Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Capturing term dependencies using a language model based on sentence trees
Proceedings of the eleventh international conference on Information and knowledge management
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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 cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Giving Temporal Order to News Corpus
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Using collocations for topic segmentation and link detection
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Semantic language models for topic detection and tracking
NAACLstudent '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop - Volume 3
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ACM SIGMOD Record
2005 Special Issue: Efficient streaming text clustering
Neural Networks - 2005 Special issue: IJCNN 2005
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Representing documents with named entities for story link detection (SLD)
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Dynamic stopwording for story link detection
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Relevance models for topic detection and tracking
HLT '02 Proceedings of the second international conference on Human Language Technology Research
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Proceedings of the 17th international conference on World Wide Web
Temporal and information flow based event detection from social text streams
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Discovering event evolution graphs from news corpora
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
Story link detection based on event model with uneven SVM
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Two-tier similarity model for story link detection
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Social media analytics: tracking, modeling and predicting the flow of information through networks
Proceedings of the 20th international conference companion on World wide web
Following the social media: aspect evolution of online discussion
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Story link detection based on event words
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Hip and trendy: Characterizing emerging trends on Twitter
Journal of the American Society for Information Science and Technology
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
Analyzing the dynamic evolution of hashtags on Twitter: a language-based approach
LSM '11 Proceedings of the Workshop on Languages in Social Media
A Novel Approach for Event Detection by Mining Spatio-temporal Information on Microblogs
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Detecting malicious tweets in trending topics using a statistical analysis of language
Expert Systems with Applications: An International Journal
Identifying interesting Twitter contents using topical analysis
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Due to the explosive growth of social-media applications, enhancing event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past events for tackling emerging real-world events, in this work we utilize the social-media messages to characterize real-world events through mining their contents and extracting essential features for relatedness analysis. On one hand, we established an online clustering approach on Twitter microblogs for detecting emerging events, and meanwhile we performed event relatedness evaluation using an unsupervised clustering approach. On the other hand, we developed a supervised learning model to create extensible measure metrics for offline evaluation of event relatedness. By means of supervised learning, our developed measure metrics are able to compute relatedness of various historical events, allowing the event impacts on specified domains to be quantitatively measured for event comparison. By combining the strengths of both methods, the experimental results showed that the combined framework in our system is sensible for discovering more unknown knowledge about event impacts and enhancing event awareness.