Detecting tension in online communities with computational Twitter analysis
Detecting tension in online communities with computational Twitter analysis
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DOI：
10.1016/j.techfore.2013.04.013
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发表时间：
2015-06-01
影响因子：
12
通讯作者：
Sloan, Luke
中科院分区：
文献类型：
--
作者：
Burnap, Pete;Rana, Omer F.;Sloan, Luke
越来越多的人使用社交媒体与他人交流并记录他们的个人观点和行为，这正在产生大量数据流，为社会科学家提供了进行在线形式研究的机会，从而深入了解在线社会形态。本文研究了通过社交媒体预测社会紧张局势高峰的可能性——英国警察部门将其定义为“任何倾向于表明个人或群体之间的正常关系已严重恶化的事件”。使用从 Twitter 收集的人类编码数据样本，尝试了多种不同的计算方法来检测紧张局势的峰值，这些数据与英超联赛足球比赛期间的种族虐待指控有关。结合句法和基于词典的文本挖掘规则的会话分析；情绪分析；机器学习方法作为一种可能的方法进行了测试。结果表明，对话分析方法和文本挖掘的结合在对单个推文的紧张程度进行分类方面优于许多机器学习方法和情绪分析工具。 (C) 2013 Elsevier Inc. 保留所有权利。
The growing number of people using social media to communicate with others and document their personal opinion and action is creating a significant stream of data that provides the opportunity for social scientists to conduct online forms of research, providing an insight into online social formations. This paper investigates the possibility of forecasting spikes in social tension - defined by the UK police service as "any incident that would tend to show that the normal relationship between individuals or groups has seriously deteriorated" - through social media. A number of different computational methods were trialed to detect spikes in tension using a human coded sample of data collected from Twitter, relating to an accusation of racial abuse during a Premier League football match. Conversation analysis combined with syntactic and lexicon-based text mining rules; sentiment analysis; and machine learning methods was tested as a possible approach. Results indicate that a combination of conversation analysis methods and text mining outperforms a number of machine learning approaches and a sentiment analysis tool at classifying tension levels in individual tweets. (C) 2013 Elsevier Inc. All rights reserved.