A Bibliometric Mapping of Transformer-Based Misinformation Detection: Trends and Gaps

The rapid spread of misinformation on social platforms has intensified research on automated detection, with Transformer-based architectures becoming a primary technical foundation. However, the volume and diversity of publications make it difficult to track the field’s evolution. It is also challenging to identify the contributors, influential studies, and remaining gaps that shape its knowledge base. This study presents a bibliometric mapping of Transformer-based research on misinformation detection, based on Scopus records retrieved on 3rd March 2026 (n = 3,637). Performance indicators and science mapping analysis are used to profile publication growth, subject-area distribution, leading countries, institutions, and authors, citation impact, and thematic structure. Results show that conference proceedings (47.18%) and journals (41.93%) dominate dissemination, reflecting both rapid-cycle publishing and archival consolidation. Computer Science accounts for most publications (84.99%), while Social Sciences and Medicine contribute non-trivial shares, indicating applied and societal engagement. China, the United States, and India lead in national output, and institutional productivity is concentrated in a small set of research hubs. Citation indicators (h-index = 82; g-index = 154) suggest a broad influence with a distinct, highly cited core. Keyword mapping confirms a stable emphasis on NLP-driven detection in social media and shows recent growth in multimodal approaches and generative-AI-related topics. The findings provide an evidence-based overview of current trends and emerging directions, helping researchers position future work and prioritize underexplored problems.

@article{Rahman2026,
  title     = {A Bibliometric Mapping of Transformer-Based Misinformation Detection: Trends and Gaps},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Borhan Ab Rahman and Mohd Zakree Ahmad Nazri and Mohd Ridzwan Yaakub},
  doi       = {10.14569/IJACSA.2026.0170645},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170645}
}