Threat Modelling and Detection Using Semantic Network for Improving Social Media Safety

Social media provides a free space to users to post their information, opinions, feelings, etc. Also, it allows users to easily and simultaneously communicate with each other. As a result, threat detection in social media is critical for ensuring the user’s safety and preventing suspicious activitie...

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Bibliographic Details
Published in:International journal of computer network and information security 2023-02, Vol.15 (1), p.39-53
Main Authors: Fkih, Fethi, Al-Turaif, Ghadeer
Format: Article
Language:English
Subjects:
Algorithms
Digital media
Ethnic factors
Forecasting
Internet
Machine learning
Mathematical models
Messages
Model accuracy
Safety
Semantics
Social networks
Threat models
Threats
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Summary:Social media provides a free space to users to post their information, opinions, feelings, etc. Also, it allows users to easily and simultaneously communicate with each other. As a result, threat detection in social media is critical for ensuring the user’s safety and preventing suspicious activities such as criminal behavior, hate speech, ethnic conflicts and terrorist plots. These suspicious activities have a negative impact on the community’s life and cause tension and social unrest among individuals in both inside and outside of cyberspace. Furthermore, with the recent popularity of social networking sites, the number of discussions containing threats is increasing, causing fear in various parties, whether at the individual or state level. Moreover, these social networking service providers do not have complete control over the content that users post. In this paper, we propose to design a threat detection model on Twitter using a semantic network. To achieve this aim, we designed a threat semantic network, named, ThrNet that will be integrated in our proposed threat detection model called, DetThr. We compared the performance of our model (DetThr) with a set of well-known Machine Learning algorithms. Results show that the DetThr model achieves an accuracy of 76% better than Machine Learning algorithms. It works well with an error rate of forecasting threatening tweet messages as non-threatening (false negatives) is about 29%, while the error rate of forecasting non-threatening tweet messages as threatening (false positives) is about 19%.
ISSN:2074-9090
2074-9104
DOI:10.5815/ijcnis.2023.01.04