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Socio-temporal trends in urban cultural subpopulations through social media

机译:社会媒体的社会颞态趋势

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Understanding when, where, and how increasingly diverse and dynamic subpopulations interact in urban environments is critical to the integrity of the city as a whole. This knowledge can facilitate the development of communication strategies, urban planning methodologies, and resource allocation to best serve citizens. Previous research focused either upon large temporal trends for the city as a whole, or mapped citizens in social or geographic space using broad categories (such as shared interests on social media or one's racial designation). Whereas these studies focused on a single geographic area, this paper includes Twitter data from three culturally distinct metropolitan areas over the same 92-day period: Los Angeles, CA, United States; Chicago, IL, United States; and Istanbul, Turkey. The global embrace of the Twitter social media platform provides the primary data source for a methodology applicable to any urban area. For Tweets emanating from within each city, a bag-of-words approach to the messages' textual content creates topical clusters within the most frequently occurring languages. These classifications transcend traditional racial designations. Time series analysis of each Tweet's timestamp reveal that the volume of tweets across topics is significantly correlated with major regional events. Furthermore, certain subpopulations' postings rise and fall in sync with others. Examining the trends among strongly correlated topic groups provides an indication of how these groups might interact.
机译:在城市环境中互动的何时,何时以及越来越多的多样化和动态群体对整个城市的完整性至关重要。本知识可以促进沟通策略,城市规划方法和资源分配的发展,以最佳服务。以前的研究在社会或地理空间中的整个城市的大型时间趋势,或使用广泛的类别(例如社交媒体或一个人的种族名称)的社会或地理空间中的映射公民。而这些研究专注于单个地理区域,而本文则包括来自同一92天的三个文化独特的大都市区的推特数据:洛杉矶,加州,美国;芝加哥,IL,美国;和伊斯坦布尔,土耳其。 Twitter社交媒体平台的全球拥有为适用于任何城市地区的方法提供主要数据源。对于从每个城市内部发出发出的推文,消息的袋式方法对消息的文本内容在最常发生的语言中创建主题集群。这些分类超越了传统的种族名称。每个推文的时间戳的时间序列分析表明,跨主题的推文数量与主要区域事件有显着相关。此外,某些群体的帖子与他人同步地升起并脱落。检查强烈相关主题组之间的趋势提供了这些群体如何互动的指示。

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