Information artifacts incorporate cognitive elements in their design to inform users about and entice them to consume relevant content. Sparse research has examined how to design cognitive elements in information artifacts in the digital news platforms context. This study investigates how information artifacts' semantic and sentiment elements convey meaning and emotion to elicit users to share online news. We propose a dissonant framework and hypothesize that three dissonance dimensions

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... semantic dissonance, textual sentiment dissonance, and visual sentiment dissonance) influence news sharing. We tested the hypotheses using real-world data from 2013 to 2015 from Mashable-a popular digital news platform. We used novel machine-learning techniques to extract topics and sentiments from text and photos in news articles. Findings from our econometric analysis support that textual sentiment and visual sentiment dissonance positively affect news sharing.