# Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks
###### Abstract  
In this paper, we propose a local event detection scheme by analyzing relevant documents in social networks to improve the accuracy of event detection. To detect local events by using geographical data, the proposed scheme embeds them using a geographical data dictionary and generates a weighted keyword graph using social network characteristics. The data left by users in social networks include not only postings but also related documents such as comments and threads. In this way, the proposed scheme detects a local event based on a keyword graph that is constructed through the analysis of the relevant documents. This can improve the accuracy of local event detection by analyzing relevant documents embedded with region-related information using a geographical data dictionary, without requiring users to tag geographic data. In order to verify the superiority of the proposed scheme, we compare it with the existing event detection schemes through various performance evaluations. social network service; event detection; relevant documents; keyword graph +
Footnote †: journal:  
Text-based event detection schemes have been proposed for considering the importance and frequency of keywords by analyzing and extracting meaningful words on hashtags, contents, and comments [5; 6; 7; 8; 9; 10; 11; 12]. Text-based event detection schemes utilize term frequency-inverse document frequency (TF-IDF) to extract keywords [5]. Recently, some studies utilized machine learning algorithms such as Word2 Vec [9]. Graph-based event detection schemes, which involve making a graph from extracted keywords and conducting clustering algorithms to find events, have also been proposed [13; 14; 15; 16]. The authors in [13] generated a graph by adapting vector space models; the graph can detect events from clusters that are built based on weights assigned to the graph considering the frequency of co-occurrences. Recently, local event detection schemes using geo-tags in SNSs have been studied [17; 18; 19]. [PERSON] et al. [17] proposed a local event detection scheme that uses time-stamps and geographical information included in the posts. [PERSON] et al. [18] proposed a geo-tag based local event detection scheme. However, since the proposed scheme in [17] performs text-based event detection, there is a problem when little information is obtained in the event of a short posting and hence less noise in the process of extracting keywords related to local events, resulting in poor accuracy. In addition, since the actual majority of social network data do not have geo-tagging, the event detection scheme using only the geo-tagging approach proposed in [8] has a problem with poor accuracy. In this paper, we propose a local event detection scheme by analyzing relevant documents in social networks. The proposed scheme creates a keyword graph by extracting keywords in the social network data. After that, we assign weights to vertices and edges of the keyword graph by considering social network characteristics. We can easily understand the relationships between an event and keywords by using the keyword graph. In order to detect local events by using geographical information, the proposed scheme utilizes a geographical dictionary and geo-tag information to classify the geographical keywords from the keyword graph. The main contributions of this paper are as follows:  
1. The proposed scheme uses a geographical dictionary to solve the limitations of the existing schemes that provide and detect local events, considering that geo-tags appear very sparsely with posts. The geographical dictionary refers to a database that consists of location data mapped to a noun that represents a particular region or area. 2. The proposed scheme analyzes the related documents such as comments and threads to improve the accuracy of the event detection scheme. After the analysis, we apply a clustering algorithm to the keyword graph according to weight values. 3.