earticle

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Twitter is one of the most popular social media outlets available and has been expanding over the years in both scope and reach. The growing number of users, and the accessibility to their micro-posts and metadata make Twitter a popular subject for research in various research communities. In this paper, we propose and examine a content analysis method that utilizes the hierarchy of effects model, which has a very long history of use by both practitioners and academics in the field of advertising and marketing. We have judges manually annotate tweets in accordance with one of the five stages of attitudes: Attention, Interest, Desire, Action, or Satisfaction. Next, we examine the tagged corpus to identify general traits and to explore the possibilities for the newly gained information from the tweets. The results suggest that consumer attitude information can be utilized to improve the prediction quality of box-office revenues and possibly better represent movie audience sentiment. These findings can complement other content analysis methods that utilizes Twitter data by providing an additional dimension for the researchers to consider.