@misc{braun2026,

title = {Video Misinformation Detection: A Systematic Review of Manipulation Tactics, Datasets and Algorithms},

author = {Florian Braun and Truc Hoang and Gianluca Demartini and Isao Echizen and Timo Spinde},

url = {https://media-bias-research.org/wp-content/uploads/2026/05/braun2026.pdf},

doi = {10.13140/RG.2.2.34719.52645},

year  = {2026},

date = {2026-05-13},

booktitle = {ACM Computing Surveys [under review]},

journal = {ACM Computing Surveys (under review)},

pages = {35},

publisher = {Association for Computing Machinery (ACM)},

abstract = {Misinformation research has identified diverse strategies through which false or misleading content gains credibility, spreads, and influences audiences. Yet no systematic review has synthesized how computer science research on video misinformation detection defines and engages with different misinformation strategies. We therefore systematically review 51 of over 2200 retrieved computer science papers, organizing them by misinformation strategy, dataset and label design, and detection method. We find that while model architectures address specific misinformation strategies, prevailing binary real/fake labels obscure whether each strategy is actually detected, limiting the transparency and diagnostic value of current evaluations. These findings motivate strategy-aware video misinformation detection.},

howpublished = {preprint},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}