When Certainty Is an Artifact: Keyword Lexicon Blindness and the (Mis)Measurement of Rhetorical Stance
arXiv:2606.26062v1 Announce Type: new Abstract: Can a statistically significant, large-effect-size finding in computational social science be entirely an artifact of the measurement instrument? We present a case where the answer appears to be yes. Analyzing 85 interviews across four public intellectuals (2016--2026), we find a robust negative-affect/emphatic-certainty lexical co-occurrence pattern under keyword-based scoring ($r = 0.72$--$0.93$, $p < 0.01$ for all four speakers). Replacing keyword counting with LLM-based zero-shot semantic classification on the complete diarized corpus (32,625
The proliferation of AI-based text analysis tools makes understanding their limitations increasingly critical as they become widely adopted.
This research highlights a fundamental flaw in current keyword-based AI measurement instruments, suggesting many prior social science findings reliant on such methods may be artifacts.
The reliability of purely keyword-based analysis for complex rhetorical or affective states is significantly undermined, necessitating a shift towards more sophisticated, context-aware AI methods like LLM-based semantic classification.
- · Developers of LLM-based semantic analysis tools
- · Researchers employing advanced AI for social science
- · Organizations prioritizing nuanced data interpretation
- · Researchers reliant on keyword-based sentiment analysis
- · Companies offering only keyword-driven text analytics
- · Disciplines with foundational work built on simple lexical co-occurrence
Previous research based on keyword lexicons may need re-evaluation, potentially overturning established findings in social sciences.
Increased demand for, and development of, more robust and context-sensitive AI methods for analyzing human language and affect.
A potential 'recalibration' in how academic and industry insights are generated and trusted when derived from AI text analysis, leading to new best practices.
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Read at arXiv cs.CL