Evaluating Information Sources on Social Media: A Systematic Review and Conceptual Model

Abstract

This article presents a systematic review and conceptual framework for analyzing information sources in social networks, with a focus on Telegram as the dominant news platform in Ukraine. The relevance of the study is determined by the dramatic shift in media consumption patterns following Russia's full-scale invasion in February 2022: Telegram's audience tripled from 20% to 60% within months, and by 2025 it remains the primary news source for 52% of the adult population, surpassing television (25%), YouTube (32%), and other platforms. Despite this dominance, only 29% of users trust Telegram channels, creating a paradox where mass consumption coexists with low trust and minimal verification ‒ only 6% of Ukrainians consult fact-checking organizations. The article examines the mechanisms of information manipulation, including the five-level cascading narratives model (from "Russian World" to alternative information) and the five-level amplification pyramid that describes the transformation of deliberate disinformation into unwitting misinformation through state media, expert legitimization, portal localization, social media dissemination, and organic user spread. The 4D tactics framework (Dismiss, Distort, Distract, Dismay) systematizes the core techniques of these operations. A comparative analysis of ten methods for information source analysis spanning 2008-2025 reveals a clear evolution from isolated statistical approaches through graph-based methods to modern hybrid systems integrating natural language processing, graph neural networks, and attention mechanisms. The highest performance was demonstrated by the Lightning system (AUC 86.83%) and OnlineAgglomerative (93.2% accuracy). However, none of the reviewed methods simultaneously provides comprehensive semantic, temporal, and network analysis for source evaluation. To address this gap, the article proposes a conceptual model for assessing information source similarity (similarity_score) that integrates four components: cosine similarity of content embeddings, thematic clusterization, timed semantic influence for detecting temporal-semantic dependencies between sources, and network proximity in the information flow graph. The model is extensible and can incorporate additional metrics such as sentiment analysis, named entity recognition, and toxicity assessment. An information system architecture for practical implementation on Telegram data is also proposed.