# Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks
## 3 The Proposed Local Event Detection Scheme  
### System Architecture  
The existing local event detection schemes use text mining techniques and geo-tags. However, as mentioned above, geo-tag information appears in tweets very sparsely, and thus it is hard to extract geographical information from tweets using text mining techniques. Therefore, the scheme needs to be complemented with additional geographical data. In or der to improve local event detection accuracy, we also analyze relevant documents that are associated with previous postings. In this paper, we propose a local event detection scheme by analyzing relevant documents to solve the problems of the existing schemes such as the geo-tag and text mining based schemes. We embed geographical information to the detected events by using text mining techniques and a geographical dictionary if the events do not have geo-tags. In order to complement sparse geographical data, we exploit relevant documents that have geo-tags or geographical words. A relevant document means a comment or a thread in SNSs. In Twitter, the message known as a tweet is originally restricted to 140 characters. Sometimes, users need more than one tweet to express their opinions or thoughts. In order to overcome the character restriction of the tweet, Twitter provides a function called a thread, which is a series of connected tweets from one person. It can be updated and extended consistently. Relevant documents can provide geographical information that does not appear in the previous postings. Figure 1 shows an overall architecture of the proposed local event detection scheme. The proposed scheme consists of four modules: data collection, graph modeling, relevant analysis, and local event detection. Between each module, the collected SNS data and graph data generated by the proposed scheme are transmitted. The green dotted line represents the transmission of the SNS data, while the red solid line represents the graph data. The data collection module collects and stores SNS data. It removes stop words and extracts nouns from the collected data. After the data collection, we perform a local event detection algorithm in sequence through three phases, namely graph modeling, relevant analysis, and local event detection. The first phase, the graph modeling module, constructs keyword graphs through the preprocessed data and the preconstructed geographical dictionary in the data collection module. The vertex in the keyword graph represents an extracted keyword, and the edge in the keyword graph represents co-occurrence information. We use geo-tags as much as possible, and if users do not provide geo-tags, we extract regional keywords through text mining algorithms and a geographical dictionary. After that, the graph modeling module assigns keyword type (keyword, regional keyword) to each vertex of the keyword graph. The graph modeling module allocates weights to vertices and edges to calculate vertex scores by using constructed keyword graphs. Finally, the key node selection is performed by utilizing calculated vertex scores. In the second phase, the relevant analysis module updates the keyword graph by analyzing relevant documents such as comments and threads that occur within a particular time window, which we set as 1 h. The relevant documents are continuously collected from the data collection module. The data collection module also removes stop words from the collected relevant documents. The relevant analysis module extracts geographical information from preprocessed relevant documents, and then it changes the keyword graph constructed from the graph modeling module by using extracted geographical information. In the third phase, the local event detection module detects local events through the clustering algorithm and the measurement of network modularity on an hourly basis. The proposed scheme targets only Korean documents. However, this idea can be applied to any language by properly tuning the geographical dictionary and lexical analyzer to suit each language.