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dc.contributor.author | Kurochkin, Andrew![]() |
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dc.date.accessioned | 2020-02-25T14:28:05Z | |
dc.date.available | 2020-02-25T14:28:05Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Kurochkin, Andrew. Meme Generation for Social Media Audience Engagement : Master Thesis : manuscript / Andrew Kurochkin ; Supervisor Kostiantyn Bokhan, PhD ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2020. – 42 p. : ill. | uk |
dc.identifier.uri | http://er.ucu.edu.ua/handle/1/2050 | |
dc.language.iso | en | uk |
dc.subject | Meme Generation | uk |
dc.subject | Engaging content creation | uk |
dc.subject | Image macro | uk |
dc.title | Meme Generation for Social Media Audience Engagement | uk |
dc.type | Preprint | uk |
dc.status | Публікується вперше | uk |
dc.description.abstracten | In digital marketing, memes have become an attractive tool for engaging an online audience. Memes have an impact on buyers and sellers online behavior and information spreading processes. Thus, the technology of generating memes is a significant tool for social media engagement. In this study, we collected new memes dataset of 650K meme instances, applied state of the art Deep Learning technique - GPT-2 model [1] towards meme generation, and compared machine-generated memes with human-created. We justified that MTurk workers can be used for the approximate estimating of users’ behavior in a social network, more precisely to measure engagement. Generated memes cause the same engagement as human memes, which didn’t collect engagement in the social network (historically). Still, generated memes are less engaging then random memes created by humans. | uk |