Detecting Early Signatures of Persuasion in Information Cascades

Abstract

Indiana University team worked on two different core functions to refine the persuasion detection system that we are building: (i) the feature selection system for organic and promoted content classification; (ii) user influence detection. University of Michigan team worked on i) a formal definition of rumor and ii) with the help of two human annotators to label some rumors from our Boston Marathon Explosion dataset and refine the codebook of rumor. The inter rater reliability is at first 0.46 and then improved to 0.6 after several rounds of modification of codebook. We have also been working on improving the performance of our rumor detection system. With human annotators, we had some very preliminar evaluation of our system.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2013
Accession Number
ADA605549

Entities

People

  • A. Flammini
  • F. Menczer
  • Qiaozhu Mei
  • S. Malinchik

Organizations

  • Indiana University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Clustering
  • Data Mining
  • Data Sets
  • Detection
  • Explosions
  • Feature Selection
  • Language
  • Machine Learning
  • Michigan
  • Natural Languages
  • Online Communications
  • Reliability
  • Social Media
  • Supervised Machine Learning

Readers

  • Ballistic Missile Meteorology
  • Neural Network Machine Learning.
  • Organizational Psychology.