Chapter 11. Irony in American-English tweets
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Beatriz Martín Gascón
Abstract
The present study examines verbal irony from a cognitive linguistics perspective, based on Ruiz de Mendoza’s (2017) development of the echoic account and on big data. Built on previous research on the detection of Spanish ironic utterances in Twitter (Martín-Gascón, 2019), this investigation aims to analyze how American-English speakers conceptualize and express irony and compares findings to the Spanish ones. The dataset, initially consisting of 1,157,773,379 tweets from 248 countries and 66 languages, was first reduced to 27,517 tweets from English-speaking users in the United States using the words “irony”, “ironies”, and “ironic”, then to 605 containing the words as hashtag and finally to 495 tweets evincing implicit and explicit-echoic irony. An in-depth cognitive and qualitative analysis of the sample revealed the complexities of perceiving irony in written discourse and, therefore, the relevance of adding contextual ironic markers, such as hashtags, emojis, interjections, laughter typing and ironic phraseology, among others. In line with Martín-Gascón’s (2019) study, findings showed a higher use of positive and explicit-echoic irony as compared to implicit and negative irony. By drawing attention to the similarities and differences in the expression of irony, we expect to offer preliminary informed options for the design of pedagogical proposals that enhance not only the learners’ linguistic and ironic competencies, but also their intercultural awareness.
Abstract
The present study examines verbal irony from a cognitive linguistics perspective, based on Ruiz de Mendoza’s (2017) development of the echoic account and on big data. Built on previous research on the detection of Spanish ironic utterances in Twitter (Martín-Gascón, 2019), this investigation aims to analyze how American-English speakers conceptualize and express irony and compares findings to the Spanish ones. The dataset, initially consisting of 1,157,773,379 tweets from 248 countries and 66 languages, was first reduced to 27,517 tweets from English-speaking users in the United States using the words “irony”, “ironies”, and “ironic”, then to 605 containing the words as hashtag and finally to 495 tweets evincing implicit and explicit-echoic irony. An in-depth cognitive and qualitative analysis of the sample revealed the complexities of perceiving irony in written discourse and, therefore, the relevance of adding contextual ironic markers, such as hashtags, emojis, interjections, laughter typing and ironic phraseology, among others. In line with Martín-Gascón’s (2019) study, findings showed a higher use of positive and explicit-echoic irony as compared to implicit and negative irony. By drawing attention to the similarities and differences in the expression of irony, we expect to offer preliminary informed options for the design of pedagogical proposals that enhance not only the learners’ linguistic and ironic competencies, but also their intercultural awareness.
Chapters in this book
- Prelim pages i
- Table of contents v
- Preface vii
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Section 1. Computational treatment of multiword units
- Chapter 1. Multi-word units in neural machine translation 2
- Chapter 2. ReGap 18
- Chapter 3. Evaluating the Italian-English machine translation quality of MWUs in the domain of archaeology 40
- Chapter 4. Post-editing neural machine translation in specialised languages 57
- Chapter 5. Evaluating a bracketing protocol for multiword terms 79
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Section 2. Corpus-based and linguistic studies in phraseology
- Chapter 6. Suggestions for a new model of functional phraseme categorization for applied purposes 104
- Chapter 7. Verb collocations and their semantics in the specialized language of science 124
- Chapter 8. Negative–positive adjective pairing in travel journalism in English, Italian, and Polish 141
- Chapter 9. The middle construction and some machine translation issues 156
- Chapter 10. Semantic annotation of named rivers and its application for the prediction of multiword-term bracketing 173
- Chapter 11. Irony in American-English tweets 197
- Chapter 12. A comprehensive Japanese MWE lexicon 218
- Chapter 13. Ontology-based formalisation of Italian clitic verbal MWEs 243
- Index 263
Chapters in this book
- Prelim pages i
- Table of contents v
- Preface vii
-
Section 1. Computational treatment of multiword units
- Chapter 1. Multi-word units in neural machine translation 2
- Chapter 2. ReGap 18
- Chapter 3. Evaluating the Italian-English machine translation quality of MWUs in the domain of archaeology 40
- Chapter 4. Post-editing neural machine translation in specialised languages 57
- Chapter 5. Evaluating a bracketing protocol for multiword terms 79
-
Section 2. Corpus-based and linguistic studies in phraseology
- Chapter 6. Suggestions for a new model of functional phraseme categorization for applied purposes 104
- Chapter 7. Verb collocations and their semantics in the specialized language of science 124
- Chapter 8. Negative–positive adjective pairing in travel journalism in English, Italian, and Polish 141
- Chapter 9. The middle construction and some machine translation issues 156
- Chapter 10. Semantic annotation of named rivers and its application for the prediction of multiword-term bracketing 173
- Chapter 11. Irony in American-English tweets 197
- Chapter 12. A comprehensive Japanese MWE lexicon 218
- Chapter 13. Ontology-based formalisation of Italian clitic verbal MWEs 243
- Index 263
