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Driving engagement through emotional content: a data-driven analysis from the Upworthy Research Archive: content-based recommendation system for engaging headlines from the Upworthy Research Archive
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Orientador(es)
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 impacts 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 similar news recommendations, clickbait impact, headline
category impact, and time trends impacting Upworthy’s engagement.
Descrição
Palavras-chave
Digital content Engagement Upworthy MIND Prediction News articles Headline Excerpt Categories Classification algorithms Machine learning Natural language processing User interaction Content strategy Interaction trends Patterns Insights Recommendation systems Text embeddings Text analysis Model evaluation