Using social media data, such as Twitter and Instagram, I identify relevant information, develop data models, estimate and analyse crowds’ characteristics in city events, including demographic and city-role composition, Spatial-temporal distribution, sentiment estimation, Points of Interest preferences, word use, crowd size and density estimation, which help crowd managers make better decisions.
In this website, you can find my CV below, and the summary of my research in the Portfolio section.
Education & Research Work
- Postdoctoral researcher in Human-Centered Artificial Intelligence Section in the Faculty of Industrial Design Engineering (IDE), Delft University of Technology, 2020 - 2021.
- Project: PERISCOPE, funded by EU, Pan-European Response to the ImpactS of COVID-19 and future Pandemics and Epidemics.
- Ph.D. in Transport & Planning, Delft University of Technology, 2016 - 2020 (expected).
- Topic: using social media data to investigate crowds characteristics in city events.
- Supervisor: Winnie Daamen, Alessandro Bozzon, Serge P. Hoogendoorn.
- Graduated: Sep 2020
- M.S. in Computer Science, Delft University of Technology, 2013 - 2016
- Data Science track, Information Architecture programme.
- Thesis: Exploring Human Activity Patterns Across Cities through Social Media Data.
- Gong, Vincent X., Jie Yang, Winnie Daamen, Alessandro Bozzon, Serge Hoogendoorn, and Geert-Jan Houben. Using Social Media for Attendees Density Estimation in City-Scale Events. IEEE Access 6 (2018): 36325-36340.
- Gong, Vincent X., Winnie Daamen, Alessandro Bozzon, and Serge P. Hoogendoorn. Estimate Sentiment of Crowds from Social Media during City Events. Transportation Research Record, (June 2019). doi:10.1177/0361198119846461.
- Gong, Vincent X., Winnie Daamen, Alessandro Bozzon, and Serge P. Hoogendoorn. Crowd Characterization for Crowd Management using Social Media Data in City Events. Travel Behaviour and Society, (July 2020). doi:10.1016/j.tbs.2020.03.011
- Gong, Vincent X., Winnie Daamen, Alessandro Bozzon, and Serge P. Hoogendoorn. Counting people in the crowd using social media images for crowd management in city events. Transportation , (Jan 2021). doi:10.1007/s11116-020-10159-z
- “Estimate sentiment of crowds from social media during city events.” Transportation Research Board Annual Meeting, 2019.
- “Estimate sentiment of crowds from social media during city events” TRAIL Congress, 2018.
- “Crowd characterization for crowd management using social media data in city events.” Transportation Research Board Annual Meeting, 2018.
- “Using social media for attendees density estimation in city-scale events.” ICT.OPEN, 2017.
Honours and awards
- Norvig Web Data Science Award 2014. A challenge to discover similar images in a given WARC dataset considering various factors, such as temporal factors and context information. The dataset was crawled from internet and contains various web file formats. We performed the comparison using MapReduce framework.
- Supervise student in social media data analysis, Amsterdam Institute for Advanced Metropolitan Solutions (AMS), 2017.
Updated: Mar 20, 2021