Abstract
Cigarette and e-cigarette advertisements often portray positive images of smoking behaviour, especially amongst younger generations. It portrays a lifestyle in which smoking cigarettes or e-cigarettes are normal and an important part of human lives. Images of cigarette smoking on social media platforms have played an influential role in encouraging people to smoke. There is a growing need of advanced mathematical models and machine learning techniques to monitor the portrayal of cigarette and e-cigarette use on social media platforms, as well as other harmful products to human health. In this study, we have annotated a set of 1,333 smoking images collected from a wide array of communication media. In addition, we evaluated three state-of-the-art segmentation algorithms including Mask R-CNN, Cascade Mask-R-CNN and Hybrid Task Cascade (HTC) by using the MMDetection framework to detect smoking images within our annotated dataset. The study plays an important role towards developing a practical monitoring system, which can inform policy actions to restrict unhealthy advertisements on social media and other related platforms. Finally, our evaluation results show that Mask R-CNN outperforms Cascade Mask RCNN and HTC in terms of Average Precision and Average Recall.
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Hashmi, M.U., Nguyen, N.D., Johnstone, M., Backholer, K., Bhatti, A. (2021). Application Based Cigarette Detection on Social Media Platforms Using Machine Learning Algorithms. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2021. Lecture Notes in Computer Science(), vol 13076. Springer, Cham. https://doi.org/10.1007/978-3-030-91387-8_5
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