Sub-event detection during natural hazards using features of social media data

Abstract

Social networking sites such as Flickr, YouTube, Facebook, etc. contain a huge amount of user-contributed data for a variety of real-world events. These events can be some natural calamities such as earthquakes, floods, forest fires, etc. or some man-made hazards like riots. This work focuses on getting better knowledge about a natural hazard event using the data available from social networking sites. Rescue and relief activities in emergency situations can be enhanced by identifying sub-events of a particular event. Traditional topic discovery techniques used for event identification in news data cannot be used for social media data because social network data may be unstructured. To address this problem the features or metadata associated with social media data can be exploited. These features can be user-provided annotations (e.g., title, description) and automatically generated information (e.g., content creation time). Considerable improvement in performance is observed by using multiple features of social media data for sub-event detection rather than using individual feature. Proposed here is a two-step process. In the first step, clusters are formed from social network data using relevant features individually. Based on the significance of features weights are assigned to them. And in the second step all the clustering solutions formed in the first step are combined in a principal weighted manner to give the final clustering solution. Each cluster represents a sub-event for a particular natural hazard.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 39 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.