  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.
