CASM: A DEEP-LEARNING APPROACH FOR IDENTIFYING COLLECTIVE ACTION EVENTS WITH TEXT AND IMAGE DATA FROM SOCIAL MEDIA
CASM: A DEEP-LEARNING APPROACH FOR IDENTIFYING COLLECTIVE ACTION EVENTS WITH TEXT AND IMAGE DATA FROM SOCIAL MEDIA
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DOI：
10.1177/0081175019860244
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发表时间：
2019-01-01
期刊：
影响因子：
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通讯作者：
Pan, Jennifer
中科院分区：
文献类型：
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作者：
Zhang, Han;Pan, Jennifer
抗议事件分析是研究集体行动和社会运动的重要方法，通常以传统媒体报道为数据源。我们介绍了来自社交媒体的集体行动（CASM）-一种系统，该系统在两阶段分类器中使用图像数据上的卷积神经网络和具有文本数据长短期记忆的递归神经网络来识别关于离线集体行动的社交媒体帖子。我们在中国社交媒体数据上实施了CASM，并识别了2010年至2017年的10万多个集体行动事件（CASM-China）。我们通过交叉验证，样本外验证，并与其他抗议数据集的比较来评估CASM的性能。我们评估了网络审查的影响，发现它并没有实质性地限制我们对事件的识别。与其他抗议数据集相比，CASM-中国确定了相对更多的农村，与土地有关的抗议和相对较少的集体行动事件与种族和宗教冲突。
Protest event analysis is an important method for the study of collective action and social movements and typically draws on traditional media reports as the data source. We introduce collective action from social media (CASM)-a system that uses convolutional neural networks on image data and recurrent neural networks with long short-term memory on text data in a two-stage classifier to identify social media posts about offline collective action. We implement CASM on Chinese social media data and identify more than 100,000 collective action events from 2010 to 2017 (CASM-China). We evaluate the performance of CASM through cross-validation, out-of-sample validation, and comparisons with other protest data sets. We assess the effect of online censorship and find it does not substantially limit our identification of events. Compared to other protest data sets, CASM-China identifies relatively more rural, land-related protests and relatively few collective action events related to ethnic and religious conflict.