Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)

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Detection of offending text for cryptic metaphors and sensitive references

Authors
Guanghui Chang1, Ronghui Zhang1, *, Jiahui Luo1
1School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
*Corresponding author. Email: S211201035@stu.cqupt.edu.cn
Corresponding Author
Ronghui Zhang
Available Online 31 August 2024.
DOI
10.2991/978-94-6463-490-7_59How to use a DOI?
Keywords
Offending Text Detection; NLP; Neural Networks
Abstract

Text data, as the main carrier of information dissemination, is filled with some offending and harmful content. Due to the development of time and culture, the offending content tends to use obscure language forms when expressing. In addition the compliance information that references sensitive keywords also greatly increases the detection complexity. This makes traditional text detection methods face great challenges. To solve the above problems, we constructed a detection dataset containing three offending categories. A detection method based on Natural Language Processing (NLP) technology and two detection strategies are also designed, and trained and compared on various types of advanced neural network models. The experimental results show that the obscure features and deep semantics can be obtained through learning, and also prove the effectiveness of our method.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 August 2024
ISBN
978-94-6463-490-7
ISSN
2589-4919
DOI
10.2991/978-94-6463-490-7_59How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Guanghui Chang
AU  - Ronghui Zhang
AU  - Jiahui Luo
PY  - 2024
DA  - 2024/08/31
TI  - Detection of offending text for cryptic metaphors and sensitive references
BT  - Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
PB  - Atlantis Press
SP  - 554
EP  - 562
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-490-7_59
DO  - 10.2991/978-94-6463-490-7_59
ID  - Chang2024
ER  -