  With technologies that have democratized the production and reproduction of
information, a significant portion of daily interacted posts in social media
has been infected by rumors. Despite the extensive research on rumor detection
and verification, so far, the problem of calculating the spread power of rumors
has not been considered. To address this research gap, the present study seeks
a model to calculate the Spread Power of Rumor (SPR) as the function of
content-based features in two categories: False Rumor (FR) and True Rumor (TR).
For this purpose, the theory of Allport and Postman will be adopted, which it
claims that importance and ambiguity are the key variables in rumor-mongering
and the power of rumor. Totally 42 content features in two categories
"importance" (28 features) and "ambiguity" (14 features) are introduced to
compute SPR. The proposed model is evaluated on two datasets, Twitter and
Telegram. The results showed that (i) the spread power of False Rumor documents
is rarely more than True Rumors. (ii) there is a significant difference between
the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a
criterion can have a positive impact on distinguishing False Rumors and True
Rumors.

Social and Information Networks;Computation and Language