# Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks
## 2 Related Work  
Recently, with an activation of the social network services, local event detection schemes have been actively studied through social network service data. In some studies, extracting keywords from posts is utilized for event detection. The authors in [6] proposed not only detecting events but also predicting events in the near future through a time analysis by using words related to the detected events. It utilizes the Jacard similarity coefficient to discover event-related words. In order to reduce the noise generated by a homomorphic word that is the same form of another word but both have different meanings, the context of the text containing the word is analyzed by tying the words together in pairs. For example, the word \"strike\" can have various meanings depending on the words that comes along with it. When \"strike\" occurs with \"baseball\", it has a meaning associated with the rules of baseball, and when it is associated with \"lightning\", it has a meaning associated with weather. Many posts in the SNSs have an informal style, buzzwords, special characters, and emoticons. Therefore, a text-based event detection always has noise problems due to these characteristics. The authors in [13] proposed an event detection scheme by constructing directed keyword graphs (such as EventGraph) that are created by SNS data to efficiently analyze the correlation of each word. A node in the graph represents a particular word, and an edge in the graph represents a relationship between words with a weight that is determined by the number of co-occurrences. It only applies users' posts to detect local events. Therefore, the accuracy of the local event detection is low since it is hard to determine a particular region by using users' posts only. Most events are related to the particular region as well as time. Most SNSs provide a geo-tagging function to users so that they can express their location to other users. The authors in [17] proposed a local event detection scheme by utilizing the geo-tagging function provided by SNSs. Using a map interface, the map space is divided into squares of equal size and defined as tiles. Each tile has time-spatial information that is extracted from tweets (i.e., posted messages) of Twitter. The authors in [17] used the spatio-temporally exclusive topic discovery based on nonnegative matrix factorization (STExNMF) method [20] to derive keywords and to conduct visualizations for detected events. Unfortunately, only about 1% of all tweets have geo-tag information [21]. Consequently, geo-tag based local event detection schemes have a low accuracy problem since they only consider 1% data of all contents on SNSs. The Jasmine system in [22] can detect local events in the real world using geolocation information from microblog documents. The key term extractor in Jasmine extracts key terms that appear three times or more in Twitter documents. However, it removes all retweets (i.e., tweets reposted by other users) in advance to reduce some noise. The related documents such as comments and threads are very meaningful for detecting a location or for describing an event in our scheme. GeoBurst+ in [23] detects real-time local events from geo-tagged tweet streams. The authors proposed a pivot seeking algorithm to generate candidate events. They also proposed a ranking module to classify routine events and local events by using temporal and spatial burstiness with TF-IDF weight. However, as mentioned in the study for Jasmine, only 0.7% of Twitter documents are geo-tagged, which is far too few for extracting location information. Eyewitness in [24] was proposed for automatically extracting and summarizing reports of local events from Twitter feeds. The authors presented a local event detection method by using a regression analysis on the time series of tweets. Eyewitness used a summarization algorithm for summarizing a detected event, such as the SumBasic algorithm [25]. It eliminates retweets and repeated documents, similar to Jasmine.