# Linguistic Elements of Engaging Customer Service Discourse on Social Media

arXiv: https://arxiv.org/abs/2212.12801 · HTML: https://mlsanity.com/paper/2212.12801/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 24 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [Towards Few-Shot Fact-Checking via Perplexity](https://mlsanity.com/paper/2103.09535/) (2021)
- [Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media](https://mlsanity.com/paper/1803.02952/) (2018)
- [Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff](https://mlsanity.com/paper/2012.07610/) (2020)
- ["How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts](https://mlsanity.com/paper/1709.05413/) (2017)
- [Don't get Lost in Negation: An Effective Negation Handled Dialogue Acts Prediction Algorithm for Twitter Customer Service Conversations](https://mlsanity.com/paper/1807.06107/) (2018)

## Built on by (most cited first, 2 of 2)

- [Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods](https://mlsanity.com/paper/2311.00721/) (2023)
- [Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation](https://mlsanity.com/paper/2604.11077/) (2026)

## Abstract (arXiv)

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.