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首页> 外文期刊>Information Communication & Society >HOW LONG IS A TWEET? MAPPING DYNAMIC CONVERSATION NETWORKS ON TWITTER USING GAWK AND GEPHI
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HOW LONG IS A TWEET? MAPPING DYNAMIC CONVERSATION NETWORKS ON TWITTER USING GAWK AND GEPHI

机译：推文有多长？利用GAWK和GEPHI在Twitter上映射动态对话网络

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摘要

Twitter is now well established as the world's second most important social media platform, after Faccbook. Its 140-character updates are designed for brief messaging, and its network structures are kept relatively flat and simple: messages from users are either public and risible to all (even to unregistered visitors using the Twitter website), or private and visible only to approved followers' of the sender; there are no more complex definitions of degrees of connection (family, friends, friends of Jriends) as they are available in other social networks. Over time, Twitter users have developed simple, but effective mechanisms for working around these limitations: '#hashtags', which enable the manual or automatic collation of all tweets containing the same #-hashtag, as well allowing users to subscribe to content feeds that contain only those tweets which feature specific #hashtags; and '@replies', which allow senders to direct public messages even to users whom they do not already follow. This paper documents a methodology for extracting public Twitter activity data around specific #hashtags, and for processing these data in order to analyse and visualize the @ieply networks existing between participating users — both overall, as a static network, and over time, to highlight the dynamic structure of @reply conversations. Such visualizations enable us to highlight the shifting roles played by individual participants, as well as the response of the overall #hashtag community to new stimuli-such as the entry of new participants or the availability of new information. Over longer timeframes, it is also possible to identify different phases in the overall discussion, or the formation of distinct clusters of preferentially interacting participants.
机译：推特现已成为继Faccbook之后的全球第二重要的社交媒体平台。它的140个字符的更新旨在用于简短的消息传递，并且其网络结构保持相对平坦和简单：来自用户的消息是公开的并且对所有人（甚至是使用Twitter网站的未注册访问者）都具有责任感，或者是私有的并且仅对批准的用户可见发送者的关注者；由于没有其他社交网络可用，因此对连接度（家庭，朋友，Jriends的朋友）的定义不再复杂。随着时间的推移，Twitter用户已经开发出了简单而有效的机制来解决这些限制：'#hashtags'，它可以对包含相同＃-hashtag的所有tweet进行手动或自动整理，并允许用户订阅具有以下内容的内容供稿：只包含带有特定＃标签的推文；和“ @replies”，这使发件人甚至可以将公开消息定向到尚未关注的用户。本文介绍了一种方法，该方法用于提取特定#hashtag周围的公共Twitter活动数据，并处理这些数据，以便分析和可视化参与用户之间存在的@ieply网络-总体而言，作为静态网络，随着时间的推移，以突出显示@reply对话的动态结构。这种可视化使我们能够突出显示各个参与者所扮演的角色的转变，以及整个#hashtag社区对新刺激的反应，例如新参与者的进入或新信息的可用性。在更长的时间范围内，也有可能在整个讨论中确定不同的阶段，或者确定优先互动参与者的不同集群的形成。

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