Suppr超能文献

关于牙医和医生的推文在情感及疼痛相关语言方面的差异:推特内容的文本分析

Differences in Emotional and Pain-Related Language in Tweets About Dentists and Medical Doctors: Text Analysis of Twitter Content.

作者信息

Johnsen Jan-Are K, Eggesvik Trude B, Rørvik Thea H, Hanssen Miriam W, Wynn Rolf, Kummervold Per Egil

机构信息

Department of Clinical Dentistry, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.

Department of Clinical Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.

出版信息

JMIR Public Health Surveill. 2019 Feb 6;5(1):e10432. doi: 10.2196/10432.

Abstract

BACKGROUND

Social media provides people with easy ways to communicate their attitudes and feelings to a wide audience. Many people, unfortunately, have negative associations and feelings about dental treatment due to former painful experiences. Previous research indicates that there might be a pervasive and negative occupational stereotype related to dentists and that this stereotype is expressed in many different venues, including movies and literature.

OBJECTIVE

This study investigates the language used in relation to dentists and medical doctors on the social media platform Twitter. The purpose is to compare the professions in terms of the use of emotional and pain-related words, which might underlie and reflect the pervasive negative stereotype identified in relation to dentists. We hypothesized that (A) tweets about dentists will have more negative emotion-related words than those about medical doctors and (B) pain-related words occur more frequently in tweets about dentists than in those about medical doctors.

METHODS

Twitter content ("tweets") about dentists and medical doctors was collected using the Twitter application program interface 140Dev over a 4-week period in 2015, scanning the search terms "dentist" and "doctor". Word content of the selected tweets was analyzed using Linguistic Inquiry and Word Count software. The research hypotheses were investigated using nonparametric Wilcoxon-Mann-Whitney tests.

RESULTS

Over 2.3 million tweets were collected in total, of which about one-third contained the word "dentist" and about two-thirds contained the word "doctor." Hypothesis A was supported since a higher proportion of negative words was used in tweets about dentists than in those about medical doctors (z=-10.47; P<.001). Similarly, tests showed a difference in the proportions of anger words (z=-12.54; P<.001), anxiety words (z=-6.96; P<.001), and sadness words (z=-9.58; P<.001), with higher proportions of these words in tweets about dentists than in those about doctors. Also, Hypothesis B was supported since a higher proportion of pain-related words was used in tweets about dentists than in those about doctors (z=-8.02; P<.001).

CONCLUSIONS

The results from this study suggest that stereotypes regarding dentists and dental treatment are spread through social media such as Twitter and that social media also might represent an avenue for improving messaging and disseminating more positive attitudes toward dentists and dental treatment.

摘要

背景

社交媒体为人们提供了向广大受众传达其态度和感受的便捷方式。不幸的是,许多人由于过去的痛苦经历而对牙科治疗有负面联想和感受。先前的研究表明,可能存在一种普遍存在的、与牙医相关的负面职业刻板印象,并且这种刻板印象在包括电影和文学在内的许多不同场合都有所体现。

目的

本研究调查了社交媒体平台推特上与牙医和医生相关的语言使用情况。目的是比较这两个职业在情感和疼痛相关词汇使用方面的情况,这些词汇可能构成并反映了与牙医相关的普遍负面刻板印象。我们假设:(A)关于牙医的推文将比关于医生的推文有更多与负面情绪相关的词汇;(B)与疼痛相关的词汇在关于牙医的推文中出现的频率高于关于医生的推文。

方法

2015年,使用推特应用程序接口140Dev在四周时间内收集了关于牙医和医生的推特内容(“推文”),搜索词为“dentist”和“doctor”。使用语言查询与字数统计软件对所选推文的文字内容进行分析。使用非参数威尔科克森-曼-惠特尼检验来研究研究假设。

结果

总共收集了超过230万条推文,其中约三分之一包含“dentist”一词,约三分之二包含“doctor”一词。假设A得到支持,因为关于牙医的推文中使用负面词汇的比例高于关于医生的推文(z = -10.47;P <.001)。同样,测试表明愤怒词汇(z = -12.54;P <.001)、焦虑词汇(z = -6.96;P <.001)和悲伤词汇(z = -9.58;P <.001)的比例存在差异,关于牙医的推文中这些词汇的比例高于关于医生的推文。此外,假设B也得到支持,因为关于牙医的推文中使用与疼痛相关词汇的比例高于关于医生的推文(z = -8.02;P <.001)。

结论

本研究结果表明,关于牙医和牙科治疗的刻板印象通过推特等社交媒体传播,并且社交媒体也可能是改善信息传递以及传播对牙医和牙科治疗更积极态度的一个途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cc7/6381402/6d015d7f1d16/publichealth_v5i1e10432_fig1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验