - 59
PB  - Science Publishing Group
SN  - 2994-7200
UR  - https://doi.org/10.11648/j.advances.20230402.13
AB  - Twitter, Facebook and Instagram are the popular social media platforms that allow people to access and connect to a world by a social network to express share and publish information. While online connection via media platforms is immensely desirable and come an unavoidable fact of daily life, the underbelly of social networks may be seen in the form of harmful/objectionable material. Fake news, rumors, hate speech, hostility, and bullying are examples of documented harmful material that are of major concern to society. Such damaging content hurts a negative impact on one's mental health and leads to financial losses that are rarely recoverable. Screening and filtering of such information is thus an urgent requirement. In this paper, we summarize some popular SM like Facebook WHATSAPP, LinkedIn etc. We use some notation like UGC, ML, and AI etc. In this review paper, focuses on methods for detecting harmful parts through natural language processing. The next phase looks at how to moderate this material.
VL  - 4
IS  - 2
ER  -

Copy | Download