Luo Waylon, Jin Ruoming, Kenne Deric, Phan NhatHai, Tang Tang
Department of Computer Science, Kent State University, Kent, OH, United States.
College of Public Health, Kent State University, Kent, OH, United States.
JMIR Form Res. 2024 Dec 30;8:e49567. doi: 10.2196/49567.
The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics. Our study provides a novel finding on the prevalence of drug abuse by defining a new subcategory of X content: "tweets that reference established drug lyrics."
We aim to investigate drug trends in popular music on X, identify and classify popular drugs, and analyze related artists' gender, genre, and popularity. Based on the collected data, our goal is to create a prediction model for future drug trends and gain a deeper understanding of the characteristics of users who cite drug lyrics on X.
X data were collected from 2015 to 2017 through the X streaming application programming interface (API). Drug lyrics were obtained from the Genius lyrics database using the Genius API based on drug keywords. The Smith-Waterman text-matching algorithm is used to detect the drug lyrics in posts. We identified famous drugs in lyrics that were posted. Consequently, the analysis was extended to related artists, songs, genres, and popularity on X. The frequency of drug-related lyrics on X was aggregated into a time-series, which was then used to create prediction models using linear regression, Facebook Prophet, and NIXTLA TimeGPT-1. In addition, we analyzed the number of followers of users posting drug-related lyrics to explore user characteristics.
We analyzed over 1.97 billion publicly available posts from 2015 to 2017, identifying more than 157 million that matched drug-related keywords. Of these, 150,746 posts referenced drug-related lyrics. Cannabinoids, opioids, stimulants, and hallucinogens were the most cited drugs in lyrics on X. Rap and hip-hop dominated, with 91.98% of drug-related lyrics from these genres and 84.21% performed by male artists. Predictions from all 3 models, linear regression, Facebook Prophet, and NIXTLA TimeGPT-1, indicate a slight decline in the prevalence of drug-related lyrics on X over time.
Our study revealed 2 significant findings. First, we identified a previously unexamined subset of drug-related content on X: drug lyrics, which could play a critical role in models predicting the surge in drug-related incidents. Second, we demonstrated the use of cutting-edge time-series forecasting tools, including Facebook Prophet and NIXTLA TimeGPT-1, in accurately predicting these trends. These insights contribute to our understanding of how social media shapes public behavior and sentiment toward drug use.
毒品文化在流行音乐和社交媒体中无处不在，这一点已变得很明显。此前的研究已考察了社交媒体和流行音乐中的药物滥用内容；然而，据我们所知，这两个领域中药物滥用内容的交集尚未得到探索。为应对持续的毒品流行问题，我们分析了推特（后更名为X）上与毒品相关的内容，特别关注歌词。我们的研究通过定义X内容的一个新子类别：“提及既定毒品歌词的推文”，提供了关于药物滥用流行情况的新发现。
我们旨在调查X上流行音乐中的毒品趋势，识别并分类流行毒品，分析相关艺术家的性别、音乐流派和受欢迎程度。基于收集到的数据，我们的目标是创建一个预测未来毒品趋势的模型，并更深入地了解在X上引用毒品歌词的用户特征。
通过X流式应用程序编程接口（API）收集了2015年至2017年的X数据。使用基于毒品关键词的Genius API从Genius歌词数据库中获取毒品歌词。采用史密斯 - 沃特曼文本匹配算法来检测帖子中的毒品歌词。我们确定了所发布歌词中提到的知名毒品。随后，分析扩展到X上的相关艺术家、歌曲、流派和受欢迎程度。X上与毒品相关歌词的频率汇总为一个时间序列，然后用于使用线性回归、Facebook Prophet和NIXTLA TimeGPT - 1创建预测模型。此外，我们分析了发布与毒品相关歌词的用户的关注者数量，以探索用户特征。
我们分析了2015年至2017年超过19.7亿条公开可用的帖子，识别出超过1.57亿条与毒品相关关键词匹配的帖子。其中，150,746条帖子引用了与毒品相关的歌词。大麻素、阿片类药物、兴奋剂和致幻剂是X上歌词中引用最多的毒品。说唱和嘻哈音乐占主导地位，这些流派中91.98%的歌词与毒品相关，且84.21%由男性艺术家演唱。线性回归、Facebook Prophet和NIXTLA TimeGPT - 1这三个模型的预测均表明，随着时间的推移，X上与毒品相关歌词的流行率略有下降。
我们的研究揭示了两个重要发现。首先，我们在X上识别出了一个以前未被研究的与毒品相关内容的子集：毒品歌词，其在预测与毒品相关事件激增的模型中可能发挥关键作用。其次，我们展示了使用前沿的时间序列预测工具，包括Facebook Prophet和NIXTLA TimeGPT - 1，来准确预测这些趋势。这些见解有助于我们理解社交媒体如何塑造公众对吸毒的行为和态度。
