# Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks
## 2 Related Work
The system only used geo-tagged tweets and did not use text content of tweets for event detection. The authors in [26] proposed a two-phase streaming event detection algorithm in Twitter by utilizing Storm with Cassandra. First, the system applies a keyword-based algorithm for filtering, and then it conducts a clustering-based algorithm for event detection. The proposed hybrid method in [26] provides a better balance between accuracy and processing time cost. It focuses on real-time event detection and distributed processing by using keyword burstiness. For this reason, the method cannot provide local events. Firefly in [27] detects local news using given geographical area. In order to overcome the geographical data sparsity, it utilizes users' profile information and assumes the location of tweets by examining the profile of social friends. In Firefly, the local event detection is very naive. For example, it defines a threshold, which is the ratio of the number of retweets, to capture local events in the clusters. [PERSON] et al. [28] proposed a multiscale spatiotemporal real-time event detection approach by exploiting a quad-tree and Poisson variant to dynamically identify events across different spatial scales. detecting latest local events (DeLLe) [29], a methodology for automatically detecting the latest local events from geo-tagged tweets, detects local events and summarizes them by using a machine learning algorithm such as long short-term memory (LSTM). Similar to [28], it uses a map that is divided into grid cells for grouping events. It is difficult to use this for matching actual location information since it utilizes a quad-tree and grid indexing. Moreover, the method only used geo-tagged social posts of SNS such as Twitter and Flickr. [PERSON] et al. [30] proposed a real-time disaster detection and an alert system based on multimedia big data. The authors exploited an a priori algorithm to classify and track events. They also presented an influence diffusion model to spread detected disasters easily. However, the system needs well-defined geographic area information to detect and diffuse events. It focuses on the influence diffusion model rather than a local event detection method. [PERSON] et al. [31] proposed an online event detection scheme based on a game theoretical clustering. First of all, it is not a local event detection scheme that uses geographic information. However, the authors proposed a general-purpose event detection system that can be applied to various online social networks (OSNs), as well as Twitter. In addition, the system shows a vast amount of big data processing and demonstrates its feasibility by actively utilizing big data platforms such as Cassandra, Kafka, and Spark. Existing event detection schemes can be classified for various purposes. Real-time event detection schemes [17; 22; 23; 26; 28; 30; 31] assume situations where information that must be provided to users in real time is critical. General event detection schemes [6; 13] do not have a definition of events, but they are currently aimed at detecting various issues occurring online. Local event detection schemes [17; 22; 23; 24; 27; 28; 30] aim to detect events or issues occurring in a particular region in the real world. In certain studies, there are also schemes to detect only predefined events [17; 23]. The proposed scheme in our work is not intended for processing in real time, but it is possible to provide events in a specified time unit (1 h). Furthermore, it detects local events through relevant document analysis and a geographic dictionary and does not restrict the definition of events. There are three main schemes to detect events. First, there is a scheme to detect an event using the burstiness of a particular word through a language analysis of a document [6; 22]. It is not suitable for a local event detection scheme because it analyzes only words. The second is a clustering-based scheme that builds a graph according to word frequency and graph generation rules and detects events through clustering.