GIFs afford a great degree of personalization as they are often created from popular movie and video clips, with diverse and real characters, each expressing a nuanced affect state through a combination of characters' own unique bodily gesture and distinctive visual background. This highly personalized and embodied property gave us an unique window to explore how individuals represent and express themselves on social media, through the lens of GIFs they use.
In this study, we explore how do Twitter users express their gender and racial identities through that of characters in gifs. We conducted a behavioral study (n=398) to simulate a series of tweeting and gif picking scenario and we found that gender and race identities have significant impact on users' choice of GIFs and that source familiarity and perceived audience also have significant impacts on whether a user will choose race and gender matching GIFs. / Master of Science / GIFs are one of most humorous material on the internet. People use GIFs on Twitter for public disclosure, on WhatsApp and iMessage for chatting with friends and groups. GIFs are often created from from popular movie and video clips, with diverse and real characters, each expressing a nuanced affect state through a combination of characters' own unique bodily gesture and distinctive visual background. This highly personalized property of GIFs gave us an unique window to explore how individuals represent and express themselves on social media, through the lens of GIFs they use. For instance, would you be more likely to use a GIF with a men character, if you are a women? In this study, we explore how do Twitter users express their gender and racial identities through that of characters in gifs. We conducted a behavioral study (n=398) to simulate a series of tweeting and gif picking scenario and we found that gender and race identities have significant impact on users' choice of GIFs and that source familiarity and perceived audience also have significant impacts on whether a user will choose race and gender matching GIFs.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115971 |
| Date | 02 August 2023 |
| Creators | Wang, Boyuan |
| Contributors | Computer Science and Applications, Lee, Sang Won, Rho, Ha Rim, Luther, Kurt |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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