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. 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 of the classification as well as grouping of features. We achieve and f-measure of over 0.82 in RDC task in mixed rumors data set and 84 percent in a single rumor data set using a two-step classification approach.
@article{arxiv.1912.08926,
title = {Rumor Detection and Classification for Twitter Data},
author = {Sardar Hamidian and Mona T Diab},
journal= {arXiv preprint arXiv:1912.08926},
year = {2019}
}