• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

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

相似文献

1
On Quantifying Diffusion of Health Information on Twitter.论推特上健康信息传播的量化
IEEE EMBS Int Conf Biomed Health Inform. 2017 Feb;2017:485-488. doi: 10.1109/BHI.2017.7897311. Epub 2017 Apr 13.
2
Examining Tweet Content and Engagement of Canadian Public Health Agencies and Decision Makers During COVID-19: Mixed Methods Analysis.研究 COVID-19 期间加拿大公共卫生机构和决策者的推文内容和参与度:混合方法分析。
J Med Internet Res. 2021 Mar 11;23(3):e24883. doi: 10.2196/24883.
3
How did Ebola information spread on twitter: broadcasting or viral spreading?埃博拉信息在推特上是如何传播的:广播还是病毒式传播?
BMC Public Health. 2019 Apr 25;19(1):438. doi: 10.1186/s12889-019-6747-8.
4
Analysis of twitter users' sharing of official new york storm response messages.推特用户对纽约官方风暴应对信息分享情况的分析。
Med 2 0. 2014 Mar 20;3(1):e1. doi: 10.2196/med20.3237. eCollection 2014 Jan-Jun.
5
What are health-related users tweeting? A qualitative content analysis of health-related users and their messages on twitter.与健康相关的用户在推特上发些什么?对推特上与健康相关的用户及其信息进行定性内容分析。
J Med Internet Res. 2014 Oct 15;16(10):e237. doi: 10.2196/jmir.3765.
6
Engagement with health agencies on twitter.在推特上与卫生机构互动。
PLoS One. 2014 Nov 7;9(11):e112235. doi: 10.1371/journal.pone.0112235. eCollection 2014.
7
Twitter use in scientific communication revealed by visualization of information spreading by influencers within half a year after the Fukushima Daiichi nuclear power plant accident.福岛第一核电站事故半年后,通过对有影响力者传播信息的可视化,揭示了推特在科学传播中的应用。
PLoS One. 2018 Sep 7;13(9):e0203594. doi: 10.1371/journal.pone.0203594. eCollection 2018.
8
User emotion for modeling retweeting behaviors.用户情感建模转发行为。
Neural Netw. 2017 Dec;96:11-21. doi: 10.1016/j.neunet.2017.08.006. Epub 2017 Sep 8.
9
Contents, Followers, and Retweets of the Centers for Disease Control and Prevention's Office of Advanced Molecular Detection (@CDC_AMD) Twitter Profile: Cross-Sectional Study.美国疾病控制与预防中心高级分子检测办公室(@CDC_AMD)推特账号的内容、关注者及转发情况:横断面研究
JMIR Public Health Surveill. 2018 Apr 2;4(2):e33. doi: 10.2196/publichealth.8737.
10
Exploring Discussions About Virtual Reality on Twitter to Inform Brain Injury Rehabilitation: Content and Network Analysis.探索 Twitter 上关于虚拟现实的讨论以了解脑损伤康复:内容和网络分析。
J Med Internet Res. 2024 Jan 19;26:e45168. doi: 10.2196/45168.

引用本文的文献

1
Analyzing Twitter as a Platform for Alzheimer-Related Dementia Awareness: Thematic Analyses of Tweets.将推特作为提高阿尔茨海默病相关痴呆症认知度的平台进行分析:推文的主题分析
JMIR Aging. 2018 Dec 10;1(2):e11542. doi: 10.2196/11542.
2
Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus.在推特上识别保护性健康行为:旅行建议与寨卡病毒的观察性研究
J Med Internet Res. 2019 May 13;21(5):e13090. doi: 10.2196/13090.
3
Communication About Hereditary Cancers on Social Media: A Content Analysis of Tweets About Hereditary Breast and Ovarian Cancer and Lynch Syndrome.社交媒体上关于遗传性癌症的交流:对关于遗传性乳腺癌、卵巢癌和林奇综合征推文的内容分析
J Cancer Educ. 2020 Feb;35(1):131-137. doi: 10.1007/s13187-018-1451-4.
4
Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure.牙科推特网络中信息的传播范围:一项队列研究,考察用户受欢迎程度、交流模式和网络结构。
J Med Internet Res. 2018 Sep 13;20(9):e10781. doi: 10.2196/10781.

本文引用的文献

1
Exploratory Analysis of Marketing and Non-marketing E-cigarette Themes on Twitter.推特上营销和非营销电子烟主题的探索性分析
Soc Inform (2016). 2016 Nov;10047:307-322. doi: 10.1007/978-3-319-47874-6_22. Epub 2016 Oct 19.
2
Toward automated e-cigarette surveillance: Spotting e-cigarette proponents on Twitter.迈向电子烟自动监测:在推特上识别电子烟支持者。
J Biomed Inform. 2016 Jun;61:19-26. doi: 10.1016/j.jbi.2016.03.006. Epub 2016 Mar 11.
3
Digital drug safety surveillance: monitoring pharmaceutical products in twitter.数字药品安全监测:在推特上监测药品
Drug Saf. 2014 May;37(5):343-50. doi: 10.1007/s40264-014-0155-x.
4
Twitter mining for fine-grained syndromic surveillance.用于细粒度症状监测的推特挖掘
Artif Intell Med. 2014 Jul;61(3):153-63. doi: 10.1016/j.artmed.2014.01.002. Epub 2014 Jan 31.
5
Analyzing health organizations' use of Twitter for promoting health literacy.分析健康组织利用 Twitter 提高健康素养。
J Health Commun. 2013;18(4):410-25. doi: 10.1080/10810730.2012.727956. Epub 2013 Jan 7.

论推特上健康信息传播的量化

On Quantifying Diffusion of Health Information on Twitter.

作者信息

Bakal Gokhan, Kavuluru Ramakanth

机构信息

Department of Computer Science, University of Kentucky, Lexington, KY, USA.

Division of Biomedical Informatics (Department of Internal Medicine) and the Department of Computer Science, University of Kentucky, Lexington, KY, USA.

出版信息

IEEE EMBS Int Conf Biomed Health Inform. 2017 Feb;2017:485-488. doi: 10.1109/BHI.2017.7897311. Epub 2017 Apr 13.

DOI:10.1109/BHI.2017.7897311
PMID:28736772
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5521964/
Abstract

With the increasing use of digital technologies, online social networks are emerging as major means of communication. Recently, social networks such as Facebook and Twitter are also being used by consumers, care providers (physicians, hospitals), and government agencies to share health related information. The asymmetric user network and the short message size have made Twitter particularly popular for propagating health related content on the Web. Besides tweeting on their own, users can choose to particular tweets from other users (even if they do not follow them on Twitter.) Thus, a tweet can diffuse through the Twitter network via the follower-friend connections. In this paper, we report results of a pilot study we conducted to quantitatively assess how health related tweets diffuse in the directed follower-friend Twitter graph through the retweeting activity. Our effort includes (1). development of a retweet collection and Twitter retweet graph formation framework and (2). a preliminary analysis of retweet graphs and associated diffusion metrics for health tweets. Given the ambiguous nature (due to polysemy and sarcasm) of health relatedness of tweets collected with keyword based matches, our initial study is limited to ≈ 200 health related tweets (which were manually verified to be on health topics) each with at least 25 retweets. To our knowledge, this is first attempt to study health information diffusion on Twitter through retweet graph analysis.

摘要

随着数字技术的使用日益增加,在线社交网络正成为主要的交流方式。最近,诸如脸书和推特这样的社交网络也被消费者、医疗服务提供者(医生、医院)以及政府机构用于分享健康相关信息。非对称的用户网络和简短的信息规模使得推特在网络上传播健康相关内容方面特别受欢迎。除了自己发推文之外,用户还可以选择转发其他用户的特定推文(即使他们在推特上没有关注这些用户)。因此,一条推文可以通过关注者与好友的连接在推特网络中传播开来。在本文中,我们报告了一项试点研究的结果,该研究旨在通过转发活动定量评估与健康相关的推文如何在有向的关注者-好友推特图中传播。我们的工作包括:(1)开发一个转发收集和推特转发图形成框架;(2)对健康推文的转发图和相关传播指标进行初步分析。鉴于通过基于关键词匹配收集的推文在健康相关性方面具有模糊性(由于一词多义及讽刺意味),我们的初步研究仅限于大约200条与健康相关的推文(这些推文经人工核实确实是关于健康主题的),每条推文至少有25次转发。据我们所知,这是首次尝试通过转发图分析来研究推特上的健康信息传播。