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To explore the feasibility of social media for message testing, this study connects favorable viewer responses to antismoking videos on YouTube with the videos' message characteristics (message sensation value [MSV] and appeals), producer types, and viewer influences (viewer rating and number of viewers). Through multilevel modeling, a content analysis of 7,561 viewer comments on antismoking videos is linked with a content analysis of 87 antismoking videos. Based on a cognitive response approach, viewer comments are classified and coded as message-oriented thought, video feature-relevant thought, and audience-generated thought. The three mixed logit models indicate that videos with a greater number of viewers consistently increased the odds of favorable viewer responses, while those presenting humor appeals decreased the odds of favorable message-oriented and audience-generated thoughts. Some significant interaction effects show that videos produced by laypeople may hinder favorable viewer responses, while a greater number of viewer comments can work jointly with videos presenting threat appeals to predict favorable viewer responses. Also, for a more accurate understanding of audience responses to the messages, nuance cues should be considered together with message features and viewer influences.
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Stored only when both papers are in the corpus — 31 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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