Article citationsMore>>

Wang, N., Luo, F., Shivtare, Y., Badal, V.D., Subbalakshmi, K., Chandramouli, R. and Lee, E. (2021) Learning Models for Suicide Prediction from Social Media Posts. Proceedings of the 7th Workshop on Computational Linguistics and Clinical Psychology: Improving Access, Online, 11 June 2021, 87-92.
https://doi.org/10.18653/v1/2021.clpsych-1.9

has been cited by the following article:

• TITLE: Identification of Influential Users in Online Social Network: A Brief Overview

  AUTHORS: Mahmuda Ferdous, Md. Musfique Anwar

  KEYWORDS: Online Social Network, Trending Topics, Social Influence, Influential User

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.7, July 28, 2023

  ABSTRACT: Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.