Abstract
Online propagation of untrue information has been and is becoming an increasing problem. Understanding and modeling the diffusion of information on Online Social Networks (OSN's) of voluminous data is the prime concern. The paper provides the history of the epidemic spread and its analogy with untrue information. This paper provides a review of untrue information on online social networks and methods of detection of untrue information based on epidemiological models. Open research challenges and potential future research directions are also highlighted. The paper aimed at aiding research for the identification of untrue information on OSNs.
Highlights
Online Social Networks (OSNs) are one of the most widely used Internet services worlds over
OSNs differ characteristically in terms of (i) size of post, (ii) type of data allowed in the posts, (iii) method of propagation of posted information, (iv)method of collaboration, (v) data generated and interactions
This paper examines the analogy between untrue and epidemic spread along with the introduction of various epidemic models from the literature
Summary
Online Social Networks (OSNs) are one of the most widely used Internet services worlds over. Overlapping of these classes suggests that the truthfulness of information is dependent upon the context in which the information was posted. Spread of untrue information is a function of (i) size of user network (i.e. susceptible), (ii) number of users who believe the information to be true and retweet (i.e. infected), and (iii) elapsed time. Most of the published work has neglected the following aspects: (i) Identifying structural features of untrue information; (ii) Ability to identify early stages of propagation; (iii) Developing an efficient and adaptable detection system for the analysis of false information
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