Firearms on Twitter: A Novel Object Detection Pipeline


  • Ryan Harvey The University of Queensland, Australia
  • Rémi Lebret École Polytechnique Fédérale de Lausanne
  • Stéphane Massonnet École Polytechnique Fédérale de Lausanne
  • Karl Aberer École Polytechnique Fédérale de Lausanne
  • Gianluca Demartini The University of Queensland, Australia



, Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health, Social media usage on mobile devices; location, human mobility, and behavior, Qualitative and quantitative studies of social media


Social media is an important source of real-time imagery concerning world events. One subset of social media posts which may be of particular interest are those featuring firearms. These posts can give insight into weapon movements, troop activity and civilian safety. Object detection tools offer important opportunities for insight into these images. Unfortunately, these images can be visually complex, poorly lit and generally challenging for object detection models. We present an analysis of existing gun detection datasets, and find that these datasets to not effectively address the challenge of gun detection on real-life images. Following this, we present a novel object detection pipeline. We train our pipeline on a number of datasets including one created for this investigation made up of Twitter images of the Russo-Ukrainian War. We compare the performance of our model as trained on the different datasets to baseline numbers provided by original authors as well as a YOLO v5 benchmark. We find that our model outperforms the state-of-the-art benchmarks on contextually rich, real-life-derived imagery of firearms.




How to Cite

Harvey, R., Lebret, R., Massonnet, S., Aberer, K., & Demartini, G. (2023). Firearms on Twitter: A Novel Object Detection Pipeline. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 1128-1132.