Customers are rapidly turning to social media for customer support. While brand agents on these platforms are motivated and well-intentioned to help and engage with customers, their efforts are often ignored if their initial response to the customer does not match a specific tone, style, or topic the customer is aiming to receive. The length of a conversation can reflect the effort and quality of the initial response made by a brand toward collaborating and helping consumers, even when the overall sentiment of the conversation might not be very positive. Thus, through this study, we aim to bridge this critical gap in the existing literature by analyzing language's content and stylistic aspects such as expressed empathy, psycho-linguistic features, dialogue tags, and metrics for quantifying personalization of the utterances that can influence the engagement of an interaction. This paper demonstrates that we can predict engagement using initial customer and brand posts.

本文通过分析初次客户和品牌帖子的语言内容和风格方面的表达共情、心理语言特征、对话标记和量化话语的个性化度量等因素，证明我们可以使用这些因素来预测客户和品牌之间的互动来促进客户对社交媒体客户支持的参与度。

社交媒体客户服务话语参与的语言元素