Sympathy detection in disaster Twitter data

Yingjie Li, Cornelia Caragea, Seoyeon Park, Doina Caragea, Andrea Tapia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Nowadays, micro-blogging sites such as Twitter have become powerful tools for communicating with others in various situations. Especially in disaster events, these sites can be the best platforms for seeking or providing social support, of which informational support and emotional support are the most important types. Sympathy, a sub-type of emotional support, is an expression of one's compassion or sorrow for a difficult situation that another person is facing. Providing sympathy to people affected by a disaster can help change people's emotional states from negative to positive emotions, and hence, help them feel better. Moreover, detecting sympathy contents in Twitter can potentially be used for finding candidate donors since the emotion “sympathy” is closely related to people who may be willing to donate. Thus, in this paper, as a starting point, we focus on detecting sympathy-related tweets. We address this task using Convolutional Neural Networks (CNNs) with refined word embeddings. Specifically, we propose a refined word embedding technique in terms of various pre-trained word vector models and show great performance of CNNs that use these refined embeddings in the sympathy tweet classification task. We also report experimental results showing that the CNNs with the refined word embeddings outperform not only traditional machine learning techniques, such as Naïve Bayes, Support Vector Machines and AdaBoost with conventional feature sets as bags of words, but also Long Short-Term Memory Networks.

Original languageEnglish (US)
Title of host publicationISCRAM 2019 - Proceedings
Subtitle of host publication16th International Conference on Information Systems for Crisis Response and Management
EditorsZeno Franco, Jose J. Gonzalez, Jose H. Canos
PublisherInformation Systems for Crisis Response and Management, ISCRAM
Number of pages11
ISBN (Electronic)9788409104987
StatePublished - 2019
Event16th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2019 - Valencia, Spain
Duration: May 19 2019May 22 2019

Publication series

NameProceedings of the International ISCRAM Conference
ISSN (Electronic)2411-3387


Conference16th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2019

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Electrical and Electronic Engineering


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