Detecting Rumors Through Modeling Information Propagation Networks in a Social Media Environment

Abstract

In the midst of today's pervasive influence of social media content and activities, information credibility has increasingly become a major issue. Accordingly, identifying false information, e.g. rumors circulated in social media environments, attracts expanding research attention and growing interests. Many previous studies have exploited user-independent features for rumor detection. These prior investigations uniformly treat all users relevant to the propagation of a social media message as instances of a generic entity. Such a modeling approach usually adopts a homogeneous network to represent all users, the practice of which ignores the variety across an entire user population in a social media environment. Recognizing this limitation of modeling methodologies, this study explores user-specific features in a social media environment for rumor detection. The new approach hypothesizes that whether a user tends to spread a rumor is dependent upon specific attributes of the user in addition to content characteristics of the message itself. Under this hypothesis, information propagation patterns of rumors versus those of credible messages in a social media environment are systematically differentiable. To explore and exploit this hypothesis, we develop a new information propagation model based on a heterogeneous user representation for rumor recognition. The new approach is capable of differentiating rumors from credible messages through observing distinctions in their respective propagation patterns in social media. Experimental results show that the new information propagation model based on heterogeneous user representation can effectively distinguish rumors from credible social media content.

A two-step rumor detection model based on the supernetwork theory about Weibo

“…To measure the performance of the proposed rumor detection model, following common practice [ 11 , 12 , 20 ], four methods were used, including Accuracy (Acc. ), Precision (P.), Recall (R.), and Fa - score (Fa) .…”

“…Jain et al [ 9 ] proposed a rumor detection model that only considered psychological features of online posts, and the model achieved appreciable results. Therefore, in the present paper, unlike most previous studies [ 20 , 39 ] that the psychological-based features are classified into the content category, we created a new set of features relevant to the psychological aspect. In addition, with respect to the emotional tendency of online posts, some existing papers only consider two kinds of emotions: positive and negative [ 21 ], which could not effectively describe the complex emotional expression contained in online posts, especially for those in Chinese language.…”

Human dynamics in space and time: A brief history and a view forward

“…Human dynamics research on the SIoT aims to describe human behavior in real time using the internet‐based information ecosystem from smart cities, wearable devices, and other sources of big data (Ahmad, Rathore, Paul, & Rho, ; Paul, Ahmad, Rathore, & Jabbar, ). With mobility, interaction, and context, human dynamics research investigates human contact networks (Starnini, Baronchelli, & Pastor‐Satorras, ), disease spread (Barmak, Dorso, & Otero, ), detecting rumors (Collier et al, ; Liu & Xu, ), and emergent events (Solmaz & Turgut, ).…”

A Comprehensive Insight towards Research Direction in Information Propagation

“…A mathematical modeling-based approach is used for this purpose for developing attributes for node interaction as well as for investigating the dispersion over the feasibility towards multiple states. Study towards rumor detection has been also carried out recently by Liu and Xu [48] where the authors have modeled information propagation in order to differentiate rumor-based messages. Wen et al [49] have presented an analytical technique that can distinguish between positive and negative forms of information in social networks.…”