Summary of Detection Of Opioid Users From Reddit Posts Via An Attention-based Bidirectional Recurrent Neural Network, by Yuchen Wang et al.
Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network
by Yuchen Wang, Zhengyu Fang, Wei Du, Shuai Xu, Rong Xu, Jing Li
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning approaches can potentially detect opioid users by analyzing data from social media. The paper presents a study that collects and analyzes user posts from Reddit to identify opioid users. Over a period of one month, more than 1,000 users posted on three sub-reddits, with some containing keywords like “opioid,” “opiate,” or “heroin.” Slang words like “black” or “chocolate” were also collected. The study applies an attention-based bidirectional long short memory model to identify opioid users. Experimental results show that the approach significantly outperforms competitive algorithms in terms of F1-score. Additionally, the model extracts most informative words, such as “opiate,” “opioid,” and “black,” from posts via the attention layer, providing insights on how the algorithm distinguishes drug users from non-drug users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The opioid epidemic is a growing health problem in the United States. To combat this crisis, researchers are exploring new ways to detect opioid users. One approach is using machine learning algorithms to analyze social media data. In this study, scientists collected posts from Reddit and used an attention-based model to identify users who were discussing opioids. They found that their method worked better than other approaches at detecting opioid users. |
Keywords
* Artificial intelligence * Attention * F1 score * Machine learning
