Preprocessing Techniques to Support Event Detection Data Fusion on Social Media Data

Abstract

This thesis focuses on collection and preprocessing of streaming social media feeds for metadata as well as the visual and textual information. Today, news media has been the main source of immediate news events, large and small. However, the information conveyed on these news sources is delayed due to the lack of proximity and general knowledge of the event. Such news have started relying on social media sources for initial knowledge of these events. Previous works focused on captured textual data from social media as a data source to detect events. This preprocessing framework postures to facilitate the data fusion of images and text for event detection. Results from the preprocessing techniques explained in this work show the textual and visual data collected are able to be-proceeded into a workable format for further processing. Moreover, the textual and visual data collected are transformed into bag-of-words vectors for future data fusion and event detection.

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Document Details

Document Type
Technical Report
Publication Date
Jun 16, 2016
Accession Number
AD1054215

Entities

People

  • Brandon T. Davis

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Languages
  • Data Mining
  • Event Detection
  • Feature Extraction
  • Governments
  • Machine Learning
  • Online Communications
  • Ontologies
  • Particle Swarm Optimization
  • Social Media
  • Social Networking Services
  • Social Networks
  • Supervised Machine Learning

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Political Science/ International Relations/ European Studies