Datasets for Social, Natural & Humanitarian Crisis Informatics Research
(For questions, you can mail Dr. Hemant Purohit
: h p u r o h i t at
g m u dot
e d u)
- Natural Crises: Crisis Response Coordination, Intent Mining, Serviceability, Help Seeker-Provider Matching
- H. Purohit, C. Castillo, and R. Pandey. (2020). Ranking and grouping social media requests for emergency services using serviceability model. In Social Network Analysis and Mining (SNAM), 10(1), 1-17. https://doi.org/10.1007/s13278-020-0633-3
- J. Krishnan, H. Purohit, and H. Rangwala. (2020). Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift. In Proceedings of the 19th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD).
- B. Pedrood, and H. Purohit. (2018). Mining help intent on twitter during disasters via transfer learning with sparse coding. In Proceedings of the 11th Int'l Conference on on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS).
- H. Purohit, and J. Chan. (2017). Classifying User Types on Social Media to inform Who-What-Where Coordination during Crisis Response. In Proceedings of the 14th ISCRAM Conference.
- H. Purohit, C. Castillo, F. Diaz, A. Sheth, and P. Meier. (2013). Emergency-relief coordination on social media: Automatically matching resource requests and offers. First Monday, 19(1).
- H. Purohit, G. Dong, V. Shalin, K. Thirunarayan, and A. Sheth. (2015, December). Intent Classification of Short-Text on Social Media. In 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) (pp. 222-228). IEEE.
A. Sheth, H. Purohit, A. Smith, J. Brunn, A. Jadhav, P. Kapanipathi, C. Lu, and W. Wang. (2017). Twitris: A system for collective social intelligence. In R. Alhajj & J. Rokne (Eds.), Encyclopedia of social network analysis and mining (pp. 1–23). New York, NY: Springer New York. doi: 10.1007/978-1-4614-7163-9_345-1.
- Societal Crises and activism: Gender-based Violence and User Attitudes
- R. Pandey, H. Purohit, B. Stabile, and A. Grant. (2018). Distributional Semantics Approach to Detect Intent in Twitter Conversations on Sexual Assaults. In Proceedings of the 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI).
- P. Karuna, H. Purohit, B. Stabile, and A. Hattery. (2017). On User Engagement across Social Media Campaigns to Curb Gender-based Violence. The 10th International Conference on on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), 2017.
- H. Purohit, T. Banerjee, A. Hampton, V. Shalin, N. Bhandutia, and A. Sheth. (2016). Gender-based violence in 140 characters or fewer: A #BigData case study of Twitter. First Monday, 21(1).
- Y. Ruan, H. Purohit, D. Fuhry, S. Parthasarathy, and A. Sheth. (2012). Prediction of Topic Volume on Twitter. In International ACM Conference on Web Science (Vol. 397, p. 402).