Classifying and Summarizing Information from Microblogs During Epidemics

Koustav Rudra*, Ashish Sharma, Niloy Ganguly, Muhammad Imran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population—people who are not yet affected and are looking for prevention related information (ii) affected population—people who are affected and looking for treatment related information, and (iii) health organizations—like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)933-948
Number of pages16
JournalInformation Systems Frontiers
Volume20
Issue number5
DOIs
Publication statusPublished - 1 Oct 2018

Keywords

  • Classification
  • Epidemic
  • Health crisis
  • Summarization
  • Twitter

Fingerprint

Dive into the research topics of 'Classifying and Summarizing Information from Microblogs During Epidemics'. Together they form a unique fingerprint.

Cite this