Quantifying crowd size with mobile phone and Twitter data
Botta, F., Moat, H. S., & Preis, T. (2015). Quantifying crowd size with mobile phone and Twitter data. Royal Society open science, 2(5), 150162.
CV
Education
- BSc in Physics, Università degli Studi di Milano-Bicocca, 2006-2009, graded 108/110
- MSc in Theoretical Physics, Università degli Studi di Milano-Bicocca, 2009-2012, graded 109/110
- MSc in Complexity Science, University of Warwick, 2012-2013, awarded with distinction
- PhD in Complexity Science, University of Warwick, 2013-2016
Work experience
- April 2022 - present: Senior Lecturer in Data Science Department of computer science, University of Exeter
- January 2020 - April 2022: Lecturer in Data Science Department of computer science, University of Exeter
- October 2021 - present: Turing Fellow, Alan Turing Institute
- November 2016 - December 2019: Research Fellow in Data Science
- Data Science Lab, Behavioural Science Group, Warwick Business School
- Investigation of the use of online data to measure crowd size and global mobility patterns. Development of a large scale mobile phone experiment. Collaborations with ONS Data Science Campus, the Department for Digital, Culture, Media, and Sport and the Government Office for Science
Skills
- Computer programming
- R
- Python
- Matlab
- C
- Java for Android app development
- Google Firebase platform
- Amazon Web Services
- Development of ethics framework for behavioural science experiments
- Development of data security framework for app-based experiments
- Management and supervision
- Co-supervision of PhD student in collaboration with Cabinet Office
- Supervision and management of a team developing a smart phone app
- Mentoring and support of an undergraduate student summer project
- Mentoring and support of an A-level student doing a research placement
- Teaching
- Large group teaching
- Small group teaching
- Lab teaching
- Giving summative and formative feedback
- Marking
- Supervising
Service and leadership
- Editorial roles
- Academic editor for PLOS ONE
- Journal referee
- Journal of the Royal Society Interface
- Scientific data
- EPJ Data Science
- Computational Social Networks
- Social Networks Analysis and Mining
- Journal of Economic Interaction and Coordination
- Royal Society Open Science
- Scientific Reports
- International Journal of Geo-Information
- Applied Sciences
- IEEE Pervasive Computing
- Physica A
- Physics Letters A
- Europhysics Letters
- PLOS ONE
- Conference referee
- International Conference on Computational Social Science (2015-2018)
- International Conference on Web and Social Media (2015-2018)
- Social Informatics (2017)
- Conference on Complex Systems (2017)
- Data for Policy (2017)
- Conference Program Committee
- International Conference on Computational Social Science 2019
- Complenet’18
- Elected council member of the Complex Systems Society (2017-2020)
- Elected member of the Steering Committee of the Conference on Complex Systems (2017-2020)
- Elected member of the Advisory Board of the young researchers of the Complex Systems Society (2017-2019)
- Organiser of the Conference on Complex Systems Warm-Up (2018)
- External expert and evaluator for funding bids to the European Commission’s Horizon 2020 programme
Publications
Quantifying stock return distributions in financial markets
Botta, F., Moat, H. S., Stanley, H. E., & Preis, T. (2015). Quantifying stock return distributions in financial markets. PloS one, 10(9), e0135600.
Finding network communities using modularity density
Botta, F., & del Genio, C. I. (2016). Finding network communities using modularity density. Journal of Statistical Mechanics: Theory and Experiment, 2016(12), 123402.
Analysis of the communities of an urban mobile phone network
Botta, F., & del Genio, C. I. (2017). Analysis of the communities of an urban mobile phone network. PloS one, 12(3), e0174198.
Sensing global tourism numbers with millions of publicly shared online photographs
Preis T, Botta F, Moat HS. Sensing global tourism numbers with millions of publicly shared online photographs. Environment and Planning A: Economy and Space (Accepted)
Measuring the size of a crowd using Instagram
Botta, F., Moat, H. S., & Preis, T. (2019). Measuring the size of a crowd using Instagram. Environment and Planning B: Urban Analytics and City Science, 2399808319841615.
In search of art: rapid estimates of gallery and museum visits using Google Trends
Botta, F., Preis, T., & Moat, H. S. (2020). In search of art: rapid estimates of gallery and museum visits using Google Trends. EPJ Data Science, 9(1), 14.
Modelling urban vibrancy with mobile phone and OpenStreetMap data
Botta, F., & Gutiérrez-Roig, M. (2021). Modelling urban vibrancy with mobile phone and OpenStreetMap data. Plos one, 16(6), e0252015.
Quantifying the differences in Call Detail Records
Botta, F. (2021). Quantifying the differences in call detail records. Royal Society Open Science, 8(6), 201443.
Rapid indicators of deprivation using grocery shopping data
Bannister, A., & Botta, F. (2021). Rapid indicators of deprivation using grocery shopping data. Royal Society open science, 8(12), 211069.
Cognitive Networks Extract Insights on COVID-19 Vaccines from English and Italian Popular Tweets: Anticipation, Logistics, Conspiracy and Loss of Trust
Stella, M., Vitevitch, M. S., & Botta, F. (2022). Cognitive Networks Extract Insights on COVID-19 Vaccines from English and Italian Popular Tweets: Anticipation, Logistics, Conspiracy and Loss of Trust. Big Data and Cognitive Computing, 6(2), 52.
Talks
Quantifying crowd size with mobile phone and Twitter data
Invited talk at Computational Social Science Conference,Warwick Business School, Coventry, UK
Quantifying crowd size with mobile phone and Twitter data
Contributed talk at Venice Data Science Summit, Venice, IT
Community structure of a phone call network
Contributed talk at Student Conference on Complexity Science, University of Sussex, Brighton, UK
Communities, phone calls, and all that..
Contributed talk at ECCS Warm-Up, Lucca, IT
Quantifying the behaviour of people through community detection
Invited talk at Complexity Advisory Board, University of Warwick, Coventry, UK
Quantifying crowd size and human communication with mobile phone and Twitter data
Contributed talk at Data Natives, City University of London, London, UK
Quantifying Human Communication and Crowd Size
Contributed talk at Student Conference on Complexity Science, Granada, ES
Quantifying crowd size with mobile phone and Twitter data
Contributed talk at Econophysics Colloquium 2015, Institute of Economic Studies, Charles University, Prague, CZE
Quantifying crowd size with mobile phone and Twitter data
Contributed talk at CS - DC'15 World e-Conference, Tempe, Arizona, US
Quantifying crowd size and human communication with mobile phone and Twitter data
Invited talk at Mathematics Institute, University of Warwick, Coventry, UK
Visualising students’ feedback
Invited talk at The Library, University of Warwick, Coventry, UK
Finding network communities using modularity density
Contributed talk at , Wroclaw, POL
Measuring crowds using Instagram photos
Contributed talk at Data Natives, London, UK
Finding network communities using modularity density
Invited talk at Mathematics of networks, University of Southampton, Southampton, UK
Mining and modelling mobile media data
Invited talk at Web Mining Lab, City University, Hong Kong, HK
Quantifying crowd size with mobile phone and Twitter data
Invited talk at Blavatnik School of Government, Oxford, UK
Quantifying crowd size with mobile phone and Twitter data
Contributed talk at Computational Social Science CCS'16 Satellite, Amsterdam, NL
The properties of modularity density as a quality function for community structure
Contributed talk at Conference on Complex Systems CCS'2016, Amsterdam, NL
Quantifying crowd size with online data
Contributed talk at Conference on Complex Systems CCS'2016, Amsterdam, NL
Nowcasting visitors to museums using online data
Contributed talk at Data Natives, City University of London, London, UK
Finishing your PhD
Invited talk at MathSys Annual Retreat, Ravenstor, UK
Finishing your PhD
Invited talk at MathSys Annual Retreat, Hawkshead, UK
Early indicators of the number of visitors to museums based on Google data
Contributed talk at Advances in Data Science 2018, Museum of Science and Industry, Manchester, UK
A month at the museum
Invited talk at Databeers London, City University, London, UK
Quantifying crowd size and mobility patterns using online data
Invited talk at Warwick Q-Step Methods Spring Camp 2019, University of Warwick, Coventry, UK