Information Processing & Management Journal

Special Issue on Using AI and Social Media for Disaster Response and Management


At the onset of a disaster event, victims, bystanders, and general public increasingly use social media platforms (e.g., Twitter and Facebook) to post situational updates such as reports of injured or dead people, infrastructure damage, requests of urgent needs, and so on. This online information on social media is available in different forms such as textual messages, images, and videos. Several research studies have shown that social media information is useful for disaster response and management if processed timely and effectively.

Encouraged by these findings, humanitarian organizations have already started considering to incorporate more information from non-traditional data sources into their workflows. However, there are still a number of challenges that prevent these organizations using social media information for response efforts. These challenges include near real-time information processing, information overload, information extraction, summarization, and verification among others.

The aim of this special issue is to bring together diverse research communities such as information retrieval, data mining and machine learning, natural language processing, computer vision, computational social science, and human-computer interaction, to potentially contribute towards building AI-based next-generation Information Processing Systems for an effective utilization of social media data for disaster response and management.

Topics of interest include but are not limited to:

  • Robust data mining, natural language processing, computer vision, and machine learning techniques to process social media data in real time
  • Indexing algorithms and technical challenges of handling big social media data during disasters
  • Extracting situational and actionable insights from social media text messages and/or images
  • Reducing information overload for better situational awareness
  • Aggregating multiple data sources for better information extraction
  • Automatic geo-inference from social media text messages and/or images
  • Transfer learning and domain adaptation techniques that exploit existing data and models from past disasters to deal with current events
  • Methods for dealing with task ambiguity, noise, bias, and long tails in social media data
  • Webly-supervised/weakly-supervised learning algorithms on raw user-provided content
  • Multimodal/crossmodal learning for robust classification of social media data
  • Social media information credibility, veracity, and misinformation

Important Dates:

  • Submission deadline: April 1, 2019
  • Notification: June 30, 2019
  • Revisions due: August 15, 2019
  • Final decision: September 30, 2019

Guest Editors:

Submission Guidelines:

We solicit original contributions relevant to the special issue, that have not been published/presented earlier or are not under submission at any other venue. Previously published or accepted conference papers must contain at least 30% new material to be considered for the special issue.

All submitted papers will undergo the standard review process of the journal. All papers must be submitted via the journal editorial submission system ( Authors are requested to select “SI: AI for Disaster Response” as the article type in the submission system. All manuscripts must be prepared according to the journal publication guidelines (