Detecting Early Signatures of Persuasion in Information Cascades

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Flammini, A, Menczer, F, Mei, Qiaozhu, Malinchik, S
Format: Report
Language:English
Subjects:
CLASSIFICATION
Cybernetics
DETECTION
EARLY DETECTION OF RUMORS
INFORMATION PROCESSING
Information Science
PROMOTED CONTENT CLASSIFICATION
RELIABILITY
SOCIAL COMMUNICATION
USER INFLUENCE DETECTION
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. The original document contains color images.