CS Forum: "Software-Aided Collective Response to Mass Emergencies" Maleknaz Nayebi

CS forum is a seminar series arranged at the CS department - open to everyone free-of-charge.

This talk is for general CS audience.

Before the talk at 13:00, instead of the regular coffee & chocolate, we serve sima & munkki in honor of Finnish Vappu (1st of May).

Maleknaz Nayebi
Postdoctoral Fellow at the University of Toronto

Host: Professor Casper Lassenius
Time: 13:15 (sima & munkki at 13:00)
Venue: T4, CS building

Software-Aided Collective Response to Mass Emergencies

Abstract:

The ubiquity of mobile devices has led to unprecedented growth in not only the usage of apps but also their capacity to meet people's needs. Smartphones take on a heightened role in emergency situations, as they may suddenly be among their owner’s only possessions and resources. The 2016 wildfire in Fort McMurray, Canada, intrigued us to study the functionality of the existing apps by analyzing social media information. We investigated a method to suggest features that are useful for emergency apps. Our proposed method called MAPFEAT combines various machine learning techniques to analyze tweets in conjunction with crowdsourcing and guides an extended search in app stores to find currently missing features in emergency apps based on the needs stated in social media (i) existing wildfire apps covered features that are mostly considered unessential and unhelpful, and (ii) MAPFEAT's suggest feature set that is better aligned with the general public needs. We found that Only six of the features existing in wildfire apps is among top 40 crowdsourced features explored by MAPFEAT, with the most important one ranked 13th. By using MAPFEAT, we proactively understand victims' needs and suggest mobile software support to the people impacted. MAPFEAT looks beyond the current functionality of apps in the same domain and extracts features using a variety of crowdsourced data. The continuation of this study is ongoing in advancing the use of machine learning to learn from different mass emergencies for predicting the needs for a new emergency event and providing software tools to help emergency management.