Twitter in academic events: A study of temporal usage, communication, sentimental and topical patterns in 16 Computer Science conferences
Twitter in academic events: A study of temporal usage, communication, sentimental and topical patterns in 16 Computer Science conferences
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DOI：
10.1016/j.comcom.2015.07.001
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发表时间：
2016-01-01
影响因子：
6
通讯作者：
Lin, Yu-Ru
中科院分区：
文献类型：
--
作者：
Parra, Denis;Trattner, Christoph;Lin, Yu-Ru
Twitter 通常被称为会议的秘密渠道。虽然主会议在实体环境中举行，但现场和场外的与会者可以通过 Twitter 上的微博进行社交、介绍新想法或传播信息。在本文中，我们分析了五年时间内学者们在 16 个计算机科学会议上的 Twitter 使用情况。我们的主要发现是，多年来 Twitter 的使用存在差异，信息活动（转发和 URL）增加，对话使用（回复和提及）减少，这也影响了网络结构，即信息和对话网络的连接组件的数量。我们还在推文内容上应用了主题建模，发现当根据主题对会议进行聚类时，生成的树状图清楚地揭示了这些事件的实际研究兴趣的相似性和差异。此外，我们还分析了推文的情绪，发现会议之间持续存在差异。它还表明，一些社区始终以较高的情绪表达信息，而另一些社区则以更加中立的方式表达信息。最后，我们研究了一些可以帮助预测未来用户参与在线 Twitter 会议活动的特征。通过将问题转化为分类任务，我们创建了一个模型，该模型可以识别有助于用户持续参与的因素。我们的结果对研究界实施成员持续积极参与的策略具有影响。此外，我们的工作揭示了使用 Twitter 上共享信息的潜力，通过为相关但往往鲜为人知的研究社区的新资源或研究人员提供可见性，促进研究社区之间的沟通与合作。 (C) 2015 Elsevier B.V. 保留所有权利。
Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, on-site and off-site attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze scholars' Twitter usage in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are differences with respect to the uses of Twitter, with an increase of informational activity (retweets and URLs), and a decrease of conversational usage (replies and mentions), which also impacts the network structure meaning the amount of connected components of the informational and conversational networks. We also applied topic modeling over the tweets' content and found that when clustering conferences according to their topics the resulting dendrogram clearly reveals the similarities and differences of the actual research interests of those events. Furthermore, we also analyzed the sentiment of tweets and found persistent differences among conferences. It also shows that some communities consistently express messages with higher levels of emotions while others do it in a more neutral manner. Finally, we investigated some features that can help predict future user participation in the online Twitter conference activity. By casting the problem as a classification task, we created a model that identifies factors that contribute to the continuing user participation. Our results have implications for research communities to implement strategies for continuous and active participation among members. Moreover, our work reveals the potential for the use of information shared on Twitter in order to facilitate communication and cooperation among research communities, by providing visibility to new resources or researchers from relevant but often little known research communities. (C) 2015 Elsevier B.V. All rights reserved.