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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.

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。

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