• RESHMA B
• Dr.D.Shanmuga Priyaa

Endogenous negative influence in social networks results from rumours, fake news, harmful opinions, negative emotions and aggressive behavior that are spread among connected users of the network. It is not so simple to suppress, however, because of the lack of integration with existing harmful content detection, need for intervention, the need for labelled data and the need for heavy models and complete network information for modelling of diffusion and identification of critical nodes. From 2024 to 2026 and 25 studies were reviewed. It included methods for misinformation detection machine-learning, deep-learning, transformer, graph-neural-network, reinforcement-learning, epidemic-diffusion and evolutionary-optimization; opinion modeling emotion recognition, influential-node selection, proactive defence, budget-constrained intervention. The comparative analysis showed high detection performance, but low interpretability and scalability, as well as generalisation and adaptability across platforms and in real time. The survey revealed that future systems must integrate semantic, structural, temporal, emotional and behavioural information into a coherent, explainable, adaptive system to track critical-spreaders, predict the spread and intervene with resources to prevent the propagation.

Critical Node Identification, Endogenous Negative Influence, Graph Neural Networks, Misinformation Suppression, Node Intervention, Social Networks.

"A Comprehensive Survey on Suppressing the Endogenous Negative Influence through Node Intervention in Social Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.b237-b244, September-2026, Available :http://www.jetir.org/papers/JETIR2609128.pdf

"A Comprehensive Survey on Suppressing the Endogenous Negative Influence through Node Intervention in Social Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppb237-b244, September-2026, Available at : http://www.jetir.org/papers/JETIR2609128.pdf