PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit
PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit
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DOI：
10.1109/asonam55673.2022.10068721
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Abdulaziz Alhamadani;Shailik Sarkar;Lulwah Alkulaib;Chang-Tien Lu
中科院分区：
文献类型：
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作者：
Abdulaziz Alhamadani;Shailik Sarkar;Lulwah Alkulaib;Chang-Tien Lu
药物滥用的流行在过去几年一直在上升，特别是在COVID-19大流行开始之后。我们对Reddit的初步观察显示，从2018年到2020年，关于药物的讨论增加了45%到200%，参与这些讨论的独立用户数量也增加了。现有的工作重点是利用社交媒体区分潜在的药物滥用聊天与无害的聊天，无论滥用的是什么药物。另一些讲习班侧重于从社交媒体了解药物滥用的趋势和原因。为此，我们引入了PRISTINE（Reddit上的阿片类药物危机检测），我们的工作使用增强的动态查询扩展（DQE）从Reddit评论中动态检测并提取不断变化的误导性药物名称，并借助强大的预训练嵌入来构建文本图卷积网络，以检测Reddit评论对应的药物类别。此外，我们进行了大量的实验，以调查我们的模型的有效性。
The drug abuse epidemic has been on the rise in the past few years, particularly after the start of COVID-19 pandemic. Our preliminary observations on Reddit alone show that discussions on drugs from 2018 to 2020 increased between a range of 45% to 200%, and so has the number of unique users participating in those discussions. Existing efforts focused on utilizing social media to distinguish potential drug abuse chats from unharmful chats regardless of what drug is being abused. Others focused on understanding the trends and causes of drug abuse from social media. To this end, we introduce PRISTINE (opioid crisis detection on reddit), our work dynamically detects-and extracts evolving misleading drug names from Reddit comments using reinforced Dynamic Query Expansion (DQE) and constructs a textual Graph Convolutional Network with the aid of powerful pre-trained embeddings to detect which type of drug class a Reddit comment corresponds to. Further, we perform extensive experiments to investigate the effectiveness of our model.