Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic
| Autor: | Pedro H. J. Nardelli, Renata Lopes Rosa, Marielle Jordane De Silva, Douglas Henrique Silva, Muhammad Ayub, Dick Carrillo, Demostenes Zegarra Rodriguez |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
affective analysis General Computer Science Computer science Population Social Implications of Technology Context (language use) Machine learning computer.software_genre 010603 evolutionary biology 01 natural sciences 03 medical and health sciences Deep belief network General Materials Science natural language processing education 030304 developmental biology 0303 health sciences Thesaurus (information retrieval) education.field_of_study Artificial neural network Event (computing) business.industry Behavior change General Engineering COVID-19 TK1-9971 Identification (information) online social networks Artificial intelligence Event detection Electrical engineering. Electronics. Nuclear engineering business computer Industry Applications |
| Zdroj: | IEEE Access, Vol 8, Pp 158806-158825 (2020) Ieee Access |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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