Wednesday, September 04, 2019

Communities, Bots and Vaccinations

Following on from our work on bots and health discussions in relation to online social networks (OSNs), Xiaoyi Yuan, Ross Schuchard and myself have just published a paper entitled "Examining Emergent Communities and Detecting Social Bots within the Polarized Online Vaccination Debate in Twitter" in Social Media + Society. Within the paper we explore the communication patterns of vaccine discussions in Twitter. More specifically we ask three questions:
  1. Do vaccine discussions on Twitter show a highly clustered pattern in the sense that users tend to communicate more often with those who have same opinions towards vaccination than those who do not? 
  2. If the communication is highly clustered, to what extent do pro-vaccine users reach out to anti-vaccine users and vice versa? 
  3. How much do social bots, computer algorithms designed to mimic human behavior and interact with humans in an automated fashion, contribute to the conversation as previous research has shown that social bots can have certain impact on human communication in social media?
In order to answer these questions, we use a variety of machine learning techniques (e.g.  logistic regression, support vector machine (e.g. linear and non-linear kernel), k-nearest neighbors, nearest centroid, and Naïve Bayes) trained with labeled data (which is available at to categorize each user’s vaccination stance. By exploring a combination of opinion groups and retweet networks we discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group, while users with neutral opinions are distributed across communities. In addition, our bot analysis (using the  open-source DeBot detection platform) discovered that 1.45% of the corpus users were identified as likely bots and these produced 4.59% of all tweets within our data set. If you wish to find out more about our paper, below you can read the abstract along with seeing some figures including a sketch of our methodology, a selection of results and a link to the paper.

Many states in the United States allow a “belief exemption” for measles, mumps, and rubella (MMR) vaccines. People’s opinion on whether or not to take the vaccine can have direct consequences in public health. Social media has been one of the dominant communication channels for people to express their opinions of vaccination. Despite governmental organizations’ efforts of disseminating information of vaccination benefits, anti-vaccine sentiment is still gaining momentum. Studies have shown that bots on social media (i.e., social bots) can influence opinion trends by posting a substantial number of automated messages. The research presented here investigates the communication patterns of anti- and pro-vaccine users and the role of bots in Twitter by studying a retweet network related to MMR vaccine after the 2015 California Disneyland measles outbreak. We first classified the users into anti-vaccination, neutral to vaccination, and pro-vaccination groups using supervised machine learning. We discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group. In addition, our bot analysis discovers that 1.45% of the corpus users were identified as likely bots which produced 4.59% of all tweets within our dataset. We further found that bots display hyper-social tendencies by initiating retweets at higher frequencies with users within the same opinion group. The article concludes that highly clustered anti-vaccine Twitter users make it difficult for health organizations to penetrate and counter opinionated information while social bots may be deepening this trend. We believe that these findings can be useful in developing strategies for health communication of vaccination.

Keywords: Anti-vaccine Movement, Twitter, Social Media, Opinion Classification, Bot Analysis

Full Reference:
Yuan, X.,  Schuchard, R.  and Crooks, A.T. (2019), Examining Emergent Communities and Detecting Social Bots within the Polarized Online Vaccination Debate in Twitter, Social Media + Society. (pdf)

Tuesday, September 03, 2019

Updates to the MASON

For those who are not on the MASON list-serve, the other day Sean Luke posted a message regarding the a new release (MASON 20) which is part of our NSF CI-EN: Enhancement of a Large-scale Multiagent Simulation Tool project. In this new release (apart from bugfixes) there are some new features. The first is a new distributed parameter sweep package which enables you to run many simulations in parallel with different parameter settings (similar to BehaviorSpace in NetLogo). Next up is an update to GeoMASON, not only are there new demos (as shown below) but changes in the code to enable demos and other applications to be loaded from jar files. The third update is Distributed MASON,  jointly developed with ISISLab at the University of Salerno (it's not D-MASON ). The objective of distributed MASON is to make it possible to port MASON applications to run in cloud computing architectures (more details and example models including distributed HeatBugs, Flockers, and CampusWorld can be seen in Wang et al., 2018). For more details check out the MASON webpage:

A selection of GeoMason Models included in the new release.

Publications relating to the project:
  • Wang, H., Wei, E., Simon, R., Luke, S., Crooks, A.T., Freelan, D. and Spagnuolo, C.  (2018), Scalability in the MASON multi-agent simulation system, in Besada, E., Polo, Ó.R., De Grande, R. and Risco J.L (eds.). Proceedings of the 22nd International Symposium on Distributed Simulation and Real Time Applications, Madrid, Spain, pp. 135-144. (pdf)
  • Luke, S., Simon, R., Crooks, A.T., Wang, H., Wei, E., Freelan, D., Spagnuolo, C., Scarano, V., Cordasco, G. and Cioffi-Revilla, C. (2018), The MASON Simulation Toolkit: Past, Present, and Future, in Davidsson P. and Verhagen H. (eds.), Proceedings of the 19th International Workshop on Multi-Agent-Based Simulation, Stockholm, Sweden, pp. 75-87. (pdf

Friday, August 30, 2019

Message Quality on Entity Location and Identification Performance

At the recent SPIE Optics + Photonics Conference we had a paper entitled "The Impact of Message Quality on Entity Location and Identification Performance in Distributed Situational Awareness." In the paper we discuss the importance of detecting a person or object in complicated and dynamic environments (such as for search and rescue or law enforcement). However, with the growth in sensors and the resulting information from them, the identification and tracking of objects is becoming more difficult. As a result, in this  paper we provide and assess a method to identify objects and the interactions between agents (human or technological) within a collaborative system for improving situational awareness. Below we provide the abstract to the paper, some images of the system along with some results and a link to the paper.

Location and time are critical to the success of many organizations’ missions. Sensors, software, processors, vehicles, and human analysts work together to accomplish these tasks of detecting and identifying specific entities as quickly as possible for these missions. This work aims to make a contribution by providing a team-based detection and identification performance model incorporating the theory of Distributed Situational Awareness (DSA) and its effect on completing a specific task. The task being the ability to detect and identify a specific entity within a complex urban environment. Conditions to accomplish the task is the utilization of two unmanned aerial vehicles mounted with electro-optical sensors, operated by two analysts, creating a team to execute this task. Our results provide an additional resource on the how technology and training might be utilized to find the best performance given these certain conditions and missions. A highly trained team might improve their performance with this technology, or a team with low training could perform at a high level given the appropriate technology in limited time scenarios. More importantly, the model presented in this paper provides an evaluation tool to compare new technologies and their impact on teams. Specifically, it enables answering questions, such as: is an investment in new technology appropriate if investing in additional training produces the same performance results? Future performance can also be evaluated based on the team’s level of training and use of technology for these specific tasks.

Keywords: Situational Awareness, Identification, Detection, Sensors, Training, Team.

Snapshot from FOCUS depicting the flight paths of the unmanned aerial vehicles (UAVs) and sensor field of view in green.

LiDAR map of the city of Samarra, Iraq utilized in FOCUS for this experiment.

Identification of Situational Awareness (SA) level data sets - baseline. Histogram and distribution curve.

Full Reference:
Bates, C.T., Croitoru, A., Crooks, A.T. and Harclerode, E. (2019), The Impact of Message Quality on Entity Location and Identification Performance in Distributed Situational Awareness, Proceedings of the SPIE Optics + Photonics, San Diego, CA. Paper 11137-64 (pdf)

Monday, August 26, 2019

Call for Papers: Humans, Societies and Artificial Agents - SpringSim

At the upcoming 2020 Spring Simulation (SpringSim) Conference being held at George Mason University, Philippe Giabbanelli and myslef are organizing a tract entitled Humans, Societies and Artificial Agents. 

Aims and Scope:

In the Humans, Societies and Artificial Agents (HSAA) Track, you will see that artificial societies have typically relied on agent-based models, Geographical Information Systems (GIS), or cellular automata to capture the decision-making processes of individuals in relation to places and/or social interactions. This has supported a wide range of applications (e.g., in archaeology, economics, geography, psychology, political science, or health) and research tasks (e.g., what-if scenarios or predictive models, models to guide data collection). Several opportunities have recently emerged that augment the capacity of artificial societies at capturing complex human and social behavior. Mixed-methods and hybrid approaches now enable the use of ‘big data’, for instance by combining machine learning with artificial societies to explore the model’s output (i.e., artificial societies as input to machine learning), define the model structure (i.e. machine learning as a preliminary to designing artificial societies), or run a model efficiently (i.e. machine learning as a proxy or surrogate to artificial societies). Datasets are also broader in type since artificial societies can now be built from text, or generate textual as well as visual outputs to better engage end-users. Authors are encouraged to submit papers in the following areas:
  • Artificial agents and societies (e.g., case studies, analyses of moral and ethical considerations)
  • Participatory modeling and simulation
  • Policy development and evaluation through simulations
  • Predictive models of social behavior
  • Simulations of societies as public educational tools
  • Mixed-methods (e.g., analyzing or generating text data with artificial societies, combining machine learning and artificial societies)
  • Models of individual decision-making, mobility patterns, or socio-environmental interactions
  • Testbeds and environments to facilitate artificial society development
  • Tools and methods (e.g., agent-based models, case-based modeling, soft systems)

Important Dates
  • Paper Submission: December 16, 2019 
  • Author Notification: February 17, 2020 
  • Camera-Ready: March 4, 202

For more information, including submission guidelines can be found at  

Thursday, August 01, 2019

Bot stamina: Examining the influence and staying power of bots in online social networks

Following on with our work on bots we just had a paper published in Applied Network Science entitled "Bot Stamina: Examining the Influence and Staying Power of Bots in Online Social Networks" which is significant extension of a previous conference paper. In the paper we look at thee global Twitter conversions in 2016. Specifically the 2016 U.S. presidential election primary races (February 1–28, 2016), the ongoing Ukrainian conflict (August 1–28, 2016), and the Turkish government’s implementation of censorship (December 1–28, 2016) and the influence of Bots on these conversations.

Tweets were classified as Bots using DeBot and then we explore the relative importance and persistence of social bots in online social networks by looking at retweet networks and centrality rankings (i.e. degree, in-degree, out-degree, eigenvector, betweenness and PageRank). We find through such centrality measurements that even though Bots made up less than 0.3% of the total user population, they displayed a profound level of structural network influence.  If you would like to know more about this work, below we provide the abstract to the paper, along with some figures, including one that describes our methodology, and some initial results. Finally at the bottom of the page we provide the full reference and a link to the paper.

This study presents a novel approach to expand the emergent area of social bot research. We employ a methodological framework that aggregates and fuses data from multiple global Twitter conversations with available bot detection platforms and ultimately classifies the relative importance and persistence of social bots in online social networks (OSNs). In testing this methodology across three major global event OSN conversations in 2016, we confirmed the hyper-social nature of bots: suspected social bot accounts make far more attempts on average than social media accounts attributed to human users to initiate contact with other accounts via re-tweets. Social network analysis centrality measurements discover that social bots, while comprising less than 0.3% of the total corpus user population, display a dis-proportionately high profound level of structural network influence by ranking particularly high among the top users across multiple centrality measures within the OSN conversations of interest. Further, we show that social bots exhibit temporal persistence in centrality ranking density when examining these same OSN conversations over time.

Keywords: Social bot analysis, computational social science, social network analysis, online social networks

Full Reference:
Schuchard, R., Crooks, A.T., Stefanidis, A. and Croitoru, A. (2019), Bot Stamina: Examining the Influence and Staying Power of Bots in Online Social Networks, Applied Network Science, 4: 55. Available at (pdf)

Friday, July 26, 2019

Location-Based Social Simulation

At the upcoming 16th International Symposium on Spatial and Temporal Databases (SSTD) we have vision paper entitled "Location-Based Social Simulation" accepted. In the paper we discuss issues such as data sparsity and privacy concerns with using real world location-based social networks (LBSNs) like Foursquare and Yelp. To overcomes these issues, we describe how one can employ geospatial simulation (i.e. an agent-based model) to create artificial, but socially plausible LBSN data sets which overcomes some of the limitations with respect to LBSNs.

Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN datasets in such studies has severe weaknesses: sparse and small datasets, privacy concerns, and a lack of authoritative ground-truth. Our vision is to create a large scale geosimulation framework to simulate human behavior and to create synthetic but realistic LBSN data that captures the location of users over time as well as social interactions of users in a social network. While existing LBSN datasets are trivially small, such a framework would provide the first source of very large LBSN benchmark data which would closely mimic the real world, containing high-fidelity information of location, and social connections of millions of simulated agents over several years of simulated time. Therefore, it would serve the research community by revitalizing and reshaping research on LBSNs by allowing researchers to see the (simulated) world through the lens of an omniscient entity having perfect data. These evaluations will guide future research allowing us to develop solutions to improve LBSN applications such as user-location recommendation, friend recommendation, location prediction, and location privacy.

KEYWORDS: Agent-based simulation, location-based social network, data generator, spatial network, human behavior

Full Reference: 
Kavak, H., Kim, J-S., Crooks, A.T., Pfoser, D., Wenk C. and Züfle, A (2019), Location-Based Social Simulation, Proceedings of the 16th International Symposium on Spatial and Temporal Databases, Vienna, Austria, pp 218-221. (pdf)

Update: Our paper was selected as runner-up  for best Vision Paper.

Thursday, July 04, 2019

Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research

SES diagram with examples of topics that
have been researched using social media data
While I have written about how one can use social media data to study cities, health issues etc... more recently we have been looking into how such data can be used to aid  Socio-environmental Systems (SES) research. SES are defined as tightly linked social and biophysical subsystems that mutually influence one another through positive and negative feedbacks.  To this end, Bianca Lopez, Nick Magliocca and myself just ahd a paper published in Land entitled "Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research." 

In this paper we discuss SES and how research into them poses many challenges, not least of which are collecting or compiling data at the appropriate scales and aligning social and environmental data to address SES questions.  We discuss how SES have been studied using more traditional sources of data (e.g. census data, remote sensing etc.) and explore how social media can be used in the context of SES research. Specifically we ask three specific questions. 1) How can feedback between social and environmental systems be meaningfully studied using social media data? 2) How can using social media data re-frame or compliment current SES research questions and methods? and 3) Are there best practices for collecting and validating social media data for use in SES research? If these questions sound interesting to you, we encourage you to read the abstract below or the full paper.

Social media data provide an unprecedented wealth of information on people’s perceptions, attitudes, and behaviors at fine spatial and temporal scales and over broad extents. Social media data produce insight into relationships between people and the environment at scales that are generally prohibited by the spatial and temporal mismatch between traditional social and environmental data. These data thus have great potential for use in socio-environmental systems (SES) research. However, biases in who uses social media platforms and what they use them for create uncertainty in the potential insights from these data. Here, we describe ways that social media data have been used in SES research, including tracking land-use and environmental changes, natural resource use, and ecosystem service provisioning. We also highlight promising areas for future research and present best practices for SES research using social media data.

Keywords: social media; socio-ecological systems; human-environment interactions; geospatial analysis; crowdsourced data.
Example of information provided by social media posts and how it is used in analyses. A single post from a social media user.

Example of information on people’s use of natural resources from social media data based on key word searches fish and oyster from Twitter, Instagram and Foursquare.

Full Reference:
Lopez, B., Magliocca, N. and Crooks, A.T. (2019), Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research, Land, 8(7), 107;  (pdf)

Monday, July 01, 2019

Modeling Society Reacting to a Nuclear Weapon of Mass Destruction Event
Over the last couple of years we have been working on generating synthetic human populations with realistic social networks with respect to the New York mega-city and surrounding region. This is being done for a variety of modeling applications such as the spread of a disease or exploring peoples reactions to disasters (which was a topic of a recent post on Computational Social Science of Disasters).

To this end, at the upcoming International Conference on Social Computing, Behavioral-Cultural Modeling and; Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS for short) we have a short working paper outlining some of our initial efforts to how people might react following a Nuclear Weapon of Mass Destruction (NWMD) event. In the paper we show some preliminary simulation results relating on  how we are able to simulate basic commuting patterns and initial movement away from the affected area after the NWMD event (like those in the movies below). By using a synthetic population we are able to create an artificial world populated by agents with sufficient heterogeneity to create realistic movement patterns and the social networks which play a vital role in disaster situations. If you want to know more about this work, feel free to read the abstract blow or read the paper. 

Individual connections between human beings often dictate where people go and how they behave, yet their representation through social networks are rarely used as measures of human behavior in agent-based models. Social networks are increasingly used for study of human behavior in disasters, and empirical work has shown that human beings prioritize the safety of themselves and loved ones (i.e., households) before helping neighbors and coworkers. Based on this assumption we have created a set of heuristics for modeling how agents behave in an emergency event and how the individual behavior aggregates into a variety of patterns of life. In this paper will present briefly our agent-based model being used to characterize the population’s reaction to a Nuclear Weapon of Mass Destruction (NWMD) event in the New York City region. Agents are modeled commuting on work-day schedules before the explosion of a small (10Kt) nuclear device. After the explosion, agents respond to signals in their environment and make decisions based on prioritization of safety for themselves and those in their networks. The model methodology demonstrates how social networks can be integrated into an agent-based model and act as a basis for decision-making, and preliminary simulations show how agents potentially respond to a NWMD event with measurable changes in location and network formations over space and time. 

Keywords: Agent-Based Model, Human Behavior, Social Networks, Emergency, Disaster Response, Nuclear Weapon of Mass Destruction.
Various patterns of commuting behavior representing daily routines of the individual agents.

Full Reference:
Burger, A. G., Kennedy, W.G., Crooks, A.T., Jiang, N. and Guillen-Piazza, D. (2019), Modeling Society Reacting to a Nuclear Weapon of Mass Destruction Event, 2019 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Washington DC. (pdf)

Saturday, June 08, 2019

A Semester of CSS 645: Spatial Agent-based Models of Human-Environment Interactions

This last Spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students were expected to complete a end of semester project, in this case, develop an agent-based model that explores some aspect of related to the course theme of human-environment interactions.  For several of the students this was their first exposure to either agent-based modeling or utilizing geographical information in the modeling process. In the movie below a selection of these projects can be seen. The projects ranged from migration, evacuation modeling during a natural disaster, gerrymandering, the spread of diseases, recidivism, Commons problems to that of urban decline. As can be seen the models ranged from abstract spatial representations to those utilizing geographical information as a foundation of their artificial worlds. Many of the models where created using NetLogo.

I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation in the class.  

Wednesday, May 29, 2019

Postdoctoral Research Fellow in Urban Simulation

The George Mason University, Department of Geography and Geoinformation Science (GGS), in the College of Science invites application for a Postdoctoral Research Fellow position beginning August 1st, 2019. The project, which is supported by the Defense Advanced Research Projects Agency (DARPA), is jointly conducted by Andreas Züfle, Dieter Pfoser, and Andrew Crooks at GMU, and by Carola Wenk at Tulane. George Mason University has a strong institutional commitment to the achievement of excellence and diversity among its faculty and staff, and strongly encourages candidates to apply who will enrich Mason’s academic and culturally inclusive environment.


The primary job responsibilities of this position consist of the design, development and refinement of an agent-based simulation framework of urban areas. For this purpose, we are using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java) that has been developed at GMU. Using MASON, new agent logic will have to be implemented, thus creating agents that use socially plausible rules to traverse their simulated world, and to interact with other agents. This project has started in Spring 2018, and such a simulation has already been developed. A main responsibility will be to implement more complex agent logic efficiently, thus allowing more agents to make more complex decisions, find shortest paths between locations, and interact with their simulated world, at the same time. For this purpose, implemented algorithms will need to be highly parallelizable, thus allowing to scale simulation via distribution among computing clusters located at GMU and Tulane. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

Required Qualifications:
  • Ph.D. in computer science, modeling and simulation, or closely related field;
  • Experience with Agent-Based Modeling and social science simulation;
  • Excellent written communication skills demonstrated by prior publications; and
  • A track record that demonstrates the ability to work well with interdisciplinary research teams.
Preferred Qualifications:
  • Strong programming skills in Java.
More Details: 

For more details and how to apply see:

Friday, May 24, 2019

Call for Papers: GeoSim 2019

The 2nd International Workshop on Geospatial Simulation (GeoSim) focuses on all aspects of simulation as a general paradigm to model and predict spatial systems and generate spatial data. New simulation methodologies and frameworks, not necessarily coming from the SIGSPATIAL community, are encouraged to participate. Example topics include, but are not limited to:
  • Urban Simulation
  • Agent Based Models for Spatial Simulation
  • Multi-Agent Based Spatial Simulation
  • Big Spatial Data Simulation
  • Spatial Data/Trajectory Generators
  • Road Traffic Simulation
  • Environmental Simulation
  • GIS using Spatial Simulation
  • Interactive Spatial Simulation
  • Spatial Simulation Parallelization and Distribution
  • Geo-Social Simulation and Data Generators
  • Social Unrest and Riot Prediction using Simulation
  • Spatial Analysis based on Simulation
  • Behavioral Simulation
  • Verifying, and Validating Spatial Simulations
  • Applications for Spatial Simulation
The workshop seeks high-quality full (8 pages) and short (4 pages) papers that will be peer-reviewed. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference (which will be in Chicago, Illinois), as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library. 

This workshop should also be of interest to everyone who works with spatial data. The simulation methods that will be presented and discussed in the workshop should find a wide application across the community by producing benchmark datasets that can be parameterized and scaled. Simulated data sets will be made available to the community via the website.

More information about the workshop along with key dates is available at:

Wednesday, May 22, 2019

Guest Editorial for Spatial Agent-based Models: Current Practices and Future Trends

Over the last few years we have seen spatial agent-based modeling beginning to bridge the gap from cautious early adoption towards general acceptance within the geographical sciences. One of the key features that has contributed to this is its ability to represent individual characteristics and behaviors.

In order to capture this evolution a while ago, Alison Heppenstall and myself  put out a call for papers that not only asked for papers that looked at current trends in agent-based modeling but also  for those  that highlighted and addressed the advances and challenges that researchers working within the area of spatial agent-based models face. We are happy to say this call is now over and in the current issue of GeoInformatica there are 6 great papers (full citations and links are provided below) and along with a editorial. The papers present not only a great synthesis of the current practices but also several of the key advances and challenges within the realm of spatial agent-based modeling are brought to bare. 

Several common themes will become apparent when reading the articles. All the authors were in agreement that while there has been a noticeable uptake in agent-based modeling, more work is needed to bridge the gap to acceptance as a standard tool within the spatial sciences (e.g. Polhill et al., 2019). Data (variable quality and availability) was an issue that was discussed by almost all of the authors, particularly how to translate high quality data into models to create behavioral rules and the use of novel forms of data to calibrate an empirical model (e.g. Crols and Malleson, 2019). How to represent and simulate behavior in agent-based models was also a recurrent issue with two papers discussing how approaches borrowed from machine learning can be used to improve the representation of behavior (e.g. Runck et al., 2019; Abdulkareem et al., 2019). How to create models that could scale from the micro to macro was another theme with the point being made that current agent-based modeling architectures do not foster models that are easily translatable to a regional or global context (e.g. Taillandier et al., 2019), nor are interactions across scales adequately addressed in most models (e.g. Lippe et al., 2019). The papers also highlight that to cross the bridge from novel tool to full acceptance as a standard tool within the geographical sciences, spatial agent-based modeling still has some way to go. However, the papers in this special issue can therefore be seen as a stepping stone towards this.

Papers in the Special Issue:

Our Editorial:
Heppenstall, A. and Crooks, A.T. (2019), Guest Editorial for Spatial Agent-based Models: Current Practices and Future Trends, GeoInfomatica. 23(2): 243-268 (pdf)

Friday, April 26, 2019

Computational Social Science of Disasters: Opportunities and Challenges

Figure 1: Relation of computational social science of
disasters (CSSD) with other fields.
Past posts have discussed or demonstrated how  computational social science (CSS) (i.e. the study of social science through computational methods) can be utilized explore disasters or diseases but this has not really been  formalized.  To this end, Annetta Burger, Talha Oz, William Kennedy and myself have just had a paper published in Future Internet entitled "Computational Social Science of Disasters: Opportunities and Challenges". In the paper we introduce computational social science of disasters (CSSD). CSSD is defined as an approach to explain the social dynamics of disasters via computational means by adopting the relevant parts of CSS, social sciences in disaster, and crisis informatics as depicted in Figure 1. Specifically, we briefly review the domains and the approaches of each of the traditional social science disciplines to disasters (e.g. sociology, psychology, anthropology, political science, and economics). Next we describe the fields of CSS and crisis informatics before discussing the components of CSSD. We highlight some exemplar studies which capture certain elements of CSSD along with the challenges and opportunities it brings to the study of disasters. If you would like to find out more, below is the abstract to the paper along with the full reference and link to the paper.

Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

Keywords: Disasters; Computational Social Science; Crisis Informatics; Disaster Modeling, Web 2.0; Social Media; Big Data; Volunteered Geographical Information; Crowdsourcing.
Figure 2: Interactions of data analysis, computational models, and social theory
in computational social science of disasters.

Full Reference:
Burger, A., Oz, T., Kennedy, W.G. and Crooks, A.T. (2019), Computational Social Science of Disasters: Opportunities and Challenges, Future Internet, 11(5): 103; (pdf)

Friday, March 29, 2019

Drafting Agent-Based Modeling into Basketball Analytics
Readers of this blog might find this post a little  out of left field (sorry I could not find a better analogy) as it about basketball and therefore the ball is in your court if you want to keep reading.

At the upcoming SpringSim conference Matthew Oldham and myself  just had a paper accepted entitled "Drafting Agent-Based Modeling into Basketball Analytics" where we take a shot at modeling basketball. Why you might ask? The rational is that sports analytics (SA) is a multi-million dollar industry but to date little attention has been given to agent-based modeling (ABM) even though sports can be viewed as a complex adaptive system (Matthew on his site has a great write up of this). To explore this notion we built an agent-based model (utilizing NetLogo 3D) which captures the basic dynamics of a basketball game. In order to calibrate the processes within the model we utilized 17 seasons (2000 to 2016) of individual game data from the National Basketball Association (NBA). The data collected included; game scores, winning margins, field goal attempts, the percentage of field goals made, rebounds, steals, and turnovers. From the NBA game data, density functions were calculated to aid calibrating certain aspects of the model.  Via a set of experiments, the model indicates that an increased belief in the franchise player (think Michael Jordan) leads to increased scoring action, but a belief in the hot-hand had a minor effect. This results comes from the ability of agent-based models to identify the micro-interaction of agents responsible for generating system level outcomes and thereby, demonstrating the utility of ABM to SA.

Below, you a can read the abstract of the paper, along with some figures outlining the play cycle in the model, some some results of the varying NBA game metrics compared to the model, a movie of the graphical user interface of the model during a representative game. Finally at the end of the post you can find link to the model and the actual paper. 
The growth of sports analytics (SA) has raised numerous research topics across a variety of sports, including basketball. Agent-based modeling (ABM) has great potential to assist and inform SA, but to date it has not been utilized. To support the use of ABM in SA, a model of a basketball game, which considers most fundamentals of play, is presented. Additionally, player behavior is partially predicated on assessing the length of a player’s shooting streak (testing the “hot-hand” effect) and the consideration a team gives to a streak and their franchise player. The model’s output is used to calibrate and validate it against statistics from the National Basketball Association (NBA). Via a set of experiments, the model indicates that an increased belief in the franchise player leads to increased scoring action, but a belief in the hot-hand a minor effect. Thereby, demonstrating the utility of ABM to SA, thus opening a new research field.

Keywords: agent-based modeling, sports analytics, hot-hand effect.
Figure 2: The play cycle of the model.

Figure 3: Distribution of the varying NBA game metrics compared to the model.

The model along with a detailed Overview, Design concepts, and Details (ODD) document can be found at:

Full Reference:
Oldham, M and Crooks, A.T. (2019) Drafting Agent-Based Modeling into Basketball Analytics, 2019 Spring Simulation Conference (SpringSim’19), Tucson, AZ. (pdf)

Wednesday, February 20, 2019

ABM and GIS Book Launch

Last night, thanks to CASA, saw launch of our book "Agent-Based Modelling and Geographical Information Systems: A Practical Primer" alongside Mike Batty's new book "Inventing Future Cities" which to quote from Mike's site is about how:
"We cannot predict future cities, but we can invent them.

Cities are largely unpredictable because they are complex systems that are more like organisms than machines. Neither the laws of economics nor the laws of mechanics apply; cities are the product of countless individual and collective decisions that do not conform to any grand plan. They are the product of our inventions; they evolve. In Inventing Future Cities, I explore what we need to understand about cities in order to invent their future  This book attempts to communicate many of the ideas concerning science, prediction and complexity that have been useful in thinking about cities and urban planning in the last fifty years."
For more info, or to buy Inventing Future Cities, see Mike’s blog.


Friday, January 18, 2019

Agent-based Modelling and Geographical Information Systems: A Practical Primer

Its been a long time in the making but now "Agent-Based Modelling and Geographical Information Systems: A Practical Primer" has been published by Sage. We (Nicolas Malleson, Ed Manley, Alison Heppenstall and myself) approached this book from two standpoints. First, to provide a synthesis of the underpinning ideas, techniques and frameworks for integrating agent-based modelling and geographical information systems (GIS). Second, building on our experiences of teaching at various levels, to provide a practical set of information for the development of agent-based models for geographical systems.

From these two standpoints we have developed a book that provides a practical primer in the integration of agent-based modelling and geographical information systems. In outlining the subject we cover many examples of geographical phenomena, from linking the individual movements of pedestrians to aggregate patterns of urban growth, to the integration of social networks into modelling mobility. Through this text, we hope  the reader will understand how the field has developed, how agent-based models are different from other modelling approaches, and the future challenges we see lying ahead.
By using sample code and data (all of which can be found on the accompanying website we provide the reader with many of the basic building blocks for constructing agent-based models linked to geographical information systems. Throughout the book we use the software package NetLogo, as it provides an easy route to learn and build agent-based models (although in the appendix we provide links to other models created in other platforms).

For more information visit and if you wish to buy a copy you can: Amazon or Sage Publishing. We hope you enjoy it. 
Figure 6.1 Abstracting from the real world to a series of layers to be used in the artificial world
upon which the agent-based model is based.
Full Reference:
Crooks, A.T., Malleson, N., Manley, E. and Heppenstall, A.J. (2019), Agent-based Modelling and Geographical Information Systems: A Practical Primer, Sage, London, UK.

Wednesday, January 02, 2019

Models from Teaching CSS Fall 2018

Most of the time when I teach a class instead of setting a final exam, I ask the students to carryout an end of semester research project. In my Introduction to Computational Social Science class this project entails the development of a computational model in an area of  interest to the student . The aim of this exercise is to cement what the students have (hopefully) learnt during the semester. I.e.: 
  • to understand the motivation for the use of computational models in social science theory and research; 
  • to learn about the variety of CSS research programs across the social science disciplines; 
  • to understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.
Below you can see some of the outputs from these projects this last fall. The models range in type from agent-based models, microsimulation to system dynamics models applied to a variety of topics from voting and political parties, the peer effects of students, urban decline, employment growth and rise and fall of civilizations and many other topics along the way.