Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network
<|reference_start|>Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network: The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improving our understanding of the epidemic through better health surveillance is one of the top priorities. In addition to direct testing, machine learning approaches may also allow us to detect opioid users by analyzing data from social media because many opioid users may choose not to do the tests but may share their experiences on social media anonymously. In this paper, we take advantage of recent advances in machine learning, collect and analyze user posts from a popular social network Reddit with the goal to identify opioid users. Posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. In addition to the ones that contain keywords such as opioid, opiate, or heroin, we have also collected posts that contain slang words of opioid such as black or chocolate. We apply an attention-based bidirectional long short memory model to identify opioid users. Experimental results show that the approaches significantly outperform competitive algorithms in terms of F1-score. Furthermore, the model allows us to extract most informative words, such as opiate, opioid, and black, from posts via the attention layer, which provides more insights on how the machine learning algorithm works in distinguishing drug users from non-drug users.<|reference_end|>
@article{wang2024detection,
    title={Detection of Opioid Users from Reddit Posts via an Attention-based
  Bidirectional Recurrent Neural Network},
    author={Yuchen Wang, Zhengyu Fang, Wei Du, Shuai Xu, Rong Xu, and Jing Li},
    journal={arXiv preprint arXiv:2403.15393},
    year={2024},
    doi={10.1145/3673971.3673984},
    archivePrefix={arXiv},
    eprint={2403.15393},
    primaryClass={cs.CL cs.LG cs.SI}
}