Meme Generation for Social Media Audience Engagement

Kurochkin, Andrew

Домівка
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Students Research & Project Works | Роботи студентів
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Факультет прикладних наук
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Освітня програма наук про дані
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2020
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dc.contributor.author	Kurochkin, Andrew
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