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.
翻译:利用信息制作和复制的民主化技术,社交媒体的日常互动文章中有很大一部分受到流言的影响。尽管对谣言的探测和核实进行了广泛的研究,但迄今还没有考虑到计算谣言传播力的问题。为解决这一研究差距,本研究报告寻求一种模型,用以计算流言的传播力,作为基于内容的特征在两类内容上的功能:(a) 假的流言(FR) 和 True Rumor (TR) 。为此,将采用Allport 和 Postman 的理论。它声称,在流言煽动和流言的力量中,重要性和模糊性是关键变量。在“重要性”(28个特征)和“bamgiity”(14个特征)这两个类别中,总共42个内容特征被引入了计算SPR。提议的模式在两个数据集(Twitter和Telegram)上进行评估。结果显示,(i) 虚假的流言语文件的传播力很少比真理的流言者要大。 (ii)两个群体在SPR手段上存在显著的差异。Sprillar Rumor和真实的Rumors(iii)之间有一个积极的区别性标准。