LEDS

Abstract

Twitter is one of the most popular social media platforms where people can share their opinions, thoughts, interests, and whereabouts. In this work, we propose a Local Event Discovery and Summarization (LEDS) framework to detect local events from Twitter. Many existing algorithms for event detection focus on larger-scale events and are not sensitive to smaller-scale local events. Most of the local events detected by these methods are major events such as important sports, shows, or large natural disasters. In this paper, we propose the LEDS framework to detect both larger and smaller events. LEDS contains three key steps: 1) Detecting possible event related terms by monitoring abnormal distribution in different locations and times; 2) Clustering tweets based on their key terms, time, and location distribution; and 3) Extracting descriptions including time, location, and key sentences of local events from clusters. The framework is evaluated on a real world Twitter dataset with more than 60 million tweets. The results show that compared with previous work, LEDS can detect smaller-scale and greater variety of local events. More than 43 percent of detected local events do not have an official organizer, cannot be seen on news media, and only attract the attention from a small group of people.