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Advertisers, marketers, and researchers all wrestle with finding the personal human presence in text-based online communication. Social features are present, if subtle. Users of online research must work to identify when and how unseen writers are or are not strongly committed to what they have just written, and must work even harder to keep from reading themselves and their own biases into the text being analyzed. Our discussion illustrates how stance-shift analysis, as a type of quantitative content analysis, maximizes understanding of online communication through its identification of key language patterns that highlight consumer evaluation, attitudes, and attribution of behaviors or opinions.
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