With the proliferation of Web 2.0 Social Media sites like Facebook, Twitter, Instagram, and many others (~800 others) make it possible to cover everything and anything for everyone. For instance, Twitter's 140-character message really is an amazing service that proved it to be anything to many events, disasters, people, celebrities, brands etc. It was the Twitter that triggered Arab upheaval, saved many lives during the Sandy hurricane in the US, and during a devastated earthquake in Nepal in 2015.
During emergencies such as natural disasters or man-made crises, people use social media tools to post situational updates, their sufferings and needs etc. in the form of textual messages, images, and videos. Of all information that is posted online, more importantly textual information could be useful to gain situational awareness as well as to extract actionable information. We believe that if analyzed timely and effectively, these small bits of information can collectively help humanitarian organizations in their decision-making processes and eventually can save hundreds of lives.
Having in mind this motivation, I am interested in understanding the role of microblogging platforms such as Twitter during mass convergence events by using big data analysis techniques such as text classification, data mining, machine learning, and deep neural networks. Moreover, I’m interested in developing novel computational techniques and technologies that can help stakeholders gain situation awareness and actionable information that can improve their decision-making processes.
Research talks: Research talks related to my current research interests.