• Robert Thomson
  • Christopher L. Dancy
  • Ayaz Hyder
  • Halil BisginEmail author

We are pleased to present this special issue of the best of the 11th Annual Social Computing, Behavioral Modeling and Prediction/Behavior Representation in Modeling Simulation (SBP-BRIMS) Conference in the Journal of Computational and Mathematical Organization Theory. The goal of the SBP-BRIMS Society is to build a community of computational social science scholars by fostering interaction between members of the academic, corporate, government, and military communities that are interested in understanding, forecasting, and impacting human socio-cultural behavior. In 2018, our conference featured two challenge problems: one on opioid epidemic and another on disinformation campaigns. In addition, we brought in a plenary panel to discuss the burgeoning field of social cyber security. This panel took a step towards defining this new science, its theories, its methods, and its scientific culture in a way that does not give priority to either social science or computer science, but instead understand how each field is dependent on the other. Despite decades of work in this area, this new scientific field is still in its infancy. To meet this charge and to move this science to the next level, this community must meet the following three challenges: deep understanding, socio-cognitive reasoning, and re-usable computational technology. Fortunately, as the papers in this volume illustrate, this community is poised to answer these challenges.

A total of 85 papers were submitted to the conference as regular track submissions. Of these, 18 were accepted as full papers for an acceptance rate of 21.2% and 27 were accepted as short papers for an acceptance rate of 52.9%. In this special issue, we recognize the best conference papers, as well as the best challenge problem submissions.

The papers in this special issue report work that spans computational models of behavior, studying disinformation and fake news under different contexts, and the dark web as a way to track accidental overdose. Orr et al. (this issue) develop a paradigm for multiscale computational models to simulate behavior, from individuals to society. The winner of the challenge problem on opioid epidemic (Bates et al. this issue) use synthetic data and geospatial methods to better forecast the opioid epidemic, while runner-up (Lokala et al. this issue) use the dark web to track trends in supply of opioids. To examine disinformation (Babcock et al. this issue) focus on a particular community, while (Hussain et al. this issue) focus on the integration of several platforms. Shu et al. (this issue) create discuss their FakeNewsTracker that allows the collection, detection, and visualization of fake news. Finally, our best student paper winner (Beskow et al. this issue) reports work on detecting bots on social media by discovering a common lexical feature in their usernames/handles. The papers in this special issue represent a wide-range of topics.

These seven papers represent some of the breadth of work presented at the 11th Annual SBP-BRIMS Conference (2018). In the future, SBP-BRIMS will continue to build communities that foster research in computational social science and encourage interaction between academic, corporate, military, and government communities.


Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Army Cyber InstituteUnited States Military AcademyWest PointUSA
  2. 2.Department of Computer ScienceBucknell UniversityLewisburgUSA
  3. 3.Division of Environmental Health SciencesThe Ohio State UniversityColumbusUSA
  4. 4.Department of Computer Science, Engineering, and PhysicsUniversity of Michigan-FlintFlintUSA

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