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, the research community does not own a sufficient understanding of what kinds of editing strategies effectively promote audience engagement. In this study, we aim to fill the gap by analyzing media outlets' current practices using a data-driven approach. We first build a parallel corpus of original news articles and their corresponding tweets that eight media outlets shared. Then, we explore how those media edited tweets against original headlines and the effects of such changes. 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.
翻译:为了达到更广泛的受众,并优化新闻文章的流量,媒体机构通常使用社交媒体账户,并以简短的文本摘要分享其内容。尽管在分享文章时必须撰写令人信服的信息,但研究界对何种编辑战略能够有效促进受众的参与缺乏足够的理解。在本研究中,我们的目标是通过利用数据驱动的方法分析媒体机构的现行做法来填补这一空白。我们首先建立一个平行的原始新闻文章及其对应的推文集,八个媒体机构共享。然后,我们探讨这些媒体如何根据原始头条编辑推文以及这些变化的影响。为了估计编辑新闻头条对于社交媒体参与中的共享的影响,我们提出了一个系统分析,将因果关系技术与深层次的学习结合起来;利用常识计分匹配,可以估计编辑风格的潜力(不)优缺点,与类似文章以不同风格分享的反事实案例相比较。根据对各种编辑风格的分析,我们报告不同版本的常见和不同影响。为了理解各种编辑风格的影响,媒体机构可以很容易地运用我们的工具。