Home // SOTICS 2015,The Fifth International Conference on Social Media Technologies, Communication, and Informatics // View article

Rumor Detection and Classification for Twitter Data

Authors:
Sardar Hamidian
Mona Diab

Keywords: Rumor Detection and Classification, Supervised Machine Learning, Feature-based model

Abstract:
With the pervasiveness of online media data as a source of information, verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First, we detect rumors as a type of misinformation propagation, and next, we go beyond detection to perform the task of rumor classification (RDC). We explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor to the classification as well as grouping of features. We achieve an F-Measure of over 0.82 in the RDC task in a mixed rumors data set and 84% in a single rumor data set using a two step classification approach.

Pages: 71 to 77

Copyright: Copyright (c) IARIA, 2015

Publication date: November 15, 2015

Published in: conference

ISSN: 2326-9294

ISBN: 978-1-61208-443-5

Location: Barcelona, Spain

Dates: from November 15, 2015 to November 20, 2015