Traditional data on influenza vaccination has several limitations: high cost, limited coverage of underrepresented groups, and low sensitivity to emerging public health issues. Social media, such as Twitter, provide an alternative way to understand a population’s vaccination-related opinions and behaviors. In this study, we build and employ several natural language classifiers to examine and analyze behavioral patterns regarding influenza vaccination in Twitter across three dimensions: temporality (by week and month), geography (by US region), and demography (by gender). Our best results are highly correlated official government data, with a correlation over 0.90, providing validation of our approach. We then suggest a number of directions for future work.

Abstracts of the invited talks presented at the AAAI Fall Symposium on Discovery Informatics: AI Takes a Science-Centered View on Big Data. Talks include  A Data Lifecycle Approach to Discovery Informatics,  Generating Biomedical Hypotheses Using Semantic Web Technologies,  Socially Intelligent Science, Representing and Reasoning with Experimental and Quasi-Experimental Designs, Bioinformatics Computation of Metabolic Models from Sequenced Genomes, Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science,  Predictive Modeling of Patient State and Therapy Optimization, Case Studies in Data-Driven Systems: Building Carbon Maps to Finding Neutrinos,  Computational Analysis of Complex Human Disorders, and Look at This Gem: Automated Data Prioritization for Scientific Discovery of Exoplanets, Mineral Deposits, and More.

Given the challenges of modeling multi-scale social phenomena, hybrids may hold the key to unlocking social complexity dynamics. We introduce hybrid system modeling from engineering, as a means to capture complex dynamics within interacting, multi-scale, and global social systems. Whereby hybrid modeling is used in industrial processes and automated control systems, this research uses world cup soccer tournament simulations to demonstrate successful applications. Agent-based modeling for soccer games and cellular automatons for crowd and bettor emotional reactions are modeled on each side of a playing field. A predator-prey theoretical approach is applied with self-organizing soccer teams represented as predators and the soccer ball as prey. Simulations of multiple soccer tournaments of thirty-two teams were conducted with pre-game betting and without betting as a pseudo-control measure. Tournaments conducted with pre-game betting resulted in the final tournament games having the wining team demonstrating strong defensive playing styles and scoring by a large margin. Divergence of playing styles did not develop in tournaments without pre-game betting. Hybrids offer a means to explore complexity with evolutionary learning by players, corresponding emotional reactions of spectators, and betting interacting, resulting in patterns of emergent behavior and unique evolutionary behavioral responses to complexity.

Human computing promises new capabilities that cannot be easily provided by computing machinery. However, humans are less disciplined than their mechanical counterparts and hence are liable to produce accidental or deliberate mistakes. As we start to develop regimes for identifying and correcting errors in human computation, we find an important model in the computing groups that operated at the start of the 20th century.