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        <title>Linguistic Elements of Engaging Customer Service Discourse on Social Media</title>
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            <namePart type="given">David</namePart>
            <namePart type="family">Bamman</namePart>
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            <namePart type="given">Svitlana</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.nlpcss-1.12</identifier>
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%0 Conference Proceedings
%T Linguistic Elements of Engaging Customer Service Discourse on Social Media
%A Singh, Sonam
%A Rios, Anthony
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y Keith, Katherine
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F singh-rios-2022-linguistic
%X 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.
%R 10.18653/v1/2022.nlpcss-1.12
%U https://aclanthology.org/2022.nlpcss-1.12
%U https://doi.org/10.18653/v1/2022.nlpcss-1.12
%P 105-117

Markdown (Informal)

[Linguistic Elements of Engaging Customer Service Discourse on Social Media](https://aclanthology.org/2022.nlpcss-1.12) (Singh & Rios, NLP+CSS 2022)

• Linguistic Elements of Engaging Customer Service Discourse on Social Media (Singh & Rios, NLP+CSS 2022)

ACL

• Sonam Singh and Anthony Rios. 2022. Linguistic Elements of Engaging Customer Service Discourse on Social Media. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), pages 105–117, Abu Dhabi, UAE. Association for Computational Linguistics.