Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to (...) distinguish between can sarcasm none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to (...) distinguish between can sarcasm none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

The following articke studies the definitions of sarcasm, its usage in literature, in educational system, and its pros and cons.

This article aims to conceptualize the interplay between sarcasm, scatology and writing in Darija (Moroccan Arabic or Al-Maghribia) on the web in a post-uprising era. It focuses on the new Darijophone prose that emerged after 20 February 2011 protests in Goud and The Newزحيليكر‎ (The New Bumpkin). Originating with absurdist February 20 movement founder member, Mohammed Sokrat, this writing genre is realist, vulgar, profane, taboo-breaking, and borrows from the toilet space to poke fun at the schizophrenia, herd mentality and (...) populism that attend a modernizing society under a neoliberal regime. A vocabulary of trash, waste, filthy social types and risqué gags has informed a unique online minoritarian prose that is unpopular, yet widely read. This article studies the virtual politics and poetics of dirt and laughter in written Darija. Writing at the interstices of commitment and co-optation, this subcultural mode of knowledge production reveals Moroccan youth's heretofore suppressed yet incessant longing for change in a post-uprising context of disillusionment. (shrink)

Critique of Sarcastic Reason is a philosophical dissertation that combines several different fields in order to pave the way for those studying sarcasm at the neurobiological, communicative and socio-political levels of analysis where sarcasm appears, respectively, through associated brain activity, between two or more individuals with higher level metabeliefs, and as a method by which political, religious and other social ideologies are attacked (i.e., one form of "biting sarcasm"). The academic disciplines involved in Critique of Sarcastic Reason (...) include social cognitive and developmental psychology, neuroscience, critical theory, modern and contemporary philosophy of mind, evolutionary biology, logic, metaphysics and epistemology. Sarcasm is argued to only function at the highest levels of metacognition, and sarcasm occurs within social situations during which there are tendencies for two or more people to form desires and disgusts directly related to beliefs about beliefs. Sarcasm is compared to deception and is argued to be best analyzed as either spontaneous or rehearsed. (shrink)

Mobsters and others engaged in risky forms of social coordination and coercion often communicate by saying something that is overtly innocuous but transmits another message ‘off record’. In both ordinary conversation and political discourse, insinuation and other forms of indirection, like joking, offer significant protection from liability. However, they do not confer blanket immunity: speakers can be held to account for an ‘off record’ message, if the only reasonable interpreta- tions of their utterance involve a commitment to it. Legal liability (...) for speech in the service of criminal behavior displays a similar profile of significant protection from indirection along with potential liability for reasonable interpretations. Specifically, in both ordinary and legal contexts, liability depends on how a reasonable speaker would expect a reasonable hearer to interpret their utterance in the context of utter- ance, rather than on the actual speaker’s claimed communicative intentions. (shrink)