The advantage of the proposed combination on a conceptual level is that we did not have to introduce contextual information at an early stage and were therefore less prone to overlook certain phenomena since we did not precondition the pipeline to find only what we expected, which is a typical and bias-prone strategy in object detection/recognition. Notwithstanding the existence of a number of quantitative metrics and benchmarks for the evaluation of multimodal models with image understanding capabilities (Yang et al., 2023; Bubeck et al., 2023; <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#222222)">Wang et al. 2023;</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#222222)">Yue et al. 2024</hi>), there is a notable lack of quantitative metrics, benchmarks, or datasets that have been specifically designed for the assessment of the performance of these models in the analysis of protest images, particularly those from Eastern Europe. In light of the aforementioned gap, a promising alternative approach is to employ grounded theory to identify potential errors, hallucinations, and ambiguities that may arise when using MLLMs to describe protest images using keywords. We plan to further develop this idea in accordance with the recent paper of Hwang et al. (2023). Adapting this framework for analysing protest images will allow us to systematically uncover the strengths and limitations of multimodal models in our particular task. After these improvements, we aim to test the method on a larger corpus (~1000 videos) and quantitatively compare the videos based on their topic distributions to uncover narrative strategies based on visual content.