Fine Grained Categorization of Drug Usage Tweets
Fine Grained Categorization of Drug Usage Tweets
复制标题
药物使用推文的细粒度分类
DOI：
10.1007/978-3-031-05061-9_19
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
ChengXiang Zhai
中科院分区：
文献类型：
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作者：
Priyanka Dey;ChengXiang Zhai
过去几十年来，药物滥用和过量一直困扰着美国，并严重影响了几个社区和家庭。吸毒者往往很难得到他们所需要的援助，因此许多吸毒案件一直未被发现，直到发现时为时已晚。随着社交媒体时代的蓬勃发展，许多用户往往更喜欢通过虚拟环境来讨论他们的情绪，在虚拟环境中，他们也可以遇到其他处理类似问题的人。社交媒体网站的广泛使用为应用NLP技术分析内容创造了有趣的新机会，并可能帮助那些吸毒者（例如，早期发现和干预）。为了利用这些机会，我们研究了将有关药物使用的推文分类为细粒度类别。为了便于研究所提出的新问题，我们创建了一个新的数据集，并使用该数据来研究多个代表性的分类方法的有效性。我们进一步分析这些方法所产生的错误，并探索新的功能来改进它们。我们发现，一个新的功能的基础上鸣叫的语气是非常有用的，在提高分类分数。我们进一步探讨了可能的下游应用基于这个分类系统，并提供了一套初步的调查结果。
Drug misuse and overdose has plagued the United States over the past decades and has severely impacted several communities and families. Often, it is difficult for drug users to get the assistance they need and thus many usage cases remain undetected until it is too late. With the booming age of social media, many users often prefer to discuss their emotions through virtual environments where they can also meet others dealing with similar problems. The widespread use of social media sites creates interesting new opportunities to apply NLP techniques to analyze content and potentially help those drug users (e.g., early detection and intervention). To tap into such opportunities, we study categorization of tweets about drug usage into fine-grained categories. To facilitate the study of the proposed new problem, we create a new dataset and use this data to study the effectiveness of multiple representative categorization methods. We further analyze errors made by these methods and explore new features to improve them. We find that a new feature based on tweet tone is quite useful in improving classification scores. We further explore possible downstream applications based on this classification system and provide a set of preliminary findings.