Abdulaziz Alhamadani, Shailik Sarkar, Lulwah Alkulaib, Chang-Tien Lu
{"title":"PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit","authors":"Abdulaziz Alhamadani, Shailik Sarkar, Lulwah Alkulaib, Chang-Tien Lu","doi":"10.1109/ASONAM55673.2022.10068721","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASONAM55673.2022.10068721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
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.
在过去几年中,特别是在COVID-19大流行开始之后,药物滥用流行病呈上升趋势。我们在Reddit上的初步观察显示,从2018年到2020年,关于毒品的讨论增加了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吸毒聊天和无害的聊天,而不管滥用的是什么药物。其他人则侧重于从社交媒体上了解药物滥用的趋势和原因。为此,我们引入了reddit上的阿片类药物危机检测(opioid crisis detection on reddit),我们的工作使用增强的动态查询扩展(DQE)从reddit评论中动态检测和提取不断演变的误导性药物名称,并借助强大的预训练嵌入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来研究我们模型的有效性。