Mehdi Ben Lazreg successfully defended his PhD Thesis on social media and emergency response.
In recent years, social media has become a part of daily life for many of us, and increasingly an open medium for emergency communications.
Emergency management services use social media to inform the public about the status of an emergency and what precaution the public needs to take. Also, the citizens use these online mediums to check on the safety of loved ones. Finally, eyewitness and affected individuals share their observations, concerns, and challenges they face during an emergency.
That information can potentially benefit emergency management services to improve situational awareness. However, emergency management services are still reluctant to use social media as a source of information.
Social media challenges
This thesis studies social media analysis platforms and the reason behind the emergency management services reluctance.
We identify two main challenges:
- The first challenge is the language used on social media, which includes misspellings, leetspeak, and abbreviations. This myriad of language usages requires a specific and adaptive normalization technique.
- The second challenge is that most of the dominant platforms try to find accurate ways of extracting as much information related to the crisis as possible. Consequently, they do not explicitly address the specific information-requirements of the time-constrained emergency personnel. This vast amount of supplied information causes an overflow of information, and in many aspects, renders the analytics platforms less useful.
Proposals to solve the challenges
As a mitigation to the first challenge, we propose a string metric that embraces similarities between text strings based on both the character similarities between the words and the context of these words.
For the second challenge, we propose an intelligent information retrieval framework for social media that, given the status of the emergency, provides the information most likely needed by the emergency management services.
The analytics framework we introduce combines two main components.
- The first component classifies social media messages into separate topics representing information required by emergency services during a specific situation.
- The second component decides which information to retrieve by learning what the emergency services need, based on the information available and the status of the emergency.
Doctoral thesis: A Neural Network-Based Situational Awareness Approach for Emergency Response
The Candidate: Mehdi Ben Lazreg (1987, Sousse, Tunisia), BA High school of communication of Tunis, Tunisia (2011), MA - Master Grade Thesis: “A Churn prediction model based on gaussian processes”. University of Agder (2013).
The trial lecture and the public defence will take place in internet (link below), Tuesday 28 April 2020.
Professor Andreas Prinz, Department of ICT, UiA, will chair the disputation.
Trial lecture at 10:15
Public defense at 12:15
Given topic for trial lecture: "User-centered design of information systems"
Thesis Title: “A Neural Network-Based Situational Awareness Approach for Emergency Response”
First opponent: Dr. Muhammad Imran, Hamad bin Khalifa University, Doha, Qatar
Second opponent: Professor Jim Tørresen, University of Oslo, Norway
Professor Frank Reichert, Department of ICT, UiA, is appointed as the administrator for the assessment commitee.
Supervisors were Associate Professor Morten Goodwin, Department of ICT, UiA (main supervisor) and Professor Ole-Christoffer Granmo, Department of ICT, UiA (co-supervisor)