ML Anthology

Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract

Jie Yin, Sarvnaz Karimi, Andrew Lampert, Mark A. Cameron, Bella Robinson, Robert Power
IJCAI 2015 pp. 4234-4239
/ijcai/2015/yin2015ijcai-using/

Abstract

Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.

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Cite

Text

Yin et al. "Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract." International Joint Conference on Artificial Intelligence, 2015.

Markdown

[Yin et al. "Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/yin2015ijcai-using/)

BibTeX

@inproceedings{yin2015ijcai-using,
  title     = {{Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract}},
  author    = {Yin, Jie and Karimi, Sarvnaz and Lampert, Andrew and Cameron, Mark A. and Robinson, Bella and Power, Robert},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {4234-4239},
  url       = {https://mlanthology.org/ijcai/2015/yin2015ijcai-using/}
}