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Driving engagement through emotional content: a data-driven analysis from the Upworthy Research Archive: the impact of time on engagement and emotions: forecasting and analysis 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 time-series models to predict and understand trends along
time.
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 Text embeddings Seasonality Forecasting Autoregressive models Temporal trends Time series Headline classification Text analysis Regression analysis