Offensive Words Research Articles

Transforming medical students’ speaking-up behaviors in medical errors: The impact of simulation and personalized debriefing

Introduction Sharing mental models is essential for high-performance teams, and speaking up is key for exchanging critical insights, especially during medical errors. Understanding how health providers and trainees voice their concerns is crucial for improving speaking-up behavior. This study aims to fill a gap in the literature by examining how medical students speak up when they encounter medical errors and assessing the impact of training on their speaking-up patterns. Method A quasi-experimental study involving 146 students, who were divided into two groups, was conducted in Northern Taiwan. One group of students encountered life-threatening scenario before intervention, followed by a faculty-led personalized debriefing session, then a non-life-threatening scenario after the intervention. Another group of students underwent these sessions in the reverse order. Students’ Speaking-up patterns, including expression style, form and attitude, and their speaking-up confidence were assessed at pre- and post-intervention scenarios. Results During pre-intervention scenario, in expression style, 50 students (34.5%) addressed their concerns to medical errors with direct expression and 14 students (9.7%) utilized indirect hint to express their concerns. In expression form, 31 students (21.4%) addressed their concerns to medical errors with affirmative sentences and 33 students (22.8%) asked questions to express their concerns. In speaking-up attitude, 47 students (32.4%) used unoffensive words, while 17 students (11.7%) used offensive words. After intervention, significantly change of speaking-up styles, forms, and attitude were observed along with their speaking-up confidence (p < 0.001). Discussion Medical students are inclined to speak up in the event of medical errors using more direct expression and affirmative sentences, along with increased speaking-up confidence after simulation scenario learning and faculty-led personalized debriefing. Healthcare educators can focus more on discussing with students the advantages and disadvantages of various approaches of speaking-up in medical errors, helping them to develop effective speaking-up behaviors in a variety of medical contexts.

Digital Misogyny Unveiled: An Examination of Online Hate Speech Targeting Women in Bangladesh

Discriminatory and sexist language expresses bias towards men and projects that women are inferior around the world. It is also evident in the patriarchal society of Bangladesh. While they are often prevalent subtly in communication taking place in person, the anonymous nature of the internet has enabled people to be vile towards women in the virtual world. Online hate speech and misogynistic behavior have become pervasive issues in today's digital landscape. Most people take Islamic verses literally or believe the religious clerics without checking their words' authenticity. The socio-religious idea of women being subservient to men produces numerous hate speeches against women on social media platforms. The presence of numerous online religious lectures spewing offensive words against women made the situation worse in Bangladesh. They conform to the prevalent idea that men should control women's agency, and for that purpose, even violence is acceptable. This article aims to provide a comprehensive analysis of the phenomenon, specifically focusing on the context of Bangladesh. Through case studies, it looks into how religious leaders fuel online hatred towards women.It further explores the various forms, impacts, and underlying factors of misogynistic online speech. By delving into the complexities of this issue, it also discusses the challenges faced by women in seeking redress and proposes recommendations to mitigate online violence against women in Bangladesh.

Estų kalbos planavimo rekomendacijos dėl žodžių reikšmių – pagrįstos ir būtinos, ar ne?

On the example of Estonian language planning, this research paper explores whether it is justified and necessary to give recommendations for the meanings of words. The focus is on the general language. Technical language is out of the scope of this research. First, a brief overview of the history of Estonian language planning and the development of Standard Estonian, as well as the current situation is provided. Then the paper focuses on recent research on the meanings of words in Estonian. The purpose was to revise the recommendations for the meanings of words by Estonian language planning. The research is based on the common approaches in Estonian linguistics today – the usage-based linguistics and corpus linguistics. The paper argues that recommendations by language planning for the meanings of words are not justified nor necessary in the general language, even if some explanations on the choice of words are relevant (e.g., for offensive words).

Cognition and gender differences in cyberbullying in China: an investigation of textual cues and sentimental clues

Purpose Cyberbullying is a globally pervasive problem and not novel in academia. Previous studies mainly focussed on the features, consequences and technological management of cyberbullying. However, most of the studies took cyberbullying examples in the West, and some issues still need to be addressed in the Chinese context. Thus, this study investigates how participants use cyberbullying words and why they use them in that particular way in China.Design/methodology/approach This study uses content analysis to summarise the typical features of cyberbullying words, revealing a positive relationship between cyberbullying words and sentimental expressions. This paper also uses the questionnaire (N = 705) to explore the prevalence of cyberbullying among Chinese Internet users and to compare the gender differences in the cognition of cyberbullying words and involvement in cyberbullying, in line with the social cognitive theory.Findings This study stated that people prefer repetitively using offensive words to achieve cyberbullying goals. Interestingly, this study does not find obvious gender differences in cyberbullying roles and cyberbullying language use. However, it explained the relationship between cognition and the use of cyberbullying words from a gender perspective.Practical implications Theoretically, this study expands cyberbullying studies into a new cultural environment, pointing to a novel term, “imbalanced relation,” for exploring cyberbullying behaviours. This study highlights the significance of technology and education in detecting and preventing cyberbullying, suggesting that educators and social media platforms can directly predict and prevent cyberbullying through textual perspectives and individuals' cognition of cyberbullying.Originality/value This study aims to examine cyberbullying linguistic and emotional features and individual differences in cyberbullying behaviour in a high-context culture like China. Its values include comparing the differences between cyberbullying in China and cyberbullying in the West from the linguistic and cultural directions and reconsidering the “power imbalance” feature of cyberbullying.

Using Offensive Words as Unethical Advertising in Viral Marketing at Angkringan West Semarang

Communication strategy is the main factor for doing marketing. Likewise, what was conducted by Angkringan West in order to increase the number of followers and customers in its culinary business, a communication strategy was chosen using offensive words which were considered controversial. The purpose of this study was to analyze the marketing communication strategy concept of the owner of Angkringan West interested in using offensive words in conducting marketing communications, to analyze the considerations and the expected impact of using offensive words as unethical advertising on Angkringan West. This research was conducted using a type of qualitative research. As for the results of the study, it was found that 1) the consideration of using offensive words was adapted to use the language of social communication, namely the Semarangan language which flows spontaneously; 2) choosing the strategy of using offensive words is not to cause discomfort, impolite or unethical in trading, but the acceptance of offensive words depends on the demographics of the audience and the willingness to join the Angkringan West community. Conclusion: offensive words are an appropriate marketing strategy to use with certain marketing targets.

Have media texts become more humorous?

As a research topic, humour has drawn much attention from multiple disciplines including linguistics. Based on Engelthaler &amp; Hills’ (2018) humour scale, this study developed a measure named Humour Index (HMI) to quantify the degree of humour of texts. This measure was applied to examine the diachronic changes in the degree of humour of American newspapers and magazines across a time span of 118 years (1900-2017) with the use of texts from Corpus of Historical American English (COHA). Besides, the study also discussed the contributions of different types of words to the degree of humour in the two genres. The results show significant uptrends in the degree of humour of both newspapers and magazines in the examined period. Moreover, derogatory and offensive words are found to be less frequently used than other categories of words in both genres. This study provides both theoretical and methodological implications for humour studies and claims or hypotheses of previous research, such as infotainment and linguistic positivity bias.

Corpus-Based Approach to Sociolinguistic Study of Offensive Words: Gender, Time and Register Differences

The unpleasant expressions (offensiveness) always emotionally affect to the human psyches, and exists in all languages in less or strong degrees and directly or indirectly. Therefore, this study aimed to investigate over the changes of them by corporally-derived data and their registers, times and genders were the pilot targets. BNC and COCA Corpora were put for data collections while the list of previous insulting words was selected to reuse, especially the ones with highest frequencies. The results suggested 4 words damn, shit, fuck and dick had the highest degrees of uses but those from BNC were comparatively fewer than the rest. Moreover, the top four were emerged up to the different periods of times and contexts while men used them considerably more often than the women did.

CREATING HATE SPEECH DETECTION MODEL BY USING MACHINE LEARNING METHODS

This research paper describes the using machine learning algorithms to pre-identify profanity in online content due to concerns about the growing use of profanity on social networks such as Twitter. In this article, comparative studies were carried out to develop special software using machine learning methods for effective detection and monitoring of hate speech in order to minimize the negative impact of hate speech on people and communities. The scientific novelty of this research work an algorithm defined that provides the most effective accuracy of automatic detection of obscene words among machine learning algorithms. The result of the experiment, the effectiveness of the above methods for detecting offensive words evaluated on Twitter. The purpose of this article is to compile a data set of hate speech collected from various sources and approved by experts to ensure that the obscene words detection model works effectively. The practical significance of this research work, using machine learning methods, a comprehensive method for detecting profanity on Twitter proposed, a detection model and software for recognizing complex elements of profanity in online comments are also provided.

Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT

The prevalence of cyberbullying on Social Media (SM) platforms has become a significant concern for individuals, organizations, and society as a whole. The early detection and intervention of cyberbullying on social media are critical to mitigating its harmful effects. In recent years, ensemble learning has shown promising results for detecting cyberbullying on social media. This paper presents an ensemble stacking learning approach for detecting cyberbullying on Twitter using a combination of Deep Neural Network methods (DNNs). It also introduces BERT-M, a modified BERT model. The dataset used in this study was collected from Twitter and preprocessed to remove irrelevant information. The feature extraction process involved utilizing word2vec with Continuous Bag of Words (CBOW) to form the weights in the embedding layer. These features were then fed into a convolutional and pooling mechanism, effectively reducing their dimensionality, and capturing the position-invariant characteristics of the offensive words. The validation of the proposed stacked model and BERT-M was performed using well-known model evaluation measures. The stacked model achieved an F1-score of 0.964, precision of 0.950, recall of 0.92 and the detection time reported was 3 min, which surpasses the previously reported accuracy and speed scores for all known NLP detectors of cyberbullying, including standard BERT and BERT-M. The results of the experiment showed that the stacking ensemble learning approach achieved an accuracy of 97.4% in detecting cyberbullying on Twitter dataset and 90.97% on combined Twitter and Facebook dataset. The results demonstrate the effectiveness of the proposed stacking ensemble learning approach in detecting cyberbullying on SM and highlight the importance of combining multiple models for improved performance.

Cyberbullying Detection on Twitter Using Deep Learning-Based Attention Mechanisms and Continuous Bag of Words Feature Extraction

Since social media platforms are widely used and popular, they have given us more opportunities than we can even imagine. Despite all of the known benefits, some users may abuse these opportunities to humiliate, insult, bully, and harass other people. This issue explains why there is a need to reduce such negative activities and create a safe cyberspace for innocent people by detecting cyberbullying activity. This study provides a comparative analysis of deep learning methods used to test and evaluate their effectiveness regarding a well-known global Twitter dataset. To recognize abusive tweets and overcome existing challenges, attention-based deep learning methods are introduced. The word2vec with CBOW concatenated formed the weights included in the embedding layer and was used to extract the features. The feature vector was input into a convolution and pooling mechanism, reducing the feature dimensionality while learning the position-invariant of the offensive words. A SoftMax function predicts feature classification. Using benchmark experimental datasets and well-known evaluation measures, the convolutional neural network model with attention-based long- and short-term memory was found to outperform other DL methods. The proposed cyberbullying detection methods were evaluated using benchmark experimental datasets and well-known evaluation measures. Finally, the results demonstrated the superiority of the attention-based 1D convolutional long short-term memory (Conv1DLSTM) classifier over the other implemented methods.

Impacts of President Trumps Zero Tolerance Policy and Speech on Illegal Immigrants

Under Trumps administration, there were many brave policies and speeches that are controversial enough for American founding thoughts which call for equality in the society and the development of the United States. This paper will uncover the influence of immigration policies from President Trump by listing and analyzing the ways that will marginalize illegal immigrants. This paper provides a close scope of the difficulties faced by illegal immigrants because of the harsh immigration policies, and how they will be marginalized in the future due to the unrestricted and offensive words from the speeches of President Trump. The results of the research are that new immigration policies are negative toward the illegal immigrants of the The United States obviously results in the mental health problem of the illegal immigrants and brings fatal damage to the illegal immigrant families. Also, these people who make speeches, due to their own benefit, never pay attention to the possible worse situation for the illegal immigrants, marginalizing the weak group.

Curtailing fake news creation and dissemination in Nigeria: Twitter social network and sentiment analysis approaches

Influencers create and facilitate dissemination of information in a network and can shape the attitudes and beliefs of members of a social network. Identifying the influencers and their relations, as well as the sentiment of tweets being disseminated in the network on some selected keywords can help curtail fake news creation and dissemination in the network. This study uses a sequential mixed-methods design with a quantitative method followed by qualitative methods. The quantitative data were collected using Mozdeh big data analysis software. Mozdeh software was used to collect tweets through Twitter’s Streaming application programming interface to build a corpus of tweets, collected from 1 January 2016 to 30 June 2021. The study found two major actors/influencers involved in the creation and dissemination of fake news on the tweeter social network studied. The study further found overall Av. Pos. − Av. Neg. was −0.9935. Data collected were on some specific trending keywords on a particular region in Nigeria. Identifying and monitoring the tweets of influencers in a network can aid in debunking fake news immediately after dissemination and discourage the use of offensive words in tweets. The results revealed major influencers responsible for creating and disseminating fake news on some trending issues in Nigeria.

Automated offensive language classification through “emotional” comments from network users

In recent years, online hate speech has become widespread as a result of the ease with which any online opinion can be presented on social media. This trend is mainly due to the rapid growth of mobile devices, computers and the Internet. There is a lot of interest in research on automatic hate speech detection. However, there is not a lot research on whether social networking sites can help identify hate crimes and related communities. Hate speech has a negative impact on the mental level of any person. However, some people are more prone to emotional instability than others. It is difficult for racial or ethnic groups whose language is not popular automatically collecting and evaluate texts created on the Internet. In this article, using artificial intelligence techniques in text processing and natural language processing, for the first time, a database was collected using various keywords to identify offensive words in social networks.

A Linguistic Study of Offensive Language in Online Communication Chatgroups

Since offensive language, words and expressions are widely used nowadays on the internet; the current paper is an attempt to discover and investigate offensive language that is used in one medium of electronic communication, which is “chatgroups”. The study focuses on analyzing and explaining offensive words and expressions found in chatgroups with their types and functions. The data of this study are a type of screenshots, (20) screenshots are randomly gathered from synchronous chatrooms. The analyses reveal different types of offensive language; these types are (vulgarity, insult, epithet, taboo, obscenity, and profanity). Besides, the functions of these offensive words and expressions are (body part, sexual, connotative, metaphoric, and expletive).

Fast Heuristic Detection of Offensive Words in Wordwheel Puzzles

Offensive words appear in Wordwheel-type puzzles with a high frequency. Previous approaches to eliminating these words have focused largely on eliminating puzzles that might give rise to an offensive word. This work presents a fast, heuristic approach to detecting an offensive word within a puzzle. After a preprocessing stage, the detection occurs with a single bitwise operation on a 64-bit word. Tests show that as long as there are at least 3 taboo words possible in a puzzle, the heuristic approach is faster than a depth-first search of the puzzle. In addition to being fast, the approach is guaranteed to detect all offensive words, and has a low false positive rate.

Explicit and implicit offensiveness in dialogical film discourse in Bridgit Jones films

Abstract The paper discusses the concept of offensiveness, both explicit and implicit, in film dialogical discourse based on three parts of a romantic comedy. The differences between explicitness and implicitness on the one hand, and between implicitness and (in)directness on the other are presented in the theoretical part. Indirectness and implicitness are treated in the study as independent concepts that instantiate covert meaning. On the other hand, explicitness and implicitness are viewed as gradual concepts that allow some overlap; thus, direct implicitness and indirect explicitness emerge as possible options. Furthermore, the category of offensiveness is presented as a broad category, a superordinate term that subsumes offensive language, typically realised through explicitly offensive words, such as swearwords, and (non)offence, encoded by rhetorical devices. Offensive language can have the function of offending the target addressee, i.e., to cause offence, or to build, inter alia, a jocular, intimate or friendly atmosphere. Offensiveness can thus embrace propositions that lead to offence or convey other, non-offensive meanings. Examples of both offensive language (explicit/direct forms) and subtypes of offence (figurative forms), as well as a combination of both (e.g., figurative forms such as ironic comments that contain swearwords), are gleaned from the corpus of three parts of the eponymous romantic comedy. The analysis has shown that figurative forms are often conflated (to create metaphorical irony, ironic hyperbole, and the like), the implicit forms of offensiveness occur almost as frequently as explicit forms and are distributed equally across gender, varying forms of offensiveness play the whole gamut of functions, disparagement being only one of them.

Can the New Testament be blamed for unfair discrimination or domination in modern societies?

For many Christians the word patriarchy became an offensive word as it is seen as a system ‘created by men to serve men’ with men in positions of domination, creating a social system that ‘left women as victims’. Patriarchy hurts society in many ways – it creates the opportunity for violence, rape, it suppresses women, deprives women of freedom of choice, and creates identity crises for both men and women as it steals humankind’s freedom to choose their roles to be played in society. Who or what is to be blamed for the eternalisation of patriarchy throughout history? As religion is one of the most important agents of socialisation and social control, playing a significant role in organising and directing social life, the underlying patriarchy in the New Testament is very often blamed for inequality in our societies.Contribution: This article focussed on the question who or what was to be blamed for discrimination and domination in societies. It contributed by arguing that both ‘male-dominion’ and ‘equality of men and women’ are portrayed in the New Testament. It was claimed that with resistance to patriarchy came the challenge on how to interpret the Bible and that it was all about hermeneutics. It seemed as if the New Testament as such was not to be blamed for the reinforcement of patriarchy in our modern societies, but the interpretation of the New Testament. The author therefore pleaded for a hermeneutics with a focus on all three elements in the process of communication, namely author and background, text and the reader. A method with a focus on all elements in the process of communication was used as an example of how it could be done, without being caught in over-contextualisation and the distortion of the biblical message.

The Usage of Swear Word in Hollywood Films

Language is used as a means of communication in social intercourse. It can be used to propose one’s ideas, deliver particular messages, and to express emotions. One of many ways of using language is by producing swear word. Swear word is an offensive word which often used to express humiliation, anger and any other tense emotions. This study concerns with the descriptive analysis and function of swear word found in some Hollywood films like Fast and Furious 6, Suicide Squad, Bad Boys II, and Deadpool. This study was aimed at examining the use of swear word in the movies with different topics and characters. The objectives if this study were to find out the how many using swear word in the movies. This study was conducted by descriptive qualitative method. The source of data was transcript the movies.

Sarcasm Detection in Online Social Networks Using Machine Learning Methods

Our lives have completely changed since the Internet came into our lives. Role models for people are not only the people around them but people all over the world. Although there are positive aspects of this situation, we will deal with the negative aspects in this study. One of these negative aspects is that people share their ideas on social networks without any supervision. In this way, people who use social networks are told offensive words by people they do not know in real life. Sometimes these words are not directly insulting, but they are expressed sarcastically and annoy the interlocutor. In this study, the detection of sarcastic words in social networks is considered a classification problem. Since the data type used in the proposed method is text-based, both text mining and machine learning methods are used together. In this study, the sarcastic word classification process was carried out using a data set obtained from the Twitter social network, which includes two public classes. The performance of the proposed method was obtained with the Random Forest algorithm with an accuracy of 94.9%.

PSYCHOLINGUISTIC PECULIARITIES OF VERBAL AGGRESSION IN ENGLISH POLITICAL DISCOURSE

Psycholinguistic peculiarities of verbal aggression in English political discourse. The article examines aspects of speech aggression that allows social pressure to be exercised in the conditions of the asymmetric social status of participants in verbal communications. The factors of linguistic objectification are investigated from the perspective of age, social, gender, personal characteristics and characteristics of subjects of discourse. As an example of verbal aggression, the speech acts of public speeches of the incumbent President of the United States of America D. Trump, which are considered as an integral part of an individual's activities, due to high semantic significance and the necessary level of interpersonal interaction, were subjected to linguistic analysis. The use of aggression in Trump's political discourse is is guided by reason, not emotion. His consistent use of derogatory nicknames, constant attention to the media, as well as the selection of offensive words that draw attention to the specific shortcomings of his addressees indicate that aggression is not based on impulsiveness or randomness. Verbal aggression aimed at lowering the opponent's social status is one of the methods of an offensive strategy and affects, sometimes significantly, the decision-making of an offended person. The use of methods of verbal aggression by D. Trump in the context of political discourse can be a powerful "weapon" both in relation to the internal political situation and in the external political arena. Such aspects, in the context of the increasing influence of forms of digital culture on the psycho-emotional sphere of a person, leave researchers with ample opportunities in the field of scientific research.