Classifying and Summarizing Information from Microblogs During Epidemics

Summarizing Microblogs Automatically

In this paper, we focus on a recent Web trend called microblogging, and in particular a site called Twitter. The content of such a site is an extraordinarily large number of small textual messages, posted by millions of users, at random or in response to perceived events or situations. We have developed an algorithm that takes a trending phrase or any phrase specified by a user, collects a larg...

Information Retrieval from Microblogs during Natural Disasters

In this paper, we devise an information retrieval system which can filter and rank tweets according to relevance to the query. We devise methods to understand relationships among entities and action verbs from a small set of manually annotated tweets. We further use these relationships to filter tweets and rank them accordingly. Our results (as published by FIRE Microblog Track) show that we ha...

Summarizing Microblogs for Emergency Relief and Preparedness

In recent years, Online Social Media (OSM) has established itself as one of the most significant sources of situational information during any natural or man-made disaster. Real-time summarization of this rapidly posted huge volume of crowdsourced responses is a common requirement for emergency relief and preparedness when time is critical. Our semi-automatic method exploits a combination of Su...

Information Extraction from Microblogs

The micro blogging sites contain the emotion and expression of the public in raw format. The data can be used to extract much meaningful information that could be used to develop technologies for future use. There are numerous micro blogging sites available these days that are used in different contexts. Some are used basically for conversation, some for image and video sharing, and some for fo...

HLJIT2017-IRMIDIS@IRMiDis-FIRE2017: Information Retrieval from Microblogs during Disasters