Lifting the lid on manipulative website contents: a framework mapping contextual and informational feature combinations against associated social cognitive vulnerabilities

Date published

2024-02-15

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Wiley

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Article

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1751-9004

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Citation

MacLean P, Cahillane M, Smy V., (2024) Lifting the lid on manipulative website contents: a framework mapping contextual and informational feature combinations against associated social cognitive vulnerabilities. Social and Personality Psychology Compass. Volume 18, Issue 2, February 2024, Article number e12947

Abstract

Website contents can be designed to influence individual and group decision-making for social, political or financial gain. A novel working theoretical framework was developed to provide insights into where website contents have been designed to exploit common cognitive vulnerabilities (CVs) amongst audiences; a form of social cognitive hacking. A literature synthesis on CVs, website content design and credibility identified features on a context credibility (CC) dimension that were mapped against features on an information validity (IV) dimension. Alignment of CC and IV feature pairings with CVs enables evaluation and interpretation of nuanced website content for identification of attempted influence undetectable by machine algorithms. Subjective responses to prompts about features on each dimension generate pairs of numerical values. The value pairs indicate within a quad-graph the possible presence and extent of CV exploitation. Each value pair can be traced to the underlying CC-IV features and CVs being targeted. External prima facie assessment suggests the framework can form the basis for standardised human analysis of website contents. Application of the framework provides insights into what and how feature combinations may be manipulated for CV exploitation. It has potential for application in fields such as intelligence analysis, education, and marketing.

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Github

Keywords

cognitive vulnerabilities, context credibility, information validity, online cognitive hacking

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Attribution 4.0 International

Funder/s

Defence Science and Technology Laboratory, Grant/Award Number: DSTLX1000133521

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