Extracting the patterns of truthfulness from political information systems in Serbia

Abstract

In modern information societies, there are information systems that track and log parts of the ongoing political discourse. Due to the sheer volume of the accumulated data, automated tools are required in order to enable citizens to better interpret political statements and promises, as well as evaluate their truthfulness. We propose an approach to use the established machine learning and data mining techniques for analyzing annotated political statements and promises available via the Serbian Truth-ometer (Istinomer) system in order to extract and interpret the hidden patterns of truthfulness and deceit. We perform standard textual processing and topic extraction and associate topical truthfulness profiles with the promise makers, for pattern discovery and prediction. Prevailing trends in Serbian political discourse emerge as strong association rules where truthfulness is set as the target variable. The evaluated set of standard content-based prediction models exhibit a bias towards the negative outcomes, due to an overall low truthfulness rate in the data. Our results demonstrate that it is possible to use data mining within political information systems for generating insights into the workings of governments.