Abstract
Recent years have witnessed the prevalence and use of social media during crises, such as Twitter, which has been becoming a valuable information source for offering better responses to crisis and emergency situations by the authorities. However, the sheer amount of information of tweets can’t be directly used. In such context, distinguishing the most important and informative tweets is crucial to enhance emergency situation awareness. In this paper, we design a convolutional neural network based model to automatically detect crisis-related tweets. We explore the twitter-specific linguistic, sentimental and emotional analysis along with statistical topic modeling to identify a set of quality features. We then incorporate them to into a convolutional neural network model to identify crisis-related tweets. Experiments on real-world Twitter dataset demonstrate the effectiveness of our proposed model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Muhammad, I., et al.: AIDR: Artificial intelligence for disaster response. In: Proceedings of the 23rd International Conference on World Wide Web. ACM (2014)
Takeshi, S., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World wide web. ACM (2010)
Jie, Y., et al.: Using social media to enhance emergency situation awareness. IEEE Intell. Syst. 27(6), 52–59 (2012). APA
Alexandra, O., et al.: CrisisLex: a lexicon for collecting and filtering microblogged communications in crises. In: ICWSM (2014)
Ethan, F., Chen, B., Bernstein, M.S.: Empath: understanding topic signals in large-scale text. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM (2016)
Niharika, S., Kumaraguru, P.: Call for service: character- izing and modeling police response to serviceable requests on Facebook. In: CSCW (2017)
Muhammad, I., et al.: Extracting information nuggets from disaster-related messages in social media. In: ISCRAM (2013)
Muhammad, I., et al.: Practical extraction of disaster-relevant information from social media. In: Proceedings of the 22nd International Conference on World Wide Web. ACM (2013)
Sudha, V., et al.: Natural language processing to the rescue? extracting “situational awareness” tweets during mass emergency. In: ICWSM (2011)
Alex, K., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Souvik, K., et al.: Joint acoustic factor learning for robust deep neural network based automatic speech recognition. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2016)
Tingmin, W., et al.: Twitter spam detection based on deep learning. In: Proceedings of the Australasian Computer Science Week Multiconference. ACM (2017)
Duyu, T., et al.: Coooolll: a deep learning system for twitter sentiment classification. In: SemEval@ COLING (2014)
Tianqi, C., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM sigkdd International Conference on Knowledge Discovery and Data Mining. ACM (2016)
Muhammad, I., Castillo, C.: Towards a data-driven approach to identify crisis-related topics in social media streams. In: Proceedings of the 24th International Conference on World Wide Web. ACM (2015)
Kevin, S., et al.: Identifying and categorizing disaster-related tweets. In: Conference on Empirical Methods in Natural Language Processing (2016)
Muhammad, I., et al.: Coordinating human and machine intelligence to classify microblog communications in crises. In: ISCRAM (2014)
Abdulfatai, P., et al.: Information verification during natural disasters. In: Proceedings of the 22nd International Conference on World Wide Web. ACM (2013)
Zahra, A., et al.: Tweedr: mining twitter to inform disaster response. In: ISCRAM (2014)
Alan M, M., et al.: Geo-twitter analytics: Applications in crisis management. In; 25th International Cartographic Conference (2011)
Flvio EA, H., et al.: Bridging the gap between decision-making and emerging big data sources: an application of a model-based framework to disaster management in Brazil. Decis. Support Syst. 97, 12–22 (2017)
Matthew S, G.: Predicting crime using Twitter and kernel density estimation. Decis. Support Syst. 61, 115–125 (2014)
Thapen, N., Simmie, D., Hankin, C.: The early bird catches the term: combining twitter and news data for event detection and situational awareness. J. Biomed. Semant. 7(1), 61 (2016)
Kar Wai, L., Buntine, W.: Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM (2014)
Fred, M., et al.: Understanding twitter data with tweetxplorer. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2013)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ning, X., Yao, L., Wang, X., Benatallah, B. (2017). Calling for Response: Automatically Distinguishing Situation-Aware Tweets During Crises. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-69179-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69178-7
Online ISBN: 978-3-319-69179-4
eBook Packages: Computer ScienceComputer Science (R0)