Social media for message testing: a multilevel approach to linking favorable viewer responses with message, producer, and viewer influence on YouTube.

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.