Understanding Effects of Editing Tweets for News Sharing by Media Accounts through a Causal Inference Framework

To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, research community does not own a sufficient level of understanding of what kinds of editing strategies are effective in promoting audience engagement. In this study, we aim to fill the gap by analyzing the current practices of media outlets using a data-driven approach. We first build a parallel corpus of original news articles and their corresponding tweets that were shared by eight media outlets. Then, we explore how those media edited tweets against original headlines, and the effects would be. To estimate the effects of editing news headlines for social media sharing in audience engagement, we present a systematic analysis that incorporates a causal inference technique with deep learning; using propensity score matching, it allows for estimating potential (dis-)advantages of an editing style compared to counterfactual cases where a similar news article is shared with a different style. According to the analyses of various editing styles, we report common and differing effects of the styles across the outlets. To understand the effects of various editing styles, media outlets could apply our easy-to-use tool by themselves.