Year: 2025
Type: conference-paper
Abstract: Out-of-context and misattributed imagery is the leading form of media manipulation in today's misinformation and disinformation landscape.The existing methods attempting to detect this practice often only consider whether the semantics of the imagery corresponds to the text narrative, missing manipulation so long as the depicted objects or scenes somewhat correspond to the narrative at hand.To tackle this, we introduce News Media Provenance Dataset, a dataset of news articles with provenance-tagged images.We formulate two tasks on this dataset, location of origin relevance (LOR) and date and time of origin relevance (DTOR), and present baseline results on six large language models (LLMs).We identify that, while the zero-shot performance on LOR is promising, the performance on DTOR hinders, leaving room for specialized architectures and future work.
Authors: Tomas Peterka, Matyas Bohacek
Institution: Stanford University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4412944262