Early detection of a disease outbreak using pre-diagnostic textual data is available in biosurveillance systems with the integration of data such as chief complaints. Social media has been identified as an additional pre-diagnostic data source of interest. Textual data analysis in public health is usually based on a keyword search and often involves a complex Boolean combination of terms that produce results with many false alarms. Epidemiologists may wish to query the data differently based on the event of interest, yet the process is laborious to weed out uninteresting content. Specialized detectors that decide on the topical relevance of keyword search usually require developers to adapt methods to new uses, which is a time- and cost-prohibitive activity. Users need the ability to rapidly build text content detectors on their own.
Objective
To demonstrate a framework for user-customizable text processing that can improve the efficiency and effectiveness of mining text for biosurveillance, with initial application to Twitter.