Catching crime: Detection of public safety incidents using social media
Catching crime: Detection of public safety incidents using social media
复制标题
抓捕犯罪：利用社交媒体发现公共安全事件
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Pelonomi Moiloa
中科院分区：
文献类型：
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作者：
Vukosi Marivate;Pelonomi Moiloa
社交媒体平台使用的日益普及带来了新用户生成的公共数据的爆炸式增长。这些数据围绕着许多不同的主题。感兴趣的一个主题是如何利用与公共安全和犯罪相关的用户生成的数据，以更好地了解犯罪事件发生的模式。这类数据的一个挑战是，大多数数据需要人工注释才能被机器分析。本文探讨了从社交媒体数据中提取的不同特征如何影响不同分类器的性能。分类器的构建是为了将社交媒体数据分类为与报告的犯罪有关或无关。讨论了少数标记数据的挑战，以及从文本数据中提取特征的不同方法，并探讨了用户相互交互创建的图形。
The increasing prevalence of Social Media platform use has brought with it an explosion of new user generated public data. This data is centered around many, diverse topics. One theme of interest is how one can tap into the public safety and crime related user generated data to better understand patterns in the occurrence of crime incidents. One challenge in such data is that most of the data needs human annotation to make it usable by machines to analyse. This paper explores how different features, extracted from social media data, impact the performance of different classifiers. The classifiers are built to classify social media data as having to do with a reported crime or not. The challenge of few labelled data is discussed as well as different approaches to extracting features from the text data as well as the graph created by users interacting with each other is explored.