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

EAI Endorsed Transactions on Scalable Information Systems

Khandakar Ahmed¹  Hua Wang¹  Yanchun Zhang¹  Ravinder Singh¹  Yuan Miao¹ 

[1] Institute for Sustainable Industries and Liveable Cites, Victoria University, Melbourne, Australia;

关键词: behaviour patterns;    social media;    spatio-temporal;    mobility patterns;    swarmapp;    twitter;    sentiment analysis;   

DOI  :  10.4108/eai.26-10-2020.166767

来源: DOAJ

【 摘 要 】

Location-basedsocialnetworkssuchasSwarmprovidearichsourceofinformationonhumanbehaviourandurbanfunctions. 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 softwarefor the descriptive statisticalanalysison check-indatato revealmeaningful trends and attain a deeperunderstandingofhumanbehaviourpatternsinthecity.Theresultsshowthatmostpeopledonotexpressstrongnegativeorpositiveemotionsinrelationtotheplacestheyvisit.Behaviour patterns vary based on gender.Furthermore,mobilitypatternsaredifferentondifferent days of theweek as well as at different times of a day but are not necessarily influenced by the weather.

【 授权许可】

Unknown