학술논문
Social Media based Real-time Event Detection by using Deep Learning Methods
이용수 24
- 영문명
- 발행기관
- 한국스마트미디어학회
- 저자명
- Van Quan Nguyen Hyung-Jeong Yang Young-chul Kim Soo-hyung Kim Kyungbaek Kim
- 간행물 정보
- 『스마트미디어저널』Vol6, No.3, 41~48쪽, 전체 8쪽
- 주제분류
- 공학 > 컴퓨터학
- 파일형태
- 발행일자
- 2017.09.30
국문 초록
영문 초록
Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data.
As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice.
Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.
목차
I. Introduction
II. Related work
III. Proposed Method
IV. Experimental Results
V. Conclusions
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