## 1 Introduction  
Since social media-based data or online media data is composed of natural language, it has a much larger and more complex structure than existing transaction data [1; 2]. Recently, the media distributes news articles online in order to quickly deliver news to consumers, online news articles can identify current social trends and behavioral patterns of members of society [3]. The social trend analysis technology for content published in online media has the advantage of being less expensive and faster than the analysis by existing expert groups. Therefore, research to detect and monitor current major issues by analyzing unstructured text information from social media or online news posts and extracting useful knowledge is being actively conducted. For social trend analysis, it is important to identify event sentences from text documents such as social media or online news articles [4]. The event sentence refers to a sentence in which specific content about a specific topic, i.e., who, where, when, what, etc. is expressed. The temporal and spatial information included in news articles is used to detect the early onset of disease and to determine the time and location of disease outbreaks [5]. The temporal and spatial information presented in online news articles plays a decisively important role in understanding social trends. Existing research to detect spatial and temporal information from text focuses on how accurately all temporal and spatial information contained within a document is extracted [6; 7; 8]. A document can contain many pieces of information about time and space. In this study, among various spatial and temporal information included in a document, temporal and spatial information describing the core topic of the document is defined as _'representative spatio-temporal information'_. The document including representative spatio-temporal information is defined as a _'representative spatiotemporal document'_. If not only representative spatio-temporal information but also a large number of general spatio-temporal information are extracted from one document, the accuracy of core event analysis based on spatio-temporal information can be lowered. In order to increase the accuracy of core event analysis through artificial intelligence, it is necessary to remove unnecessary spatio-temporal information from one document and extract only the representative spatio-temporal information that accurately describes the core event in the document. Since extracting representative spatio-temporal information from a single document is a high-cost task, it is difficult to treat all documents from big data such as social media-based data or online news articles as analysis targets. Therefore, in order to efficiently analyze core events through representative spatio-temporal information, it is important to select documents from which representative spatio-temporal information is extracted. Research using machine learning (Naive Bayes [9; 10], SVM [11; 12] and Random Forrest [13; 14], etc.) in automatic document classification problems have been conducted so far. Recently, as deep learning-based Convolution Neuron Network (CNN) has been used for document classification, the performance of automatic document classification has been greatly improved [15]. CNN started to attract attention in the field of artificial intelligence as it showed excellent performance in image classification or object detection in the early days [16; 17; 18]. Classification technology using CNN has expanded its field of application from images to texts [19]. Recently, document classification using CNN is characterized as an area that classifies documents (patent documents [20], contracts [21], infectious disease documents [22], etc.) of a specific domain. In this paper, we propose a character-level CNN-based representative spatio-temporal document classification model. First, we built 7400 learning data from online news articles provided by the National Institute of the Korean Language [23].