标题： Twitter话题网络中的多模态事件检测 显示英文标题

标题： Multimodal Event Detection in Twitter Hashtag Networks

摘要： 事件检测在多模态Twitter数据集中被认为是有价值的。 我们将数据集中的标签视为具有两种模式的实例：文本和地理特征。 文本特征由词袋表示组成。 地理特征由推文的地理标签（即地理位置坐标）组成。 我们融合多模态数据，旨在根据主题和地理位置检测有趣的事件及其相关标签。 为此，假设了一个生成潜在变量模型，并推导出广义期望最大化(EM)算法来学习模型参数。 所提出的方法计算效率高，适用于大数据集。 2014年8月的Twitter数据集上的实验结果表明了所提出方法的有效性。 显示英文摘要

摘要： Event detection in a multimodal Twitter dataset is considered. We treat the hashtags in the dataset as instances with two modes: text and geolocation features. The text feature consists of a bag-of-words representation. The geolocation feature consists of geotags (i.e., geographical coordinates) of the tweets. Fusing the multimodal data we aim to detect, in terms of topic and geolocation, the interesting events and the associated hashtags. To this end, a generative latent variable model is assumed, and a generalized expectation-maximization (EM) algorithm is derived to learn the model parameters. The proposed method is computationally efficient, and lends itself to big datasets. Experimental results on a Twitter dataset from August 2014 show the efficacy of the proposed method.