M4D/MKLab in cooperation with the Eastern Alps River Basin District invites you to the “DisasterMM: Multimedia Analysis of Disaster-Related Social Media Data” task at MediaEval 2022, a multimedia evaluation benchmark that is dedicated to evaluating new algorithms for multimedia access and retrieval.

The DisasterMM task concerns the multimedia analysis of social media data, specifically posts from the popular platform of Twitter, that relate to natural or manmade disasters; this year, in particular, the disaster of floods. The participants of this task are provided with a set of Tweet IDs in order to download textual as well as visual information and other metadata of tweets that have been selected with keyword-based search that involved words/phrases about flood. DisasterMM includes two subtasks:

  • Relevance Classification of Twitter Posts (RCTP): participants are asked to build a binary classification system that will be able to distinguish whether a tweet is relevant or not to flooding incidents.
  • Location Extraction from Twitter Texts (LETT): participants are asked to develop a named-entity recognition model in order to identify which words (or sequence of words) inside a tweet’s text refer to locations.

For both subtasks, the datasets are in Italian language, so as to encourage researchers to move away from a focus on English, and have been manually annotated by native speakers.

Find out more at https://multimediaeval.github.io/editions/2022/tasks/disastermm/

Or register now: https://multimediaeval.github.io/