A Multilayered Preprocessing Approach for Recognition and Classification of Malicious Social Network Messages

The primary methods of communication in the modern world are social networks, which are rife with harmful messages that can injure both psychologically and financially. Most websites do not offer services that automatically delete or send malicious communications back to the sender for correction, o...

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Bibliographic Details
Published in:Electronics (Basel) 2023-09, Vol.12 (18), p.3785
Main Authors: Čepulionytė, Aušra, Toldinas, Jevgenijus, Lozinskis, Borisas
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
Classification
Computational linguistics
Cybercrime
Data mining
Data security
Datasets
Deep learning
False information
Internet
Language
Language processing
Machine learning
Messages
Methods
Natural language interfaces
Natural language processing
Neural networks
Preprocessing
Recognition
Safety and security measures
Sentiment analysis
Social media
Social networks
Support vector machines
User behavior
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Summary:The primary methods of communication in the modern world are social networks, which are rife with harmful messages that can injure both psychologically and financially. Most websites do not offer services that automatically delete or send malicious communications back to the sender for correction, or notify the sender of inaccuracies in the content of the messages. The deployment of such systems could make use of techniques for identifying and categorizing harmful messages. This paper suggests a novel multilayered preprocessing approach for the recognition and classification of malicious social network messages to limit negative impact, resulting in fewer toxic messages, scams, and aggressive comments in social media messages and commenting areas. As a result, less technical knowledge would be required to investigate the effects of harmful messages. The dataset was created using the regional Lithuanian language with four classes: aggressive, insulting, toxic, and malicious. Three machine learning algorithms were examined, five use cases of a multilayered preprocessing approach were suggested, and experiments were conducted to identify and classify harmful messages in the Lithuanian language.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12183785