Proposal authors of accepted papers can use the tool below (scroll down) to see where they have been placed in the agenda. Note: Special Topics sessions - Only the submitter's name is searchable, not panelists. [If you wish an overview of the conference please click here: Conference Overview Agenda.
Please use this search form to:
- First or Secondary Author: Click on "person," click on your name, and "search." Then use the bottom scroll bar to slide to the right until you see the search button. Scroll down this screen to see the result of the search!
- Search by Date/Time
- Search by Keywords
- Search by Submission Type (Full Paper or Special Topics)
To see the full agenda, leave all drop-downs blank, and simply click on "Search." Please note that there are some options you may select once you have searched. (Please do not be confused about something that looks like "1.1 Digital Advertising" as this is simply a title of a group of sessions.
Contact the AAA Conference Office to accept the session assignment, or to request a change due to a conflict. Call (727) 940-2658 x 2004 if you have questions. Please be sure to reference the session title(s), date(s) and time(s) if you contact us.
2.1b Does Femvertising Sell? A Data Mining Investigation of Consumer Conversations Around Dove’s Campaign for Real Beauty on YouTube
Abstract
As a growing marketing trend that appropriates feminist values and female empowerment, femvertising has the potential to encourage brand consumption and to reduce the occurrence of advertising reactance. Dove’s Campaign for Real Beauty is a pioneer of femvertising that focuses on redefining beauty standards and enhancing women’s self-esteem. This research presents a framework that identifies five topics (i.e., ad skepticism, definition of beauty, praise of ad, critical thinking, and other) of YouTube comments around Dove’s Campaign for Real Beauty. The framework, applied to 20,419 unique comments, was developed from a combined use of qualitative textual analysis, human-based content analysis, and machine-learning-based data mining. Results indicated that definition of beauty and praise of advertising were the two most frequent topics among both top comments and overall comments; in addition, comments around these two topics received the largest number of likes. In contrast, comments around advertising skepticism received the least number of likes. Generally, it is shown that public favors femvertising.
First & Corresponding Author
Yang Feng, San Diego State University
Authors in the order to be printed.
Yang Feng, San Diego State University; Huan Chen, University of Florida; Li He, Qualcomm Technologies Inc.