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Text-analytic methods have become increasingly popular in cognitive science for understanding differences in semantic structure between documents. However, such methods have not been widely used in other disciplines. With the aim of disseminating these approaches, we introduce a text-analytic technique (Contrast Analysis of Semantic Similarity, CASS, www.casstools.org), based on the BEAGLE semantic space model (Jones &amp; Mewhort, Psychological Review, 114, 1-37, 2007) and add new features to test between-corpora differences in semantic associations (e.g., the association between democrat and good, compared to democrat and bad). By analyzing television transcripts from cable news from a 12-month period, we reveal significant differences in political bias between television channels (liberal to conservative: MSNBC, CNN, FoxNews) and find expected differences between newscasters (Colmes, Hannity). Compared to existing measures of media bias, our measure has higher reliability. CASS can be used to investigate semantic structure when exploring any topic (e.g., self-esteem or stereotyping) that affords a large text-based database.

Exploring media bias with semantic analysis tools: validation of the Contrast Analysis of Semantic Similarity (CASS)