AnxietyNet: social media trends with fuzzy logic trees

Neeharika Tripathi *

Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 1165–1168.
Article DOI: 10.30574/ijsra.2024.13.1.1816
Publication history: 
Received on 17 August 2024; revised on 22 September 2024; accepted on 25 September 2024
 
Abstract: 
In the digital age, social media platforms have become integral parts of daily life, facilitating communication, information sharing, and community building on a global scale. However, alongside the benefits of connectivity, concerns regarding public anxiety within these virtual communities have emerged. This research paper delves into the phenomenon of public anxiety on social media and proposes a novel approach utilizing fuzzy tree logic to analyze and address this issue. Through a comprehensive review of literature, theoretical frameworks, and practical applications, this paper aims to provide insights into the complex dynamics of public anxiety and offer potential solutions leveraging machine learning techniques. The proposed methodology involves the development of a cascading model to dynamically compute individual anxiety scores within social media networks. By analyzing structural influences and user interactions, this model aims to provide a nuanced understanding of anxiety dynamics within online communities. Additionally, a probabilistic model is designed to measure anxiety scores of social network messages, utilizing a generalized user profile. The integration of fuzzy tree logic enables the construction of a tree structure to effectively compute anxiety scores, offering a more flexible and human-like decision-making process. Preliminary experiments demonstrate the effectiveness of the proposed approach in capturing and analyzing public anxiety on social media platforms. The fuzzy tree model shows promise in accurately assessing anxiety levels and identifying key factors contributing to social anxiety within online communities. Moreover, the integration of machine learning techniques enhances the scalability and adaptability of the model, allowing for real-time analysis and proactive intervention strategies. This research underscores the importance of addressing public anxiety on social media through innovative analytical frameworks. By leveraging fuzzy tree logic and machine learning techniques, we can gain deeper insights into the dynamics of anxiety within online communities and develop targeted interventions to support individuals experiencing distress. Collaborative efforts in this endeavor hold the potential to foster societal well-being and resilience in the digital age.
 
Keywords: 
Fuzzy Tree Logic; Anxiety Detection; Data Analytics; Machine Learning
 
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