This paper studies the problem of extracting realworld event information from social media streams. Although existing work focuses on event signals of bursty mentions extracted from a single-source of textual streams, these signals are likely to be noisy due to ambiguous occurrences of individual mentions. To extract accurate event signals, we propose a framework capable of "grounding" mentions to unique event using multiple social networks with complementary strength. We show that our framework jointly using multiple sources outperforms state-ofthe-Arts using publicly available datasets.
|Title of host publication||Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016|
|Editors||Francesco Bonchi, Xindong Wu, Ricardo Baeza-Yates, Josep Domingo-Ferrer, Zhi-Hua Zhou|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2017 Jan 31|
|Event||16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain|
Duration: 2016 Dec 12 → 2016 Dec 15
|Name||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Other||16th IEEE International Conference on Data Mining, ICDM 2016|
|Period||16/12/12 → 16/12/15|
Bibliographical noteFunding Information:
This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)) and this research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2016-R2720-16-0007) supervised by the IITP(Institute for Information & communications Technology Promotion)
© 2016 IEEE.
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