Hybrid Approach for Detecting Propagandistic Community and Core Node on Social Networks

Akib Mohi Ud Din Khanday, Mudasir Ahmad Wani (), Syed Tanzeel Rabani and Qamar Rayees Khan
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Akib Mohi Ud Din Khanday: Department of Computer Sciences, BGSB University, Kashmir 185234, India
Mudasir Ahmad Wani: EIAS Data Science Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia
Syed Tanzeel Rabani: Department of Computer Sciences, BGSB University, Kashmir 185234, India
Qamar Rayees Khan: Department of Computer Sciences, BGSB University, Kashmir 185234, India

Sustainability, 2023, vol. 15, issue 2, 1-15

Abstract: People share their views and daily life experiences on social networks and form a network structure. The information shared on social networks can be unreliable, and detecting such kinds of information may reduce mass panic. Propaganda is a kind of biased or unreliable information that can mislead or intend to promote a political cause. The disseminators involved in spreading such information create a sophisticated network structure. Detecting such communities can lead to a safe and reliable network for the users. In this paper, a Boundary-based Community Detection Approach (BCDA) has been proposed to identify the core nodes in a propagandistic community that detects propagandistic communities from social networks with the help of interior and boundary nodes. The approach consists of two phases, one is to detect the community, and the other is to detect the core member. The approach mines nodes from the boundary as well as from the interior of the community structure. The leader Ranker algorithm is used for mining candidate nodes within the boundary, and the Constraint coefficient is used for mining nodes within the boundary. A novel dataset is generated from Twitter. About six propagandistic communities are detected. The core members of the propagandistic community are a combination of a few nodes. The experiments are conducted on a newly collected Twitter dataset consisting of 16 attributes. From the experimental results, it is clear that the proposed model outperformed other related approaches, including Greedy Approach, Improved Community-based 316 Robust Influence Maximization (ICRIM), Community Based Influence Maximization Approach (CBIMA), etc. It was also observed from the experiments that most of the propagandistic information is being shared during trending events around the globe, for example, at times of the COVID-19 pandemic.

Keywords: dubious; propagandistic; community; core; nodes; online social networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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