Extending information quality assessment methodology: A new veracity/deception dimension and its measures

Abstract

This paper extends information quality (IQ) assessment methodology by arguing that veracity/deception should be one of the components of intrinsic IQ dimensions. Since veracity/deception differs contextually from accuracy and other well-studied components of intrinsic IQ, the inclusion of veracity/deception in the set of IQ dimensions has its own contribution to the assessment and improvement of IQ. Recently developed software to detect deception in textual information represents the ready-to-use IQ assessment (IQA) instruments. The focus of the paper is on the specific IQ problem related to deceptive messages and affected information activities as well as IQA instruments (or tools) of detecting deception to improve IQ. In particular, the methodology of automated deception detection in written communication provides the basis for measuring veracity/deception dimension and demonstrates no overlap with other intrinsic IQ dimensions. Considering several known deception types (such as falsification, concealment and equivocation), we emphasize that the IQA deception tools are primarily suitable for falsification. Certain types of deception strategies cannot be spotted automatically with the existing IQA instruments based on underlying linguistic differences between truth-tellers and liars. We propose the potential avenues for the future development of the automated instruments to detect deception taking into account the theoretical, methodological and practical aspects and needs. Blending multidisciplinary research on Deception Detection with the one on IQ in Library and Information Science (LIS) and Management Information Systems (MIS), the paper contributes to IQA and its improvement by adding one more dimension, veracity/deception, to intrinsic IQ.

Veracity Roadmap: Is Big Data Objective, Truthful and Credible?

“…Objectivity-subjectivity variation in many ways depends on its context, since context determines the types of linguistic cues used to express objective or subjective opinions (Hirst, 2007). To quantify deception levels in big data, we propose to use the existing automated tools on deception detection (see overview in (Rubin & Conroy, 2012;Rubin & Lukoianova, Forthcoming;Rubin & Vashchilko, 2012). For credibility assessment, we propose to use blogs that contain trust evaluation of published content or entire websites.…”

“…The goal of Ott et al (2011) was to identify fake reviews of products and services on the Internet. Several software programs (Chandramouli and Subbalakshmi 2012, Ott et al 2011, Moffit andGiboney 2012) were evaluated in our previous work (Rubin and Vashchilko 2012). The majority of the software offers on-line evaluation tools without algorithm provision (Chandramouli and Subbalakshmi 2012), or with the provision of API (Ott et al 2011, Moffit andGiboney 2012), and customizable dictionaries (Moffit and Giboney 2012).…”

“…The majority of the software offers on-line evaluation tools without algorithm provision (Chandramouli and Subbalakshmi 2012), or with the provision of API (Ott et al 2011, Moffit andGiboney 2012), and customizable dictionaries (Moffit and Giboney 2012). For discussion of advantages and disadvantages of various approaches and the comparative evaluation details of the software capabilities (Rubin & Vashchilko, 2012). Further analysis of similar deception detection tools is needed to determine which of them are particularly suitable for detection deception in big data to establish its veracity.…”

Deception Detection and Rumor Debunking for Social Media

“…These tools use not only linguistic cues to resolve expression uncertainty problems, but also establish the factuality of events and statements using experts' opinions and additional necessary sources. For an overview of related content annotation and automation efforts, see (Morante and Sporleder, 2012) and Pustejovsky, 2009, Sauri andPustejovsky, 2012 Rubin and Vashchilko, 2012).…”

Truth and deception at the rhetorical structure level

“…In recent years, the question of whether there is deception in textual information has been addressed algorithmically. Although deception has been traditionally studied outside of the library and information science field (in disciplines such as psychology and communication studies), automated deception detection methods are now of interest in information science because of their application (a) in tools to support and enhance human abilities in discerning information from misinformation, concepts originally linked by Fox () ; (b) as an additional metric for information quality assessment discussed by Rubin and Vashchilko () and Lukoianova and Rubin (), and (c) as a measure for the assessment of source credibility, the notion traditionally seen in library and information science juxtaposed to cognitive authority such as that of Rieh (). Deceptive pieces of information, even authoritatively stated, can mislead or misinform information users, negatively affecting the outcome of their decision making.…”