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Event Categorization and Key Prospect Identification from Storylines

  • Manu ShuklaEmail author
  • Andrew Fong
  • Raimundo Dos Santos
  • Chang-Tien Lu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 741)

Abstract

Event analysis and prospect identification in social media is challenging due to endless amount of information generated daily. While current research focuses on detecting events, there is no clear guidance on how those events should be processed such that they are meaningful to a human analyst. There are no clear ways to detect prospects from social media either. In this paper, we present DISTL, an event processing and prospect identifying platform. It accepts as input a set of storylines (a sequence of entities and their relationships) and processes them as follows: (1) uses different algorithms (LDA, SVM, information gain, rule sets) to identify themes from storylines; (2) identifies top locations and times in storylines and combines with themes to generate events that are meaningful in a specific scenario for categorizing storylines; and (3) extracts top prospects as people and organizations from data elements contained in storylines. The output comprises sets of events in different categories and storylines under them along with top prospects identified. DISTL uses in-memory distributed processing that scales to high data volumes and categorizes generated storylines in near real-time.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Manu Shukla
    • 1
    • 2
    • 3
    Email author
  • Andrew Fong
    • 1
    • 2
    • 3
  • Raimundo Dos Santos
    • 1
    • 2
    • 3
  • Chang-Tien Lu
    • 1
    • 2
    • 3
  1. 1.Omniscience CorporationPalo AltoUSA
  2. 2.US Army Corps of Engineers ERDC GRLAlexandriaUSA
  3. 3.Computer Science DepartmentVirginia TechFalls ChurchUSA

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