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Quantifying crowd size with mobile phone and Twitter data

Published in Royal Society Open Science, 2015

Being able to infer the number of people in a specific area is of extreme importance for the avoidance of crowd disasters and to facilitate emergency evacuations. Here, using a football stadium and an airport as case studies, we present evidence of a strong relationship between the number of people in restricted areas and activity recorded by mobile phone providers and the online service Twitter. Our findings suggest that data generated through our interactions with mobile phone networks and the Internet may allow us to gain valuable measurements of the current state of society.

Recommended citation: Botta, F., Moat, H. S., & Preis, T. (2015). Quantifying crowd size with mobile phone and Twitter data. Royal Society open science, 2(5), 150162. 10.1098/rsos.150162

Quantifying stock return distributions in financial markets

Published in PLOS ONE, 2015

Being able to quantify the probability of large price changes in stock markets is of crucial importance in understanding financial crises that affect the lives of people worldwide. Large changes in stock market prices can arise abruptly, within a matter of minutes, or develop across much longer time scales. Here, we analyze a dataset comprising the stocks forming the Dow Jones Industrial Average at a second by second resolution in the period from January 2008 to July 2010 in order to quantify the distribution of changes in market prices at a range of time scales. We find that the tails of the distributions of logarithmic price changes, or returns, exhibit power law decays for time scales ranging from 300 seconds to 3600 seconds. For larger time scales, we find that the distributions tails exhibit exponential decay. Our findings may inform the development of models of market behavior across varying time scales.

Recommended citation: Botta, F., Moat, H. S., Stanley, H. E., & Preis, T. (2015). Quantifying stock return distributions in financial markets. PloS one, 10(9), e0135600. https://doi.org/10.1371/journal.pone.0135600

Finding network communities using modularity density

Published in Journal of Statistical Mechanics: Theory and Experiment, 2016

Many real-world complex networks exhibit a community structure, in which the modules correspond to actual functional units. Identifying these communities is a key challenge for scientists. A common approach is to search for the network partition that maximizes a quality function. Here, we present a detailed analysis of a recently proposed function, namely modularity density. We show that it does not incur in the drawbacks suffered by traditional modularity, and that it can identify networks without ground-truth community structure, deriving its analytical dependence on link density in generic random graphs. In addition, we show that modularity density allows an easy comparison between networks of different sizes, and we also present some limitations that methods based on modularity density may suffer from. Finally, we introduce an efficient, quadratic community detection algorithm based on modularity density maximization, validating its accuracy against theoretical predictions and on a set of benchmark networks.
An implementation of the algorithm presented in this paper is available on my GitHub page .

Recommended citation: Botta, F., & del Genio, C. I. (2016). Finding network communities using modularity density. Journal of Statistical Mechanics: Theory and Experiment, 2016(12), 123402. https://arxiv.org/pdf/1612.07297.pdf

Analysis of the communities of an urban mobile phone network

Published in PLOS ONE, 2017

Being able to characterise the patterns of communications between individuals across different time scales is of great importance in understanding people’s social interactions. Here, we present a detailed analysis of the community structure of the network of mobile phone calls in the metropolitan area of Milan revealing temporal patterns of communications between people. We show that circadian and weekly patterns can be found in the evolution of communities, presenting evidence that these cycles arise not only at the individual level but also at that of social groups. Our findings suggest that these trends are present across a range of time scales, from hours to days and weeks, and can be used to detect socially relevant events.

Recommended citation: Botta, F., & del Genio, C. I. (2017). Analysis of the communities of an urban mobile phone network. PloS one, 12(3), e0174198. https://doi.org/10.1371/journal.pone.0174198

Measuring the size of a crowd using Instagram

Published in Environment and Planning B: Urban Analytics and City Science, 2019

Measuring the size of a crowd in a specific location can be of crucial importance for crowd management, in particular in emergency situations. Here, using two football stadiums as case studies, we present evidence that data generated through interactions with the social media platform Instagram can be used to generate estimates of the size of a crowd. We present a detailed analysis of the impact of varying the time period and spatial area considered for the collection of Instagram data. Crucially, we demonstrate how to address issues that arise from changes in the usage of a social media platform such as Instagram. Our findings show how social media datasets carrying location-based information may help provide near to real-time measurements of the size of a crowd.

Recommended citation: 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.

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