Text mining applications to support health library practice: A case study on marijuana legalization Twitter analytics

IF 2.2 4区医学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

Health Information and Libraries Journal Pub Date : 2023-01-04 DOI:10.1111/hir.12473

Janice Y. Kung MLIS, Kynan Ly MA, Ali Shiri PhD

{"title":"Text mining applications to support health library practice: A case study on marijuana legalization Twitter analytics","authors":"Janice Y. Kung MLIS, Kynan Ly MA, Ali Shiri PhD","doi":"10.1111/hir.12473","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Twitter is rich in data for text and data analytics research, with the ability to capture trends.</p>\n </section>\n \n <section>\n \n <h3> Objectives</h3>\n \n <p>This study examines Canadian tweets on marijuana legalization and terminology used. Presented as a case study, Twitter analytics will demonstrate the varied applications of how this kind of research method may be used to inform library practice.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Twitter API was used to extract a subset of tweets using seven relevant hashtags. Using open-source programming tools, the sampled tweets were analysed between September to November 2018, identifying themes, frequently used terms, sentiment, and co-occurring hashtags.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>More than 1,176,000 tweets were collected. The most popular hashtag co-occurrence, two hashtags appearing together, was #cannabis and #CdnPoli. There was a high variance in the sentiment analysis of all collected tweets but most scores had neutral sentiment.</p>\n </section>\n \n <section>\n \n <h3> Discussion</h3>\n \n <p>The case study presents text-mining applications relevant to help make informed decisions in library practice through service analysis, quality analysis, and collection analysis.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Findings from sentiment analysis may determine usage patterns from users. There are several ways in which libraries may use text mining to make evidence-informed decisions such as examining all possible terminologies used by the public to help inform comprehensive evidence synthesis projects and build taxonomies for digital libraries and repositories.</p>\n </section>\n </div>","PeriodicalId":47580,"journal":{"name":"Health Information and Libraries Journal","volume":"41 1","pages":"53-63"},"PeriodicalIF":2.2000,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Information and Libraries Journal","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/hir.12473","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}

引用次数: 0

Abstract

Background

Twitter is rich in data for text and data analytics research, with the ability to capture trends.

Objectives

This study examines Canadian tweets on marijuana legalization and terminology used. Presented as a case study, Twitter analytics will demonstrate the varied applications of how this kind of research method may be used to inform library practice.

Methods

Twitter API was used to extract a subset of tweets using seven relevant hashtags. Using open-source programming tools, the sampled tweets were analysed between September to November 2018, identifying themes, frequently used terms, sentiment, and co-occurring hashtags.

Results

More than 1,176,000 tweets were collected. The most popular hashtag co-occurrence, two hashtags appearing together, was #cannabis and #CdnPoli. There was a high variance in the sentiment analysis of all collected tweets but most scores had neutral sentiment.

Discussion

The case study presents text-mining applications relevant to help make informed decisions in library practice through service analysis, quality analysis, and collection analysis.

Conclusions

Findings from sentiment analysis may determine usage patterns from users. There are several ways in which libraries may use text mining to make evidence-informed decisions such as examining all possible terminologies used by the public to help inform comprehensive evidence synthesis projects and build taxonomies for digital libraries and repositories.

查看原文本刊更多论文

支持卫生图书馆实践的文本挖掘应用：大麻合法化推特分析案例研究。

背景：Twitter 拥有丰富的数据，可用于文本和数据分析研究，并能捕捉趋势：本研究探讨了加拿大有关大麻合法化的推文和使用的术语。作为一项案例研究，推特分析将展示这种研究方法在图书馆实践中的各种应用：方法：使用 Twitter API 提取使用七个相关标签的推文子集。使用开源编程工具，在2018年9月至11月期间对采样推文进行分析，确定主题、常用术语、情感和共现标签：结果：共收集了超过 117.6 万条推文。最受欢迎的标签共现（两个标签同时出现）是 #cannabis 和 #CdnPoli。对所有收集到的推文进行的情感分析存在很大差异，但大多数分数都是中性情感：讨论：本案例研究介绍了文本挖掘的相关应用，通过服务分析、质量分析和馆藏分析，帮助图书馆在实践中做出明智的决策：情感分析的结果可以确定用户的使用模式。图书馆可以通过多种方式使用文本挖掘来做出有依据的决策，例如检查公众可能使用的所有术语，以帮助为综合证据合成项目提供信息，并为数字图书馆和资料库建立分类标准。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Health Information and Libraries Journal INFORMATION SCIENCE & LIBRARY SCIENCE-

CiteScore

6.70

自引率

10.50%

发文量

期刊介绍： Health Information and Libraries Journal (HILJ) provides practitioners, researchers, and students in library and health professions an international and interdisciplinary forum. Its objectives are to encourage discussion and to disseminate developments at the frontiers of information management and libraries. A major focus is communicating practices that are evidence based both in managing information and in supporting health care. The Journal encompasses: - Identifying health information needs and uses - Managing programmes and services in the changing health environment - Information technology and applications in health - Educating and training health information professionals - Outreach to health user groups