社会化媒体
用户参与度
计算机科学
用户生成的内容
内容(测量理论)
媒体内容
人工智能
人机交互
多媒体
万维网
数学
数学分析
作者
Daniel Årrestad Stave,Hanne Korneliussen,H. Nøkleby Hjellup,Raju Shrestha
标识
DOI:10.1109/airc57904.2023.10303076
摘要
Today, many artificial or virtual influencers roam social media platforms to maximise followers and offer commercial options for companies. This work focuses on developing artificial influencers using state-of-the-art techniques within deep learning. Specifically, an autonomous theoretical framework for generating social media content that maximises user engagement is proposed. Deep learning models for generating realistic images and hashtags are trained on a dataset from a social media platform, and content is optimised for user engagement using an evolutionary algorithm. The generated images were evaluated by participants from existing social media users through two separate surveys. The complete framework is built, trained, and tested, and functionality is confirmed. The framework, which appears to be the first of its kind, produces content that matches the users' preferences well.
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