## 4 A Twitter-Based Event Detection Method for Monitoring Emergency Events
### A Twitter-Based Real-Time Event Detection Method  
In this work, we developed a Twitter-based real-time event detection method for monitoring emergent events. Using our previously developed event detection algorithm [62; 63] and collected Twitter messages for more than 10 years, we built a tweet corpus to perform the tasks in this research work. It contains datasets of original tweet information and a collection of tweets classified based on detected events. We used an online text-stream clustering algorithm and adjustable sliding window method developed to group the data to find out the social information of major international events. In the following subsection, we will briefly introduce our developed Twitter-based event detection method. Event Modelling Using Twitter Text Streams  
In brief, for detecting real-time events by Twitter-data streams, we made use of a density-based clustering method based on burst detection, spatio-temporal information, and textual content. Along with our developed algorithm and data model [62; 63], we continuously collected new data through Twitter StreamAPI for carrying out the event detection task. After filtering out non-ASCII tweets, the selected tweets had been utilized as our data source. Subsequently, we partitioned messages into unigrams, and all capital letters in each tweet were converted into lowercase for our experiments. Bursts of topic discussion in Twitter are indictors of for periods with an increased amount of posts and attention from the Twitter users around the world. Since bursts usually occur at the same time as events in the real world, burst detection is an effective method for grouping tweets related to events. In order to detect bursts, we applied a developed burst detection algorithm on the tweets posted by Twitter users. The algorithm employed a sliding  
Figure 4: Incorporating the Ethereum blockchain into the supply chain system. window for which the number of tweets is counted. We will describe the main concept of the algorithm later, and the detailed description can be found in [62; 63]. The purpose of the developed event detection system model is to detect the occurrence and situational change of an event and to understand how it evolves. We developed an algorithmic method that can help users efficiently extract key event features for quick response to emergent events. In order to extract the key features (timeline, geographical center, cluster energy) and the main concept of an emergent event, a modelling process is applied to normalize the clustered data sets with a dynamic weighting function. The clustered message stream is manipulated as multiple threads to trace all events happening in the same period. The modelling process can extract a real-time event in the early stage by monitoring the evolvement of event energy in each thread. Due to the size of a continuous data stream is not limited, and storage and computing capabilities are also limited, the stream is usually processed within a limited data collection or time frame. The length of the time window and constrains the number of clusters within a finite size were determined by the developed system. Meanwhile, to extract the content of the stream in an efficient way, it is often necessary to aggregate real-time data streams together to obtain acceptable performance in a single pass. We implemented a weighting scheme and mining processes to deal with such issues. We first compare the word distribution between the long-period and short-period collections of social-media streams and then formulate its bursting factor to weigh online messages for real-time stream mining. In particular, appropriate pre-processing procedures should be developed to indicate the strength of incoming messages. Then, the streams can be gathered in near real-time with the advantages of efficiency and accuracy. Although some Twitter message provides valuable information, most messages are meaningless.