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Driving engagement through emotional content: a data-driven analysis from the Upworthy Research Archive: clickbait detection and its implications using a machine learning approach applied to the Upworthy Archive
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Resumo(s)
In a digital landscape where capturing reader attention is crucial to news platforms, the
Upworthy Research Archive enables the study of how article headlines influence engagement.
This research assesses how the components of a headline impact user clicks through machine
learning methods to extract features related to emotional and phrase-meaning topics to predict
the level of engagement on each piece of news. Results show that timing dominates
engagement, headline structure plays a key role, and emotional traits have limited influence.
Additional analysis explores clickbait detection and analysis of its impact in the
Upworthy Research Archive.
Descrição
Palavras-chave
Digital content Engagement Upworthy MIND Prediction News articles Digital content Headline Excerpt Categories Classification Algorithms Machine Learning Natural
Language Processing User Interaction Content strategy Interaction trends Patterns Insights clickbait trends Plots Statistical analysis t-Test Regression analysis Hypothesis testing Model evaluation