Expert Systems with Applications: An International Journal
Learning to explore spatio-temporal impacts for event evaluation on social media
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
A probability based subnet selection method for hot event detection in Sina Weibo microblogging
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Landmark-based user location inference in social media
Proceedings of the first ACM conference on Online social networks
Event identification in web social media through named entity recognition and topic modeling
Data & Knowledge Engineering
Social Media Business Intelligence: A Pharmaceutical Domain Analysis Study
International Journal of Sociotechnology and Knowledge Development
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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 pickup all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to enhance the understanding on how social networks can be used as a reliable source of spatio-temporal information, by analyzing the temporal and spatial dynamics of Twitter activity. In this work, we developed several algorithms for mining microblogging text stream to obtain real-time and geospatial event information. The goal of our approach is to effectively detecting and grouping emerging topics by making use of real-time messages and geolocation data provided by social network services.