Teaching children with autism to detect and respond to sarcasm

Persicke, Angela; Tarbox, Jonathan; Ranick, Jennifer; Clair, Megan St.

doi:10.1016/j.rasd.2012.08.005

Cited by 52 publications

(28 citation statements)

References 18 publications

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“…e average accuracy for many datasets was found to be 82.45%. Persicke et al [31] developed a system for teaching autistic children a method for detecting sarcasm and responding to the same. e author included rules along with video clips for this purpose.…”

Section: Literature Survey On Sarcasm Detectionmentioning

confidence: 99%

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.

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Section: Literature Survey On Sarcasm Detectionmentioning

confidence: 99%

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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“…In one of the few studies on the topic, Persicke et al (2013) implemented sarcasm training with three children aged 6-7 years who had been diagnosed with ASD. These children had received prior training in related skills like facial expressions, beliefs, and intentions, but not in sarcasm per se.…”

Section: Existing Training Studiesmentioning

confidence: 99%

Addressing the Challenge of Verbal Irony: Getting Serious about Sarcasm Training

2019

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In verbal irony, the speaker’s intended meaning can be counterfactual to the literal meaning of their words. This form of figurative language can help speakers achieve a number of communicative aims, but also presents an interpretive challenge for some listeners. There is debate about the skills that support the acquisition of irony comprehension in typical development, and about why verbal irony presents a challenge for many individuals, including children with a diagnosis of autism spectrum disorders and second-language learners. Researchers have explored teaching verbal irony in a very small number of training studies in disparate fields. We bring together and review this limited research. We argue that a focus on training studies in future research could address a number of theoretical questions about irony comprehension and could help refine interventions for individuals who struggle with this form of social language.

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“…In another study, Persicke et al () used a multiple exemplar training package consisting of rules and feedback to teach children with ASD appropriate listener responses to sarcasm. Specifically, participants were told that “sarcastic statements actually mean the opposite of what is said.” For example, the tact nice shot when playing basketball is sarcastic when the ball is nowhere near going into the hoop, but it is a sincere statement when the ball goes in the hoop.…”

mentioning

confidence: 99%

“…In another study, Persicke et al (2013) used a multiple exemplar training package consisting of rules and feedback to teach children with ASD appropriate listener responses to sarcasm. Specifically, participants were told that "sarcastic statements actually mean the opposite of what is said."…”

mentioning

confidence: 99%

Teaching children with autism to respond to disguised mands

Najdowski

Bergstrom

Tarbox

et al. 2017

J of App Behav Analysis

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Children with autism spectrum disorder (ASD) often have difficulty inferring the private events of others, including private verbal behavior (e.g., thoughts), private emotional responses, and private establishing operations, often referred to as "perspective taking" by the general psychology community. Children with ASD also have difficulty responding to disguised mands. Skinner's description of the "disguised mand" is verbal behavior wherein the speaker's mand directly describes neither its reinforcer nor the corresponding establishing operations. Appropriate responding to disguised mands is required for successful social interaction, making it a social skill worth teaching to children with ASD. We used a nonconcurrent multiple baseline across participants design to investigate the effects of a multiple exemplar training package consisting of rules, role play, and feedback for teaching three boys with ASD to respond to disguised mands. The intervention was effective and generalization to novel disguised mands and people was observed.

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Teaching children with autism to detect and respond to sarcasm

Cited by 52 publications

References 18 publications

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Addressing the Challenge of Verbal Irony: Getting Serious about Sarcasm Training

Teaching children with autism to respond to disguised mands

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