Computational social science

Mann, Adam

doi:10.1073/pnas.1524881113

Cited by 23 publications

(14 citation statements)

References 9 publications

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“…With an increasing number of devices and services that collect data about people activity, interactions and mobility in the last few years, human dynamics research became the key topic in computational social science [7]. Human dynamics research evolves with changing every day circumstances in which people live such as natural and urban environment, emerging new technologies, climate change and society.…”

Section: Related Workmentioning

confidence: 99%

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

Novovic

Brdar

Mesaroš

et al. 2020

IJGI

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CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks.

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Section: Related Workmentioning

confidence: 99%

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

Novovic

Brdar

Mesaroš

et al. 2020

IJGI

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“…We name such crossroads computational social science (CSS) : a new discipline that can offer abstracted (simplified, idealized) models and methods (mainly from statistical physics), large storage, algorithms and computational power (computer and data science), and a set of social hypotheses together with a conceptual framework for the results to be interpreted (Social Science) [3][4][5]. Despite its youth, the field is developing rapidly in terms of contents (articles, books, etc.…”

Section: At the Crossroads: Lessons And Challenges In Computational Smentioning

confidence: 99%

Editorial: At the Crossroads: Lessons and Challenges in Computational Social Science

2016

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The interest of physicists in economic and social questions is not new: during the last decades, we have witnessed the emergence of what is formally called nowadays sociophysics [1] and econophysics [2] that can be grouped into the common term "Interdisciplinary Physics" along with biophysics, medical physics, agrophysics, etc. With tools borrowed from statistical physics and complexity science, among others, these areas of study have already made important contributions to our understanding of how humans organize and interact in our modern society. Large scale data analyses, agent-based modeling and numerical simulations, and finally mathematical modeling, have led to the discovery of new (universal) patterns and their quantitative description in socio-economic systems.At the turn of the century, however, it was clear that huge challenges-and new opportunitieslied ahead: the digital communication technologies, and their associated data deluge, began to nurture those models with empirical significance. Only a decade later, the advent of the Web 2.0, the Internet of Things and a general adoption of mobile technologies have convinced researchers that theories can be mapped to real scenarios and put into empirical test, closing in this way the experiment-theory cycle in the best tradition of physics.We are nowadays at a crossroads, at which different approaches converge. We name such crossroads computational social science (CSS) : a new discipline that can offer abstracted (simplified, idealized) models and methods (mainly from statistical physics), large storage, algorithms and computational power (computer and data science), and a set of social hypotheses together with a conceptual framework for the results to be interpreted (Social Science) [3][4][5]. Despite its youth, the field is developing rapidly in terms of contents (articles, books, etc.), but also institutionally-either under the form of labs, institutes, and academic programs; or as consolidated events and scientific gatherings.This "work-in-progress" spirit is reflected as well in this volume: the call was launched in late 2014 and 10 articles were eventually accepted and published, including reviews-a look behind-, one methods paper, and six original contributions-a look ahead-introducing a broad range of research, from models with a strong analytical flavor to data-driven problems.As mentioned above, each new research line in CSS starts with analysing a sizable dataset, containing transactional data or user generated content on social web (also called Big Data), The availability of data however poses new methodological challenges. Among them are statistical

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“…Information and Communication Technology enables large amounts of data related to human behaviors to be collected in milliseconds opening a novel research area of data-driven social sciences [1][2][3]. In particular, personal opinions and feelings that cannot be known directly from other sources are archived from blogs.…”

Section: Introductionmentioning

confidence: 99%

Identifying long-term periodic cycles and memories of collective emotion in online social media

et al. 2019

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Collective emotion has been traditionally evaluated by questionnaire survey on a limited number of people. Recently, big data of written texts on the Internet has been available for analyzing collective emotion for very large scales. Although short-term reflection between collective emotion and real social phenomena has been widely studied, long-term dynamics of collective emotion has not been studied so far due to the lack of long persistent data sets. In this study, we extracted collective emotion over a 10-year period from 3.6 billion Japanese blog articles. Firstly, we find that collective emotion shows clear periodic cycles, i.e., weekly and seasonal behaviors, accompanied with pulses caused by natural disasters. For example, April is represented by high Tension, probably due to starting school in Japan. We also identified long-term memory in the collective emotion that is characterized by the power-law decay of the autocorrelation function over several months.

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Computational social science

Cited by 23 publications

References 9 publications

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

Editorial: At the Crossroads: Lessons and Challenges in Computational Social Science

Identifying long-term periodic cycles and memories of collective emotion in online social media

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