Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study

Singh, Ravinder and Zhang, Yanchun and Wang, Hua and Miao, Yuan and Ahmed, Khandakar (2020) Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study. EAI Endorsed Transactions on Scalable Information Systems, 8 (31). e2. ISSN 2032-9407

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Location-based social networks such as Swarm provide a rich source of information on human behaviour and urban functions. Our analysis of data created by users who voluntarily used check-ins with a mobile application can give insight into a user’s mobility and behaviour patterns. In this study, we used location-sharing data from Swarm to explore spatio-temporal, geo-temporal and behaviour patterns within the city of Melbourne. Moreover, we used several tools for different datasets. We used the MeaningCloud tool for sentiment analysis and the LIWC15 tool for psychometric analysis. Also, we employed SPSS software for the descriptive statistical analysis on check-in data to reveal meaningful trends and attain a deeper understanding of human behaviour patterns in the city. The results show that most people do not express strong negative or positive emotions in relation to the places they visit. Behaviour patterns vary based on gender. Furthermore, mobility patterns are different on different days of the week as well as at different times of a day but are not necessarily influenced by the weather.

Item Type: Article
Uncontrolled Keywords: Behaviour Patterns, Social Media, Spatio-Temporal, Mobility Patterns, Swarmapp, Twitter, Sentiment Analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 09 Jul 2021 08:31
Last Modified: 09 Jul 2021 08:31

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