Maximizing Influence and Mitigating Harmful Viral Content in Social Networks

This study presents an integrated framework containing sentiment and network analysis for social media modernization to mitigate the spread of harmful viral posts. The research focuses on detecting harmful content, identifying key influencers, and generating counter-narratives to promote constructive engagement. A Twitter Dataset was preprocessed to remove URLs, special characters, and numbers, and the VADER Tool was used to classify tweets into harmful, positive, and neutral categories. Network analysis was conducted by constructing directed retweet graphs to visualize information flow and identify influential users using the PageRank Algorithm and Vander centrality metrics. Counter-narratives were generated for harmful tweets to neutralize negativity and encourage positive discourse. Results show that integrating sentiment and network analysis reduces harmful content propagation by approximately 60 per cent through effective targeting of influential users. The proposed approach offers a scalable and data-driven model for social media moderation, contributing to safer and more ethical online communication environments by balancing freedom of expression with responsible content regulation.

@article{Mohsin2025,
  title     = {Maximizing Influence and Mitigating Harmful Viral Content in Social Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Mohsin and Muhammad Yaseen and Umar Farooq Khattak and Gohar Rahman},
  doi       = {10.14569/IJACSA.2025.0161111},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161111}
}