Wednesday, July 18, 2018

Online Vaccination Discussion and Communities in Twitter

Continuing on our work of exploring health related issues in social media, Xiaoyi Yuan and myself had a paper accepted at the 9th International Conference on Social Media and Society. In our paper entitled: "Examining Online Vaccination Discussion and Communities in Twitter"  we examined the communication patterns of anti-vaccine and pro-vaccine users on Twitter by studying the retweet network from 660,892 tweets related to the measles, mumps, and rubella (MMR) vaccine published by 269,623 users using supervised learning to identify clusters of users based on their opinions (i.e. a pro-vaccine, anti-vaccine, or neutral user). 

The overall methodology can be seen in Figure 1 and more details can be found in the paper. Our data was collected using the GeoSocial Gauge System, however, since tweets are short and their content diverse, the data corpus needed to be cleaned so that the tweets could then be converted to features (e.g., unigrams or bigrams). After which we were able to use such features for training a variety of classifiers (i.e., logistic regression, support vector machine (linear and non-linear kernel), k-nearest neighbors, nearest centroid, and Na├»ve Bayes) to identify opinion groups. After this, we moved from on from identifying each user’s opinion to construct a retweet network in order to understand how in-group and cross-group communicate in the committees detected via retweet network. By carrying out this analysis we discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group, while users with neutral opinions are distributed across communities. Below you can read our abstract, see some results from our study and the full reference (and link) to the paper.


Figure1: Steps used in our study to unveil the communication patterns of pro-vaccine and anti-vaccine users on Twitter
 Abstract:
Many states in the US allow a “belief exemption” for measles, mumps, and rubella (MMR) vaccines. People’s opinion on whether or not to take the vaccine could have direct consequences in public health— once the vaccine refusal of a group within a population is higher than what herd immunity can tolerate, a disease can transmit fast causing large scale of disease outbreaks. Social media has been one of the dominant communication channels for people to express their opinions of vaccination. Despite governmental organizations’ effects of disseminating information of vaccination benefits, anti-vaccine sentiment is still gaining its momentum, especially on social media. This research investigates the communicative patterns of anti-vaccine and pro-vaccine users on Twitter by studying the retweet network from 660,892 tweets related to MMR vaccine published by 269,623 users after the 2015 California Disneyland measles outbreak. Using supervised learning, we classified the users into anti-vaccination, neutral to vaccination, and pro-vaccination groups. Using a combination of opinion groups and retweet network structural community detection, we discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group, while users with neutral opinions are distributed across communities. For most cross-group communication, it was found that pro-vaccination users were retweeting anti-vaccination users than vice-versa. The paper concludes that anti-vaccine Twitter users are highly clustered and enclosed communities, and this makes it difficult for health organizations to penetrate and counter opinionated information. We believe that this finding may be useful in developing strategies for health communication of vaccination and overcome some the limits of current strategies.

Key Words: Anti-vaccine movement, Twitter, social media, opinion classification
Figure 2: Network visualizations of the four largest communities. A: is colored by the belonging to a specific structural community and; B: is colored by belonging to opinion groups

Figure 3: Distributions of opinion groups in the four largest structural community

Full Reference:
Yuan, X. and Crooks, A.T. (2018), Examining Online Vaccination Discussion and Communities in Twitter, Proceedings of the 9th International Conference on Social Media and Society, Copenhagen, Denmark, pp 197-206. (pdf)

Wednesday, July 04, 2018

MASON Update

At the upcoming Multi-Agent-Based Simulation (MABS) workshop, we have a paper entitled "The MASON Simulation Toolkit: Past, Present, and Future" in which we discuss MASON's development history, its design and (probably more interesting) where MASON is going. This includes:
  1. Making it more robust (i.e. easier to run parameter tests), 
  2. Making it distributed in order to  run large scale models including geographical explicit ones along for optimization and validation purposes.
  3. Making it more coder-friendly by adding code templates that allow users to generate code skeletons for common MASON patterns and a way to easily record outputs and statistics.
  4. Making it more community-friendly by hopefully developing a special online repository to enable researchers to distribute models as jar files along with education aids and examples. Relating to this last point we have added a number of example models (code and data) from our own research to GitHub, see: https://github.com/eclab/mason/tree/master/contrib/geomason/sim/app/geo and the data to run the models is either there or here https://cs.gmu.edu/~eclab/projects/mason/extensions/geomason/geodemodata.zip (note this is 1.5 GB).
Below you can read the abstract from the paper along with a link to the paper itself.

Example Applications of MASON

Abstract
MASON is a widely-used open-source agent-based simulation toolkit that has been in constant development since 2002. MASON’s architecture was cutting-edge for its time, but advances in computer technology now offer new opportunities for the ABM community to scale models and apply new modeling techniques. We are extending MASON to provide these opportunities in response to community feedback. In this paper we discuss MASON, its history and design, and how we plan to improve and extend it over the next several years. Based on user feedback will add distributed simulation, distributed GIS, optimization and sensitivity analysis tools, external language and development environment support, statistics facilities, collaborative archives, and educational tools.

Keywords: Agent-Based Simulation, Open Source, Library

Full Reference:
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, 19th International Workshop on Multi-Agent-Based Simulation (MABS2018), Stockholm, Sweden. (pdf)

Available on Github


This research is supported by the National Science Foundation (Grant 1727303).

Wednesday, June 13, 2018

Call for Papers: GeoSim’18



The GeoSim’18 workshop 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. Also, this workshop is 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.

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, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

Example topics include, but not limited to:
  • Applications for Spatial 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
  • Geoinformation Systems 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
  • Urban Simulation
  •  
Important Dates:
  • Submission deadline: August 20, 2018
  • Notification: September 20, 2018
  • Workshop date: November 06, 2018
For more information please visit www.geosim.org

https://www.dropbox.com/s/lgt6ip1u9lxvgwa/GeoSim18_cfp_final.pdf?dl=0

Tuesday, May 29, 2018

Spatial Agent-based Models of Human-Environment Interactions: Spring 2018

During the past spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students are 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. In the movie below is a selection of these projects can be seen. The projects ranged from urban growth, housing markets, the adoption of solar energy, employment opportunities, populations at risk from terrorism, commuting, to the spread of diseases. Many of the models were done in NetLogo, MASON and some in Python including using MESA.




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

Monday, April 09, 2018

Predicting Rice Cropping Patterns around Poyang Lake, China using a Cellular Automata Model

http://mason.gmu.edu/~qtian2/QingTianSummary.html
Normally, on this blog, the focus is on agent-based modeling and GIS. However, I am not agnostic to other modeling approaches especially cellular automata (CA) modeling (which I have written about in the past).  To this end, Rui Zhang, Qing Tian, Luguang Jiang, Shuhua Qi, Ruixin Yang and myself recently had a paper published in Land Use Policy entitled: "Projecting Cropping Patterns around Poyang Lake and Prioritizing Areas for Policy Intervention to Promote Rice: A Cellular Automata Model" In the paper we explore current land use patterns in the Poyang Lake Region (PLR) of China. Specifically, we focus on current rice production in the region and what this might look like in the future (especially the impact of farmland consolidation) by using an CA model (built on the DINAMICA EGO platform). Below you can read the abstract to our paper, along with some figures, outlining our study area, the model design and development, along with observed current day and predicted rice cropping patterns around Poyang Lake. Finally at the bottom of the post I provide the full reference and a link to the paper.

Abstract:
Rural households’ cropping choices are increasingly influenced by nonfarm activities across the developing world, raising serious concerns about food security locally and globally. In China, rapid urbanization has led to agricultural decline in some regions. To stimulate agriculture, the Chinese government has recently increased its effort in farmland consolidation by providing special support to large farms in an attempt to address land-use inefficiency associated with small farming operations in rural China. Focusing on the Poyang Lake Region (PLR), we develop a Cellular Automata (CA) model to explore future agricultural land use and examine the impact of farmland consolidation. PLR is an important rice production base in Jiangxi Province and China. In PLR rice can be grown once a year on a plot, called one-season rice, or twice a year on the same plot, called two-season rice. Our CA model simulates the transition between one-season and two-season rice. Emphasizing distributional differences in the region, we use the modeling results to identify five areas where rice cultivation is (i) relatively stable for one-season rice, (ii) more likely to be one-season rice, (iii) of equal probability for either type, (iv) more likely to be two-season rice, and (v) relatively stable for two-season rice. We then explore the characteristics of these areas in terms of biophysical and geographical environments to provide further insights into how the government may prioritize areas for interventions to effectively promote food production and environmental sustainability. The analysis also indicates a positive effect of farmland consolidation on promoting rice production.

Keywords: Agricultural Land Use; Cellular Automata; Food Security; Environmental Sustainability; Farmland Consolidation; China.
Poyang Lake Region. The left map shows its location in China. Rice cropping patterns shown on the right map were interpreted from Landsat images in 2013.
Model design and development

Rice cropping patterns around Poyang Lake. The map on the left is observed land use in 2013 and on the right prediction for 2033.

Full Reference:
Zhang, R., Tian, Q., Jiang, L., Crooks, A.T., Qi, S. and Yang, R. (2018), Projecting Cropping Patterns around Poyang Lake and Prioritizing Areas for Policy Intervention to Promote Rice: A Cellular Automata Model, Land Use Policy, 74: 248-260. (pdf)
As always, any thoughts or comments are most welcome.