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- W2336223491 abstract "This paper presents a method to classify social media users based on their socioeconomic status. Our experiments are conducted on a curated set of Twitter profiles, where each user is represented by the posted text, topics of discussion, interactive behaviour and estimated impact on the microblogging platform. Initially, we formulate a 3-way classification task, where users are classified as having an upper, middle or lower socioeconomic status. A nonlinear, generative learning approach using a composite Gaussian Process kernel provides significantly better classification accuracy ( $$75,%$$ ) than a competitive linear alternative. By turning this task into a binary classification – upper vs. medium and lower class – the proposed classifier reaches an accuracy of $$82,%$$ ." @default.
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- W2336223491 date "2016-01-01" @default.
- W2336223491 modified "2023-10-13" @default.
- W2336223491 title "Inferring the Socioeconomic Status of Social Media Users Based on Behaviour and Language" @default.
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- W2336223491 doi "https://doi.org/10.1007/978-3-319-30671-1_54" @default.
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