Filo-Transformer: A Graph-Based Model with Phylogenetic Tree Alignment and Transformers for Rumor and Fake News Detection

• Acauan C. Ribeiro UFAM / UFRR
• Eduardo L. Feitosa UFAM
• André Carvalho UFAM

DOI: https://doi.org/10.5753/sbseg.2025.10657

Abstract

This paper presents Filo-Transformer, an innovative approach that combines the deep semantics of Transformer models with the evolutionary analysis of Tree Alignment Graphs (TAGs) to detect rumors and fake news. The model uses embeddings (SBERT/GPT) to represent content and extracts real phylogenetic attributes from Twitter conversation cascades, such as cascade depth, branching factor, and verified user ratio. A Feature Tokenizer Transformer (FT-Transformer) integrates this information for classification. Experiments on the PHEME dataset show that Filo-Transformer outperforms purely semantic models across all major metrics, with fusion weights learning converging to 65% phylogenetic and 35% semantic features, confirming the value of structural propagation patterns.

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