Predicting Public Opinion on Drug Legalization: Social Media Analysis and Consumption Trends

被引:1
|
作者
Motlagh, Farahnaz Golrooy [1 ]
Shekarpour, Saeedeh [2 ]
Sheth, Amit [1 ]
Thirunarayan, Krishnaprasad [1 ]
Raymer, Michael L. [1 ]
机构
[1] Wright State Univ, Kno E Sis Ctr, Dayton, OH 45435 USA
[2] Univ Dayton, Dayton, OH 45469 USA
基金
美国国家科学基金会;
关键词
Marijuana Legalization; Drug Abuse Ontology; Public Opinion; Sentiment Analysis; Prediction; Consumption Trends; Entity Extraction; Machine Learning; MARIJUANA; TWITTER;
D O I
10.1145/3341161.3344380
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we focus on the collection and analysis of relevant Twitter data on a state-by-state basis for (i) measuring public opinion on marijuana legalization by mining sentiment in Twitter data and (ii) determining the usage trends for six distinct types of marijuana. We overcome the challenges posed by the informal and ungrammatical nature of tweets to analyze a corpus of 306,835 relevant tweets collected over the four-month period, preceding the November 2015 Ohio Marijuana Legalization ballot and the four months after the election for all states in the US. Our analysis revealed two key insights: (i) the people in states that have legalized recreational marijuana express greater positive sentiments about marijuana than the people in states that have either legalized medicinal marijuana or have not legalized marijuana at all; (ii) the states that have a high percentage of positive sentiment about marijuana is more inclined to authorize (e.g., by allowing medical marijuana) or broaden its legal usage (e.g., by allowing recreational marijuana in addition to medical marijuana). Our analysis shows that social media can provide reliable information and can serve as an alternative to traditional polling of public opinion on drug use and epidemiology research.
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页码:952 / +
页数:10
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