Investigation of social behaviour patterns using location-based data - a Melbourne case study

[thumbnail of eai.26-10-2020.166767.pdf]
eai.26-10-2020.166767.pdf - Published Version (4MB) | Preview
Available under license: Creative Commons Attribution

Singh, Ravinder, Zhang, Yanchun ORCID: 0000-0002-5094-5980, Wang, Hua ORCID: 0000-0002-8465-0996, Miao, Yuan ORCID: 0000-0002-6712-3465 and Ahmed, Khandakar ORCID: 0000-0003-1043-2029 (2021) Investigation of social behaviour patterns using location-based data - a Melbourne case study. EAI Endorsed Transactions on Scalable Information Systems, 21 (31). ISSN 2032-9407


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 spatiotemporal, 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.

Dimensions Badge

Altmetric Badge

Item type Article
DOI 10.4108/eai.26-10-2020.166767
Official URL
Subjects Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > FOR (2020) Classification > 4605 Data management and data science
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords social networks, human behavior, mobile check in data, human behavior patterns
Citations in Scopus 14 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login