Purpose: This study aims to recognize the challenge of identifying deceptive information and provides a framework for thinking about how we as humans negotiate the current media environment filled with misinformation and disinformation. Design/methodology/approach: This study reviews the influence of Wilson's (2016) General Theory of Information Behavior (IB) in the field of information science (IS) before introducing Levine's Truth-Default Theory (TDT) as a method of deception detection. By aligning Levine's findings with published scholarship on IB, this study illustrates the fundamental similarities between TDT and existing research in IS. Findings: This study introduces a modification of Wilson's work which incorporates truth-default, translating terms to apply this theory to the broader area of IB rather than Levine's original face-to-face deception detection. Originality/value: False information, particularly online, continues to be an increasing problem for both individuals and society, yet existing IB models cannot not account for the necessary step of determining the truth or falsehood of consumed information. It is critical to integrate this crucial decision point in this study's IB models (e.g. Wilson's model) to acknowledge the human tendency to default to truth and thus providing a basis for studying the twin phenomena of misinformation and disinformation from an IS perspective. Moreover, this updated model for IB contributes the Truth Default Framework for studying how people approach the daunting task of determining truth, reliability and validity in the immense number of news items, social media posts and other sources of information they encounter daily. By understanding and recognizing our human default to truth/trust, we can start to understand more about our vulnerability to misinformation and disinformation and be more prepared to guard against it.