Verbal irony use in personal blogs

Whalen, Juanita M.; Pexman, Penny M.; Gill, Alastair J.; Nowson, Scott

doi:10.1080/0144929x.2011.630418

Cited by 34 publications

(29 citation statements)

References 22 publications

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“…Analysis of the ironic markers revealed that more than three quarters of ironic utt erances were signaled with markers, and there was no signifi cant diff erence between F2F and CMC, suggesting that the two sett ings enable speakers to signal irony in an equivalent manner. In the wake of Hancock (2004) and Whalen et al (2009Whalen et al ( , 2013, our study confi rms that the idea that CMC is not suitable for producing irony because of the lack of nonverbal cues (prosody and facial expressions) is obsolete. Some markers were the same across both sett ings (e.g., amplifi er words) while others were specifi c either to F2F (e.g., prosody) or to CMC (e.g., punctuation and other typographic devices).…”

Section: Discussionsupporting

confidence: 78%

Producing Irony in Adolescence: A Comparison Between Face-to-Face and Computer-Mediated Communication

Aguert

Laval

Gauducheau

et al. 2016

Psychology of Language and Communication

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The literature suggests that irony production expands in the developmental period of adolescence. We aimed to test this hypothesis by investigating two channels: face-to-face and computer-mediated communication (CMC). Corpora were collected by asking seventh and 11th graders to freely discuss some general topics (e.g., music), either face-to-face or on online forums. Results showed that 6.2% of the 11th graders’ productions were ironic utterances, compared with just 2.5% of the seventh graders’ productions, confirming the major development of irony production in adolescence. Results also showed that adolescents produced more ironic utterances in CMC than face-to-face. The analysis suggested that irony use is a strategy for increasing in-group solidarity and compensating for the distance intrinsic to CMC, as it was mostly inclusive and well-marked on forums. The present study also confirmed previous studies showing that irony is compatible with CMC.

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Section: Discussionsupporting

confidence: 78%

Producing Irony in Adolescence: A Comparison Between Face-to-Face and Computer-Mediated Communication

Aguert

Laval

Gauducheau

et al. 2016

Psychology of Language and Communication

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“…The results from our study suggest that such distinctions are not exclusive to metaphor. Instead, hyperbole (see also Claridge, 2011;Whalen et al, 2013) and irony (see also Barbe, 1995;Burgers et al, 2015) also seem to be used in conventional and novel instantiations. As such, all three tropes can be analyzed on these two dimensions.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, other types of CMC show different uses of hyperbole and sarcasm. In e-mail conversations (Whalen, Pexman & Gill, 2009) and online blogs (Whalen, Pexman, Gill & Nowson, 2013), hyperbole was used more often than sarcasm. Speakers also tended to use less discourse markers with hyperbole than with sarcasm (Whalen et al, 2009).…”

Section: Hyperbole and Ironymentioning

confidence: 99%

Metaphor, hyperbole, and irony: Uses in isolation and in combination in written discourse

Burgers

Lavalette

Steen

2018

Journal of Pragmatics

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Highlights We study how metaphor, hyperbole, and irony are used in isolation and combination. We conducted a corpus analysis of Dutch economic news discourse. Typical combinations of metaphor-hyperbole differ from their usage in isolation. Irony was associated with hyperbole, but not with metaphor. Combinations of the three tropes reflected conventional rather than novel word use. AbstractWhile classical theories on rhetoric cluster figurative devices like metaphor, hyperbole, and irony under the encompassing category of tropes, current theories and research typically focus on one of the tropes in isolation. To determine how these different tropes are used in combinations, we conducted a large-scale corpus analysis of Dutch printed news discourse (54,851 words). For metaphor and hyperbole, we find that typical combinations are found in nouns and adjectives, showing that such combinations differ from the use of either trope in isolation. For hyperbole and irony, we find a relation between the two tropes in that ironic clauses contain more hyperbole than non-ironic clauses. In contrast, for metaphor and irony, we find no empirical evidence that the use of metaphors differs between ironic and non-ironic clauses. Analysis of clauses containing the three tropes of metaphor, hyperbole and irony shows that these may not always reflect novel and creative word use. Instead, various cases seem to contain conventional uses of metaphor and hyperbole.

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“…In this article we also address the task of detecting irony in tweets, by identifying a set of discriminative features to automatically differentiate an ironic text from a non-ironic one. In line with most of the current approaches and with some theoretical accounts Whalen et al 2013], irony is here considered an umbrella term that covers also sarcasm, being the issue of discriminating between the two devices a further challenge for figurative language processing. Our irony detection model, called emotIDM, extends the model proposed in [Hernández Farías et al 2015] with new features, in particular experimenting the use of a wide range of psycholinguistic and affective features concerning affective information, with the main aim to answer to our main research questions: (1) Does information about different facets of affect help in distinguishing among ironic and non-ironic tweets?…”

Section: Introductionmentioning

confidence: 91%

Irony Detection in Twitter

Farías

Patti

Rosso

2016

ACM Trans. Internet Technol.

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Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model which explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and non-ironic tweets. Our model outperforms the stateof-the-art in almost all cases.

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Verbal irony use in personal blogs

Cited by 34 publications

References 22 publications

Producing Irony in Adolescence: A Comparison Between Face-to-Face and Computer-Mediated Communication

Producing Irony in Adolescence: A Comparison Between Face-to-Face and Computer-Mediated Communication

Metaphor, hyperbole, and irony: Uses in isolation and in combination in written discourse

Irony Detection in Twitter

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