Abstract
This paper examines anger expression in a corpus of 66 customer service complaints posted to the Mexican X/Twitter account of the online cab company, Uber. Specifically, building on work on emotions and (im)politeness as well as work on service-related anger episodes, it focuses on how these complainers signal their anger, and how these emotional expressions are related to rapport management orientation. Adopting a qualitative perspective, we observed complainers in this corpus expressing anger explicitly (using specific lexical choices such as profanities), implicitly (for example, asking rhetorical questions with semantic connotations of anger), or indulging in anger behaviours (threatening retaliatory behaviour). The majority of these strategies would be considered rapport challenging, however, certain more constructive, rapport enhancing strategies were also observed: for example, complainers portrayed themselves as approachable using medium specific in-group slang or provided means to open a dialogue to solve whatever problem they were complaining about.
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Articles in the same Issue
- Frontmatter
- Editorial
- Introduction to the Special Issue: Pragmatics, digital content and opinions
- Research Articles
- Discursive news values analysis: the case of Liz Truss’ representation in the British press
- The case of romantic relationships: analysis of the use of metaphorical frames with ‘traditional family’ and related terms in political Telegram posts in three countries and three languages
- Expressing anger on Mexican X/Twitter: the case of Uber customer complaints
- Expressing negative opinions through metaphor and simile in popular music reviews
- Slur reclamation, irony, and resilience
- What is the authentic internet register before & after the Russian invasion in Ukraine? Polish and Czech YouTube comments from 2021–2023
- Application of natural language processing for the recognition of obesity-related topics in the discourses of Argentine Twitter users
- Opinion events and stance types: advances in LLM performance with ChatGPT and Gemini
- Classifying offensive language in Arabic: a novel taxonomy and dataset
- Implicit offensive language taxonomy
Articles in the same Issue
- Frontmatter
- Editorial
- Introduction to the Special Issue: Pragmatics, digital content and opinions
- Research Articles
- Discursive news values analysis: the case of Liz Truss’ representation in the British press
- The case of romantic relationships: analysis of the use of metaphorical frames with ‘traditional family’ and related terms in political Telegram posts in three countries and three languages
- Expressing anger on Mexican X/Twitter: the case of Uber customer complaints
- Expressing negative opinions through metaphor and simile in popular music reviews
- Slur reclamation, irony, and resilience
- What is the authentic internet register before & after the Russian invasion in Ukraine? Polish and Czech YouTube comments from 2021–2023
- Application of natural language processing for the recognition of obesity-related topics in the discourses of Argentine Twitter users
- Opinion events and stance types: advances in LLM performance with ChatGPT and Gemini
- Classifying offensive language in Arabic: a novel taxonomy and dataset
- Implicit offensive language taxonomy