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Measuring the size of a crowd using Instagram
16 references·2 citing·47 second hop
Measuring the size of a crowd using Instagram (2019)Measuring the size of a crowd using Insta…The dynamics of information-driven coordination phenomena: A transfer entropy analysis (2016)The dynamics of information-d…Quantifying crowd size with mobile phone and Twitter data (2015)Quantifying crowd size with m…Daily Rhythms in Mobile Telephone Communication (2015)Daily Rhythms in Mobile Telep…A survey of results on mobile phone datasets analysis (2015)A survey of results on mobile…The shortest path to happiness (2014)The shortest path to happinessThe distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics (2014)The distorted mirror of Wikip…Quantifying the semantics of search behavior before stock market moves (2014)Quantifying the semantics of …Quantifying Wikipedia Usage Patterns Before Stock Market Moves (2013)Quantifying Wikipedia Usage P…Quantifying Trading Behavior in Financial Markets Using Google Trends (2013)Quantifying Trading Behavior …Manifesto of computational social science (2012)Manifesto of computational so…Computational social science: Making the links (2012)Computational social science:…Circadian Patterns of Wikipedia Editorial Activity: A Demographic Analysis (2012)Circadian Patterns of Wikiped…Beating the news using social media: the case study of American Idol (2012)Beating the news using social…Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference (2011)Social Event Detection in Mas…Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Real-Time Urban Monitoring Us…Computational Social Science (2009)Computational Social ScienceRapid indicators of deprivation using grocery shopping data (2021)Rapid indicators of deprivati…Crowd Behavior Analysis using Snap Map (2019)Crowd Behavior Analysis using…The mobile connection: the cell phone's impact on society (2004)Are Women Really More Talkative Than Men? (2007)Limits of Predictability in Human Mobility (2010)Chronotype, gender and general health (2016)Bursty Human Dynamics (2017)Promises and Pitfalls of Using Digital Traces for Demographic Research (2018)A BEHAVIORAL MODEL OF RATIONAL CHOICE (1955)Loss Aversion in Riskless Choice: A Reference-Dependent Model (1991)Twitter mood predicts the stock market (2011)Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data (2013)Predicting short-term stock prices using ensemble methods and online data sources (2018)Measuring objective and subjective well-being: dimensions and data sources (2020)In Search of Attention (2011)Quantifying China’s regional economic complexity (2017)Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective (2017)Walking Distances to and from Light-Rail Transit Stations (1996)Introduction to a Critique of Urban Geography (2008)Walkable City: How Downtown Can Save America, One Step at a Time (2012)Algorithms (and the) everyday (2019)Urban Computing Leveraging Location-Based Social Network Data (2019)Integrating GIS, deep learning, and environmental sensors for multicriteria evaluation of urban street walkability (2022)The Evolution of Cooperation (1981)Bowling alone (2000)Bowling alone: the collapse and revival of American community (2000)Geographic information systems and science (2005)Geographic information systems and science (2011)Geographic Information Systems and Science (2019)A NEW MEASURE OF RANK CORRELATION (1938)BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era (2013)The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy (2014)COVID-19, lockdowns and well-being: Evidence from Google Trends (2020)The Strength of Weak Ties (1973)Power-Law Distributions in Empirical Data (2009)A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015)Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Power-law distributions in empirical data (2018)The universal visitation law of human mobility (2021)The origin of bursts and heavy tails in human dynamics (2005)Analyzing and visualizing the semantic coverage of Wikipedia and its authors (2007)Dynamics of Conflicts in Wikipedia (2012)The Most Controversial Topics in Wikipedia: A Multilingual and Geographical Analysis (2013)Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study (2008)A 61-million-person experiment in social influence and political mobilization (2012)Private traits and attributes are predictable from digital records of human behavior (2013)Structural Topic Models for Open‐Ended Survey Responses (2014)Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)
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References

  • 2009Computational Social Science3,359 cited
  • 2013Quantifying Trading Behavior in Financial Markets Using Google Trends947 cited
  • 2015A survey of results on mobile phone datasets analysis652 cited
  • 2010Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome623 cited
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  • 2015Daily Rhythms in Mobile Telephone Communication143 cited
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  • 2011Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference88 cited

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  • 2012Beating the news using social media: the case study of American Idol78 cited
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    • 2021Rapid indicators of deprivation using grocery shopping data7 cited
    • 2019Crowd Behavior Analysis using Snap Map5 cited