Mining spatio-temporal information on microblogging streams using a density-based online clustering method

Authors:
Chung-Hong Lee
Affiliations:
Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 26
Cited 5

Learning in the presence of concept drift and hidden contexts

Machine Learning
Query clustering using user logs

ACM Transactions on Information Systems (TOIS)
Incremental Clustering for Mining in a Data Warehousing Environment

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Topic Extraction from News Archive Using TF*PDF Algorithm

WISE '02 Proceedings of the 3rd International Conference on Web Information Systems Engineering
An On-Line Document Clustering Method Based on Forgetting Factors

ECDL '01 Proceedings of the 5th European Conference on Research and Advanced Technology for Digital Libraries
Clustering Data Streams: Theory and Practice

IEEE Transactions on Knowledge and Data Engineering
A System for new event detection

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Giving Temporal Order to News Corpus

ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
2005 Special Issue: Efficient streaming text clustering

Neural Networks - 2005 Special issue: IJCNN 2005
A framework for clustering evolving data streams

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A Novelty-based Clustering Method for On-line Documents

World Wide Web
Incremental Document Clustering Based on Graph Model

ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
"OMG, from here, I can see the flames!": a use case of mining location based social networks to acquire spatio-temporal data on forest fires

Proceedings of the 2009 International Workshop on Location Based Social Networks
Integrating web-based intelligence retrieval and decision-making from the twitter trends knowledge base

Proceedings of the 2nd ACM workshop on Social web search and mining
TwitterStand: news in tweets

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams

Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Earthquake shakes Twitter users: real-time event detection by social sensors

Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
You are where you tweet: a content-based approach to geo-locating twitter users

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A spatio-temporal framework for related topic search in micro-blogging

AMT'10 Proceedings of the 6th international conference on Active media technology
Hip and trendy: Characterizing emerging trends on Twitter

Journal of the American Society for Information Science and Technology
Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Discovery of unusual regional social activities using geo-tagged microblogs

World Wide Web
BursT: a dynamic term weighting scheme for mining microblogging messages

ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Query-Guided Event Detection From News and Blog Streams

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

A weakly-supervised detection of entity central documents in a stream

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Exploiting online social data in ontology learning for event tracking and emergency response

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Leveraging microblogging big data with a modified density-based clustering approach for event awareness and topic ranking

Journal of Information Science
Learning to create an extensible event ontology model from social-media streams

ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Software architecture knowledge for intelligent light maintenance

Advances in Engineering Software

Quantified Score

Hi-index	12.05

Visualization

Abstract

Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users for picking up all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to investigate how microblogging social networks (i.e. Twitter) can be used as a reliable information source of emerging events by extracting their spatio-temporal features from the messages to enhance event awareness. In this work, we applied a density-based online clustering method for mining microblogging text streams, in order to obtain temporal and geospatial features of real-world events. By analyzing the events detected by our system, the temporal and spatial impacts of the emerging events can be estimated, for achieving the goals of situational awareness and risk management.