Domain-independent deception: a new taxonomy and linguistic analysis

TL;DR

This paper introduces a new way to understand and detect deception across different online contexts using language patterns and machine learning.

Contribution

A new computational definition of deception, a taxonomy, and evidence of cross-domain knowledge transfer in detecting deception.

Findings

Common linguistic cues for deception were identified across various domains.

Significant evidence of knowledge transfer was found between different forms of deception.

A new real-world dataset was created to study deception comprehensively.

Abstract

Internet-based economies and societies are drowning in deceptive attacks. These attacks take many forms, such as fake news, phishing, and job scams, which we call “domains of deception.” Machine learning and natural language processing researchers have been attempting to ameliorate this precarious situation by designing domain-specific detectors. Only a few recent works have considered domain-independent deception. We collect these disparate threads of research and investigate domain-independent deception.

First, we provide a new computational definition of deception and break down deception into a new taxonomy. Then, we briefly mention the debate on linguistic cues for deception. We build a new comprehensive real-world dataset for studying deception. We investigate common linguistic features for deception using both classical and deep learning models in a variety of situations including cross-domain experiments.

We find common linguistic cues for deception and give significant evidence for knowledge transfer across different forms of deception.

We list several directions for future work based on our results.

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