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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

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Author(s):
Rosa, Renata Lopes [1] ; De Silva, Marielle Jordane [1] ; Silva, Douglas Henrique [1] ; Ayub, Muhammad Shoaib [2] ; Carrillo, Dick [3] ; Nardelli, Pedro H. J. [3] ; Rodriguez, Demostenes Zegarra [1]
Total Authors: 7
Affiliation:
[1] Univ Fed Lavras UFLA, Dept Comp Sci, BR-37200900 Lavras - Brazil
[2] Chulalongkorn Univ, Dept Elect Engn, Bangkok 10330 - Thailand
[3] Lappeenranta Lahti Univ Technol, Sch Energy Syst, Lappeenranta 53850 - Finland
Total Affiliations: 3
Document type: Journal article
Source: IEEE ACCESS; v. 8, p. 158806-158825, 2020.
Web of Science Citations: 0
Abstract

People use Online Social Networks (OSNs) to express their opinions and feelings about many topics. Depending on the nature of an event and its dissemination rate in OSNs, and considering specific regions, the users' behavior can drastically change over a specific period of time. In this context, this work aims to propose an event detection system at the early stages of an event based on changes in the users' behavior in an OSN. This system can detect an event of any subject, and thus, it can be used for different purposes. The proposed event detection system is composed of the following main modules: (1) determination of the user's location, (2) message extraction from an OSN, (3) topic identification using natural language processing (NLP) based on the Deep Belief Network (DBN), (4) the user behavior change analyzer in the OSN, and (5) affective analysis for emotion identification based on a tree-convolutional neural network (tree-CNN). In the case of public health, the early event detection is very relevant for the population and the authorities in order to be able take corrective actions. Hence, the new coronavirus disease (COVID-19) is used as a case study in this work. For performance validation, the modules related to the topic identification and affective analysis were compared with other similar solutions or implemented with other machine learning algorithms. In the performance assessment, the proposed event detection system achieved an accuracy higher than 0.90, while other similar methods reached accuracy values less than 0.74. Additionally, our proposed system was able to detect an event almost three days earlier than the other methods. Furthermore, the information provided by the system permits to understand the predominant characteristics of an event, such as keywords and emotion type of messages. (AU)

FAPESP's process: 15/24496-0 - Evaluation of the service of communication operators using the voice Quality Index
Grantee:Demostenes Zegarra Rodriguez
Support type: Regular Research Grants
FAPESP's process: 18/26455-8 - Audio-Visual Speech Processing by Machine Learning
Grantee:Miguel Arjona Ramírez
Support type: Regular Research Grants