Sarcastic Remarks Research Articles

Специфические вызовы и проблемы семей участников СВО: результаты фокус-групповых дискуссий

The article analyzes the data of focus group discussions with representatives of families of participants of the special military operation, conducted in March 2024. The study examined the issues related to everyday life, information awareness and accessibility barriers, existing support measures. The discussions revealed that the main conflict-causing factor for this group is the manifestation of polarization and negative societal attitudes towards the special military operation and its participants. Representatives of their families often face misunderstanding in the family, among colleagues and acquaintances, sarcastic remarks about payments, as well as indifference of others to their experiences. The author examines the factors of insufficient awareness of prescribed support measures, specific difficulties in obtaining state-mandated benefits and preferences for military families.

A Fusion Pretrained Approach for Identifying the Cause of Sarcasm Remarks

Sarcastic remarks often appear in social media and e-commerce platforms to express almost exclusively negative emotions and opinions on certain instances, such as dissatisfaction with a purchased product or service. Thus, the detection of sarcasm allows merchants to timely resolve users’ complaints. However, detecting sarcastic remarks is difficult because of its common form of using counterfactual statements. The few studies that are dedicated to detecting sarcasm largely ignore what sparks these sarcastic remarks, which could be because of an empty promise of a merchant’s product description. This study formulates a novel problem of sarcasm cause detection that leverages domain information, dialogue context information, and sarcasm sentences by proposing a pretrained language model-based approach equipped with a novel hybrid multihead fusion-attention mechanism that combines self-attention, target-attention, and a feed-forward neural network. The domain information and the dialogue context information are then interactively fused to obtain the domain-specific dialogue context representation, and bidirectionally enhanced sarcasm-cause pair representations are generated for detecting sarcasm spark. Experimental results on real-world data sets demonstrate the efficacy of the proposed model. The findings of this study contribute to the literature on sarcasm cause detection and provide business value to relevant stakeholders and consumers. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the National Natural Science Foundation of China [Grants 72293575, 62071467, and 62141608] and the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants 11500322 and 11500421]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0285 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0285 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Automatic Sarcasm Detection on Cross-Platform Social Media Datasets: A GLoVe and Bi-LSTM Based Approach 

Sarcastic remarks on social media platforms have become commonplace, with people expressing their bad feelings in a quite positive manner or in a mocking way. This contradictive nature of sarcasm makes its detection a very challenging task. Many researchers have provided their solutions to perform automatic sarcasm detection from a single domain dataset. Most of them have considered only the content of the text and has ignored the context of the text. Understanding that the context of a text is the most important factor in determining either it is sarcastic or not. This study aims to detect sarcastic remarks from multi-domain dataset by using Bi-LSTM model employed with pre-trained GloVe word embeddings because the GloVe embeddings and Bi-LSTM both are good at capturing contextual information from the provided data. The dataset is generated by the concatenation of three publicly available datasets, the gosh tweets, the news headlines dataset, and the sarcasm corpus v2 dataset. GloVe embeddings will extract contextual and semantic features from the text while the Bi-LSTM model will get trained and tested on those features. The proposed model has achieved 86.35% of accuracy with 88% of Recall, Precision, and F1-score. Different experiments have been done to test the model's reliability. This study's findings indicate that the proposed model yields state-of-the-art or comparable results. The proposed study aids in improving the performance of sentiment analysis. It will also help individuals, and different organizations to identify accurate sentiments of people about individuals or products of an organization. 

The Use of Sarcasm in ESP Classes

Sarcasm can be defined as the use of words that mean the opposite of what the speaker actually says, especially to insult the recipient, to show irritation or to amuse the others. Sarcastic remarks are funny and constructive when they are mutually understood within a genuine relationship. Sarcasm has the potential to catalyze creativity for those who produce and receive it. A positive message delivered in a friendly tone - „You did a great job” and a negative message delivered in a patronizing or deprecating tone - “Your answers are far beyond all expectations” - are congruent.

Sindiran Antiphrassis Warganet di Instagram @Mastercorbuzier : Tuduhan Pelecehan Seksual

<p>The increasing use of social media on Instagram has triggered the emergence of sarcastic comments, particularly concerning the topic of sexual harassment, in the comment section. This research aims to identify the types and meanings of the sarcasm used by netizens on the Instagram account @mastercorbuzier, as well as the functions of these sarcastic remarks. This research follows a qualitative descriptive approach, using descriptive sentences rather than numbers. The data for this study consist of Instagram comments on the @mastercorbuzier account, which are expressed in sentence form. The data collection procedure employs the observation and note-taking method. The data are analyzed based on their meanings, types, and functions, and the results are then summarized. The data analysis technique involves an interactive model that examines the written language of sarcasm from Dedy Corbuzier's Instagram account, records the identified data, analyzes them descriptively, and draws conclusions from the research findings. The findings of this study reveal that the stylistic device of sarcasm, known as antiphrasis, appears in 24 instances. These instances demonstrate that Instagram users employ implicit language that conveys the opposite or contrary meaning to express their comments. Furthermore, out of the 23 instances, netizens predominantly utilize connotative meanings to convey their intentions in the comment section. Lastly, the most common linguistic function employed by netizens, with 13 instances, is discourse, which serves to discuss ongoing events.</p>

Sarcasm Detection over Social Media Platforms Using Hybrid Ensemble Model with Fuzzy Logic

A figurative language expression known as sarcasm implies the complete contrast of what is being stated with what is meant, with the latter usually being rather or extremely offensive, meant to offend or humiliate someone. In routine conversations on social media websites, sarcasm is frequently utilized. Sentiment analysis procedures are prone to errors because sarcasm can change a statement’s meaning. Analytic accuracy apprehension has increased as automatic social networking analysis tools have grown. According to preliminary studies, the accuracy of computerized sentiment analysis has been dramatically decreased by sarcastic remarks alone. Sarcastic expressions also affect automatic false news identification and cause false positives. Because sarcastic comments are inherently ambiguous, identifying sarcasm may be difficult. Different individual NLP strategies have been proposed in the past. However, each methodology has text contexts and vicinity restrictions. The methods are unable to manage various kinds of content. This study suggests a unique ensemble approach based on text embedding that includes fuzzy evolutionary logic at the top layer. This approach involves applying fuzzy logic to ensemble embeddings from the Word2Vec, GloVe, and BERT models before making the final classification. The three models’ weights assigned to the probability are used to categorize objects using the fuzzy layer. The suggested model was validated on the following social media datasets: the Headlines dataset, the “Self-Annotated Reddit Corpus” (SARC), and the Twitter app dataset. Accuracies of 90.81%, 85.38%, and 86.80%, respectively, were achieved. The accuracy metrics were more accurate than those of earlier state-of-the-art models.

The Adjustment of Complexity on Sarcasm Processing in Chinese: Evidence from Reading Time Indicators

It is controversial whether sarcasm processing should go through literal meaning processing. There is also a lack of eye movement evidence for Chinese sarcasm processing. In this study, we used eye movement experiments to explore the processing differences between sarcastic and literal meaning in Chinese text and whether this was regulated by sentence complexity. We manipulated the variables of complexity and literality. We recorded 33 participants' eye movements when they were reading Chinese text and the results were analyzed by a linear mixed model. We found that, in the early stage of processing, there was no difference between the processing time of the sarcastic meaning and the literal meaning of simple remarks, whereas for complex remarks, the time needed to process the sarcastic meaning was longer than that needed to process the literal meaning. In the later stage of processing, regardless of complexity, the processing time of the sarcastic meaning was longer than that of the literal meaning. These results suggest that sarcastic speech processing in Chinese is influenced by literal meaning, and the effect of literal meaning on sarcastic remarks is regulated by complexity. Sarcastic meaning was expressed differently in different stages of processing. These results support the hierarchical salience hypothesis of the serial modular model.

A pilot study of adapted social cognition and intervention training (SCIT) for hoarding disorder

BackgroundEmerging research has suggested that hoarding may be associated with reduced social cognition, specifically reduced theory of mind and hostility biases, which may contribute to the social difficulties observed in this population. The primary aim of this study was to evaluate the feasibility and acceptability of Social Cognition and Interaction Training (SCIT) in a sample of individuals with Hoarding Disorder (HD) and assess its potential as an adjunct to CBT. We secondarily examined changes in loneliness and hoarding symptoms. MethodsTwelve individuals, with a primary diagnosis of HD, completed a 12-week SCIT program and one booster session. Participants were assessed at pre-treatment, mid-treatment, post-treatment, and one-month follow-up on measures of theory of mind, attributional style, hoarding symptom severity, and loneliness. ResultsFrom pre-to post-treatment, participants had an improvement in theory of mind for sarcastic remarks and a reduction in hostility bias, hoarding symptoms, and loneliness. Retention was also good as all participants completed treatment. ConclusionThese preliminary results suggest that SCIT is a promising treatment to improving social cognition for hoarding disorder and may improve interpersonal difficulties such as loneliness. We propose that SCIT may represent a potential adjunct targeting social factors to improve the efficacy of CBT.

MULTIMODAL DISCOURSE ANALYSIS OF SARCASM IN ENGLISH MEDIA AND NEWS ARTICLES

With the rapid development of information technologies, the implementation of visual content has become a complementary component of social discourse, particularly in the media and news sectors. In this respect, it is increasingly important to pay huge attention to media literacy and relevant information processing. Sarcasm, one of the most widely used language choices in social discourse, can easily be a part of any media or news article. Sarcastic remarks are used for numerous reasons, namely, to indirectly express contempt, pretend an attitude, mock a situation or a person, or perhaps, they are more creative solutions to anger-provoking situations. The aim of the present research is to detect sarcastic messages in media and news articles through the multimodal markers of the discourse. The dataset analyzed to achieve the above-mentioned goal is derived from American and British media and news platforms Politico, The Guardian, and The Sun.

TANA: The amalgam neural architecture for sarcasm detection in indian indigenous language combining LSTM and SVM with word-emoji embeddings

• TANA, A Hindi sarcasm detection model that combines LSTM and hinge loss of SVM . • Use of pre-trained fastText and emoji2vec embeddings for training. • Performance validation using various metrics, accuracy, F1, recall, precision. • Superior performance in comparison to existing works. Sentiment analysis is indeed a difficult task owing to the playful language mannerism, altered vocabulary and speak-text used on online forums. Humans tend to use words and phrases in ways that are incomprehensible to those who are not involved in the discourse. Sarcastic remarks in conversations are often utilized to mock others by saying something that isn't pleasant. Sardonic or humorous statements/ tones are used to insult or make others appear puerile. Automated sarcasm detection is considered as one of the key tasks to tweak sentiment analysis and extending it to a morphologically rich and free-order dominant indigenous Indian language Hindi is another challenge. This research puts forward ‘ T he A malgam N eural A rchitecture’, TANA, to detect sarcasm in Hindi tweets. The architecture is trained using two embeddings, namely word and emoji embeddings and combines an LSTM with the loss function of SVM for sarcasm detection. We use the Sarc-H dataset, which is built by scrapping Hindi language tweets and manually annotating based on the hashtags ‘ ’ (pronounced as kataaksh, which means sarcasm in Hindi) and ‘ ’ (pronounced as vyangya, another word for sarcasm in Hindi) used by the tweeters and the results are evaluated using various classification performance metrics and achieves a F-score of 0.9675 outperforming LSTM using last layer as softmax as well as the existing works.

Nursing students' experiences of horizontal violence and occupational belonging during clinical placements

Background: Horizontal violence has negatively affected the students' active learning, interpersonal relationship, and occupational belonging. However, little attention has been paid to the relationship between horizontal violence and occupational belonging among nursing students.Objective: The objective of this study was to explore the frequency, types, perpetrators, effects, and responses of horizontal violence experienced by nursing students and the relationship between occupational belonging and horizontal violence.Materials and Methods: The present study used a descriptive, cross-sectional survey design. The data were collected from the 277 undergraduate nursing students selected by convenience sampling method using personal characteristics form, horizontal violence survey, and occupational belonging subscale at nursing faculty in Istanbul.Results: The first three horizontal violence behaviors experienced by nursing students were found, respectively: being forced to do trivial and unimportant work which are not the students' responsibility (84.1%), being exposed to sarcastic remarks by nurses (80.9%) and unwillingness of the nurse to help and lack of support (80.9%) and being ignored by nurses (77.3%). Nearly half of the students (48.4%) considered leaving nursing profession as a result of horizontal violence. There was a negative relation between horizontal violence and occupational belonging (P < 0.05). As horizontal violence scores increased, occupational belonging scores decreased.Conclusions: Findings from this study shown that horizontal violence decreasing occupational belonging, and so nursing students consider leaving nursing profession or education. Further studies are needed to better demonstrate addressing the horizontal violence bilaterally and the effective preventive interventions.

An Explorative Study of Financial Manipulation of Working Women through Discourse

The present study aims to find out the role of language in financial manipulation of working women. Financial manipulation refers to the unfair use of someone’s labor and financial resources. The study population comprises of the working women serving in the provincial government departments located in Dera Ghazi Khan Division in the province of Punjab, Pakistan. The sample size of 150 working women through purposive sampling technique is taken for study, and a self-administered questionnaire was served through convenience sampling technique. Dijik’s (2006) idea of manipulation through discourse as appeared in Discourse and Manipulation is taken as theoretical framework for the study. The data is analyzed quantitatively using SPSS.The study concludes that the working women in South Punjab, Pakistan are manipulated financially through various linguistic tools as they intentionally ignore their own needs in order to save family relations and to avoid sarcastic remarks. The study can be helpful for further research to be carried out in the field of women empowerment in society. It can pave the way for future study in the use of language as a tool of manipulation.

SOCIAL CRITICISM IN THE MISTER FRIMEN ANIMATED SERIES

Social criticism is generally comprised of individuals' ideas and opinions of societal problems that bring changes. Individuals can express their opinions through various media, including animated films. The animated series Mister Frimen created by Pavel Muntyan, Vladimir Ponomarev, Anatoly Dobrozan, and Vadim Demchog are notoriously sarcastic. The sarcastic remarks given by the main character are always about current problems in Russian society. This study focuses on the way the animated series Mister Frimen expresses social problems. This article uses intertextual theory and discourse analysis to analyze every message and symbol and how the messages are generated. The results show that each scene in Mister Frimen contains symbols illustrating social criticism messages about the situation of Russian society. Nonetheless, satire/social criticism offers suggestions for establishing societal peace and prosperity

Sarcasm Detection From Twitter Database Using Text Mining Algorithms

Sarcasm is well-defined as a cutting, frequently sarcastic remark intended to fast ridicule or dislike. Irony detection is the assignment of fittingly labeling the text as’ Sarcasm’ or ’non- Sarcasm.’ There is a challenging task owing to the deficiency of facial expressions and intonation in the text. Social media and micro-blogging websites are extensively explored for getting the information to extract the opinion of the target because a huge of text data existence is put out into the open field into social media like Twitter. Such large, openly available text data could be utilized for a variety of researches. Here we applied text data set for classifying Sarcasm and experiments have been made from the textual data extracted from the Twitter data set. Text data set downloaded from Kaggle, including 1984 tweets that collected from Twitter. These data already have labels here. In this paper, we apply these data to train our model Classifiers for different algorithms to see the ability of model machine learning to recognize sarcasm and non-sarcasm through a set of the process start by text pre-processing feature extraction (TF-IDF) and apply different classification algorithms, such as Decision Tree classifier, Multinomial Naïve Bayes Classifier, Support vector machines, and Logistic Regression classifier. Then tuning a model fitting the best results, we get in (TF-IDF) we achieve 0.94% in Multinomial NB, Decision Tree Classifier we achieve 0.93%, Logistic Regression we achieve 0.97%, and Support vector machines (SVM) we achieve 0.42%. All these result models were improved, except the SVM model has the lowest accuracy. The results were extracted, and the evaluation of the results has been proved above to be good in accuracy for identifying sarcastic impressions of people.

Examining the role of context in written sarcasm comprehension: Evidence from eye-tracking during reading.

This article addresses a current theoretical debate between modular and interactive accounts of sarcasm processing, by investigating the role of context (specifically, knowing that a character has been sarcastic before) in the comprehension of a sarcastic remark. An eye-tracking experiment was conducted in which participants were asked to read texts that introduced a character as being either sarcastic or not and ended in either a literal or an unfamiliar sarcastic remark. The results indicated that when the character was previously literal, a subsequent sarcastic remark was more difficult to process than its literal counterpart. However, when the context was supportive of the sarcastic interpretation (i.e., the character was known to be sarcastic), subsequent sarcastic remarks were as easy to read as literal equivalents, which would support the predictions of interactive accounts. Importantly, this effect was not preceded by a main effect of literality, which constitutes evidence against the predictions of modular accounts. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Sarcasm in Organizations: A Review and Research Agenda

Although sarcastic remarks have important implications for both employees and organizations, research on sarcasm has not received adequate attention in the organizational behavior literature. The current study aims to highlight the importance of dedicating more studies to this critical variable, stressing the theoretical foundations of linking sarcastic behavior to industrial and psychological variables. Specifically, the current study seeks to discuss this concept and indicate its psychological roles in organizations. Moreover, it investigates some research avenues that deserve scholars' effort in the form of a research agenda. This agenda offers a number of propositions that could advance conceptual insights on this important topic. They address the issue of measuring sarcastic behavior and explore the potential relationships between sarcastic remarks and employees' withdrawal and innovative behaviors. Furthermore, this review proposes a research model in which hindrance and challenge stressors mediate the relationships between sarcastic remarks and withdrawal/innovative behavior. It also presents a number of dispositional and situational variables that could moderate the relationships between sarcastic remarks and hindrance and challenge stressors, and ultimately withdrawal/innovative behavior.

Responses to Sarcasm in Three &lt;i&gt;Star Trek&lt;/i&gt; Movies

Sarcasm has been widely studied in various disciplines such as linguistics, psychology, neurology, sociology, and even cross-cultural studies. Its aggravating nature, however, often elicits various responses by the hearer. This study attempts to investigate responses to sarcasm by the characters of three Star Trek “reboot” version movies. It aims to examine responses to sarcasm and to analyze the patterns of responses to sarcastic remarks in relation to the characters’ interpersonal relationship. The data used in this research were taken from the dialogues of the movies, which were categorized into eight classes of responses: laughter, literal, zero response, smile, nonverbal, sarcasm, topic change, and metalinguistic comment. The results show that the most frequent responses conveyed by the characters were literal responses (29.41%), whereas the least frequent responses are laughter (1.96%). There is no pattern in responding to sarcastic remarks in relation to the interpersonal relationship between the interlocutors. However, strangers tend to respond in literal, zero response, and topic change. Meanwhile, close acquaintance tend to give various responses.Keywords: interpersonal relationship; pragmatics; response; sarcasm.

Poštovanje prava na privatnost žrtava saobraćajnih nesreća u online komentarima na crnogorskim portalima

The emergence of digital media on the market is accompanied by new ethical problems and dilemmas, which urges the necessity of a constant revision of codes of ethics. Technological development has led to the participatory journalism, which was supposed to result in the transformation of the media into a public forum. However, there is a clear tendency of citizens not only to exchange opinions in relation to topics of public interest, but also to comment on crime column articles, without paying attention to the victims' right to privacy and mocking those affected. If journalists violated the citizens' right to privacy with sensationalist reporting about crime and interviews with controversial figures in the 1830s, then today citizens are the ones violating the right to privacy of crime and accident victims by disclosing details about those involved in the events, while the media do not try to tackle this problem through internal codes of ethics. On the basis of content analysis of the comments posted on the two most visited online portals in Montenegro (Vijesti and Analitika), in this paper we aim to determine to what degree the media, but also the citizens, respect the right to privacy of the victims of traffic accidents. The results show that both portals take care of the ethical dimensions of the content of online comments: they are dominated by ethical comments in which the citizens express their views. With much higher number of comments than Analitika, portal Vijesti has a harder task and it allowed for publication the comments which contain sarcastic remarks about the texts.

Developing Appreciation for Sarcasm and Sarcastic Gossip: It Depends on Perspective.

Speakers use sarcasm to criticize others and to be funny; the indirectness of sarcasm protects the addressee's face (Brown & Levinson, 1987). Thus, appreciation of sarcasm depends on the ability to consider perspectives. We investigated development of this ability from late childhood into adulthood and examined effects of interpretive perspective and parties present. We presented 9- to 10-year-olds, 13- to 14-year-olds, and adults with sarcastic and literal remarks in three parties-present conditions: private evaluation, public evaluation, and gossip. Participants interpreted the speaker's attitude and humor from the addressee's perspective and, when appropriate, from the bystander's perspective. Children showed no influence of interpretive perspective or parties present on appreciation of the speaker's attitude or humor. Adolescents and adults, however, shifted their interpretations, judging that addressees have less favorable views of criticisms than bystanders. Further, adolescents and adults differed in their perceptions of the social functions of gossip, with adolescents showing more positive attitudes than adults toward sarcastic gossip. We suggest that adults' disapproval of sarcastic gossip shows a deeper understanding of the utility of sarcasm's face-saving function. Thus, the ability to modulate appreciation of sarcasm according to interpretive perspective and parties present continues to develop in adolescence and into adulthood.

Indian Topic in R. Kipling’s early Creative Art («Plain Tales from the Hills», 1888): An Alternative View

This article presents the study of Rudyard Kipling’s early pieces of writing (the collection of stories Plain Tales from the Hills, 1888). The author proposes an alternative view to the consideration of the writer’s literary heritage from the position of jingoism and propagation of the civilizing mission of the British Empire, which can still be encountered in academic research. The analysis of Plain Tales from the Hills suggests that the praise of British imperialism was not the main idea of Kipling’s early works. The researcher comes to the conclusion that Kipling did not regard India as a conquered barbarous land which the British people had to civilize. This remote exotic country became a motherland for a representative of Anglo-Indian society, who was born in Indian environment. Kipling’s love for India is felt in his strive to give a detailed description of exotic locations and ethnographic peculiarities and even to restore the manner of speech of Indian population. The reader of Plain Tales from the Hills can perceive the author’s respect for the English and Indian people, working in harsh climate, his interest and great sympathy with the aboriginal population, living in hard social conditions under the British rule. The sarcastic remarks of his characters, which reflect Kipling’s doubt in the beneficial role of the British Empire in the lives of the Indian people, finally refute the statement about the writer’s glorification of the imperialistic policy of Great Britain as the main aim of his creative art.