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

Renata Lopes Rosa; Marielle Jordane De Silva; Douglas Henrique Silva; Muhammad Shoaib Ayub; Dick Carrillo; Pedro H J Nardelli; Demostenes Zegarra Rodriguez

doi:10.1109/ACCESS.2020.3020391

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

IEEE Access. 2020 Aug 31:8:158806-158825. doi: 10.1109/ACCESS.2020.3020391. eCollection 2020.

Authors

Renata Lopes Rosa¹, Marielle Jordane De Silva¹, Douglas Henrique Silva¹, Muhammad Shoaib Ayub², Dick Carrillo³, Pedro H J Nardelli³, Demostenes Zegarra Rodriguez¹

Affiliations

¹ Department of Computer ScienceUniversidade Federal de Lavras (UFLA) Lavras 37200 Brazil.
² Department of Electrical EngineeringChulalongkorn University Bangkok 10330 Thailand.
³ School of Energy SystemsLappeenranta-Lahti University University of Technology 53850 Lappeenranta Finland.

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.

Keywords: COVID-19; Event detection; affective analysis; natural language processing; online social networks.

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Grants and funding

This work was supported in part by the Fundação de Amparo à Pesquisa do Estado de Sao Paulo (FAPESP) under Grant 2015/24496-0 and Grant 2018/26455-8; in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); and in part by the Academy of Finland through the ee-IoT Project under Grant 319009, the FIREMAN Consortium CHIST-ERA under Grant 326270, and the EnergyNet Research Fellowship under Grant 321265 and Grant 328869.