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To reach a broader audience and optimize traffic toward news articles, mediaoutlets commonly run social media accounts and share their content with a shorttext summary. Despite its importance of writing a compelling message in sharingarticles, the research community does not own a sufficient understanding ofwhat kinds of editing strategies effectively promote audience engagement. Inthis study, we aim to fill the gap by analyzing media outlets' currentpractices using a data-driven approach. We first build a parallel corpus oforiginal news articles and their corresponding tweets that eight media outletsshared. Then, we explore how those media edited tweets against originalheadlines and the effects of such changes. To estimate the effects of editingnews headlines for social media sharing in audience engagement, we present asystematic analysis that incorporates a causal inference technique with deeplearning; using propensity score matching, it allows for estimating potential(dis-)advantages of an editing style compared to counterfactual cases where asimilar news article is shared with a different style. According to theanalyses of various editing styles, we report common and differing effects ofthe styles across the outlets. To understand the effects of various editingstyles, media outlets could apply our easy-to-use tool by themselves.

How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement