Harnessing Computational Communication Science: Analyzing Social Network Structures, Sentiment Dynamics, and Algorithmic Content Personalization to Combat Misinformation in Digital Ecosystems

Digital ecosystems are inundated with misinformation that undermines public confidence, public health, and political stability. In this paper, we discuss how computational communication science can be used to combat fake news through three dimensions: social network structures, sentiment and algorithmic content personalization. In the first, it explores how network topology, centrality and clustering enable or discourage the dissemination of misinformation. Second, it highlights emotional triggers that are targeted by misinformation, and reveals how sentiment analysis can assist in early detection and mitigation. Third, the studies examine the biases of algorithmic personalisation and recommend design approaches that emphasize diversity, openness, and trust. Bringing together the insights from network analysis, sentiment tracking and algorithmic reform, this paper presents a whole-system approach to countering false information. This result underscores the importance of interdisciplinary engagement between technologists, policymakers and educators to develop sustainable digital ecosystems that reconcile user interaction with informational integrity.