Profane Research Articles

The immigrant linguistic maturation of Asian American and Latinx language brokers

AbstractImmigrant children in the U.S. often learn English before their caretakers, leading them to take on the role of day‐to‐day translators (“language brokers”). This study explores the familial socialization of immigrant, linguistic‐minority families in the U.S. by drawing on deductive‐inductive thematic analysis of 14 semi‐structured interviews with Asian American and Latinx young adult language brokers reflecting on how this role shaped their childhoods and prepared them for adulthood. The bulk of interviewees experienced working‐class childhoods. Despite this, respondents seem to have experienced a family socialization model that reflects elements of both middle‐class and working‐class models. “Immigrant Linguistic Maturation” (ILM) consists of linguistic scaffolding in English and heritage languages, verbal airtime, and engagement with authority figures, while also leading children to hold adult knowledge, roles, and responsibilities. Racial and ethnic differences primarily lie in the actors involved in ILM socialization processes. Extended family, and especially grandparents, played a more active role in the ILM socialization of Asian American brokers, while ILM socialization of Latinx brokers was primarily driven by parents, particularly mothers. The case of Asian American and Latinx language brokers calls attention to the importance of factors like immigrant background and linguistic marginalization in shaping familial socialization.

UNVEILING THE VEIL: EXPLORING GENDERED ABUSIVE LANGUAGE IN CONTEMPORARY PUNJABI DISCOURSE

The pervasive use of coarse language, despite its prohibition grounded in cultural values and traditions, presents a complex societal phenomenon that warrants in-depth exploration. The ubiquitous nature of this linguistic shift is particularly evident in daily verbal exchanges and media dissemination, challenging the conventional understanding of language as a mere preserver of cultural and religious identity. Abusive language, which finds expression in moments of surprise, frustration, and happiness, has become a multifaceted tool that not only defies established norms but also plays a pivotal role in shaping social dynamics. This paper's specific focus on profanity within the Punjabi language community serves as a microcosm for understanding broader issues related to gendered language. Through a comprehensive study involving interviews with Punjabi respondents aged 21 and above, encompassing both genders, the research exposes the deeply ingrained gendered nature of coarse language. Females are frequently and directly associated with insults and curses, highlighting a troubling pattern that reinforces the societal devaluation of women. The findings underscore how profanity, beyond its linguistic implications, acts as a mechanism that perpetuates and strengthens the existing patriarchal system within the Punjabi community. As society grapples with evolving language norms, this examination sheds light on the intricate interplay between language, gender dynamics, and cultural practices, providing valuable insights for addressing the broader implications of linguistic shifts in contemporary society.

Analysis of Offensive Language Use by South Korea’s Two Major Political Parties: Using Party Press Releases from 2007 to 2023

Analysis of Offensive Language Use by South Korea’s Two Major Political Parties: Using Party Press Releases from 2007 to 2023

Safeguarding Online Communications using DistilRoBERTa for Detection of Terrorism and Offensive Chats

People use social media for both good and distasteful purposes. When used with malicious intent, it raises significant concerns as it involves the use of offensive language and hate speech that promote terrorism and other negative behaviors. To create a safe, secure and pleasant environment, these communications must be closely monitored to prevent severe problems, associated risks and other pertinent issues. With the help of AI, specifically Large Language Models (LLM), we can quickly analyze text and speech to determine whether the communications promote the dangers identified here above not to mention other toxic elements. For this research, the LLM used is the DistilRoBERTa model from the Transformers library using Hugging Face. The DistilRoBERTa model was trained on datasets consisting of terrorism-related conversations, offensive-related conversations, and neutral conversations. These datasets were obtained from publicly available sources. The results of the experimentation show that the model achieved 99% accuracy, precision, recall, F1 score, and ROC curve. To improve the robustness of the model, it must be continuously fine-tuned to predict dynamic communication behavior since real conversations are inaccessible due to restrictions. A drag-and-drop interface is used to upload the files and get the categorical output, ensuring seamless and easy interaction.

Self-Directed Learning in Adult Second Language Acquisition

The pervasive challenge in adult education is achieving effective second language acquisition through self-directed learning (SDL). As globalization intensifies, the demand for bilingual proficiency emerges, yet traditional educational structures often fall short in fostering necessary language skills among adults. This investigation aims to dissect the efficacy of SDL in adult second language learners, focusing on the specific factors that influence successful outcomes. Our methodology embraced a qualitative approach, concentrating on an exhaustive bibliographic review to uncover nuances in SDL practices and outcomes. This included the analysis of 49 scientific articles sourced from recognized academic databases, ensuring a robust foundation for our insights. Results from the study indicated that intrinsic motivation, access to technology, and effective use of strategic learning practices are paramount in enhancing language proficiency through SDL. However, barriers such as inadequate digital literacy and economic disparities significantly impact learning outcomes. In conclusion, while SDL presents a viable pathway for adult language acquisition, its success is heavily dependent on overcoming technological and motivational hurdles. Tailored educational strategies and support systems are essential to maximize the benefits of SDL.

Evaluating the accuracy of automated processing of child and adult language production in preschool classrooms.

Young children's language and social development is influenced by the linguistic environment of their classrooms, including their interactions with teachers and peers. Measurement of the classroom linguistic environment typically relies on observational methods, often providing limited 'snapshots' of children's interactions, from which broad generalizations are made. Recent technological advances, including artificial intelligence, provide opportunities to capture children's interactions using continuous recordings representing much longer durations of time. The goal of the present study was to evaluate the accuracy of the Interaction Detection in Early Childhood Settings (IDEAS) system on 13 automated indices of language output using recordings collected from 19 children and three teachers over two weeks in an urban preschool classroom. The accuracy of language outputs processed via IDEAS were compared to ground truth via linear correlations and median absolute relative error. Findings indicate high correlations between IDEAS and ground truth data on measures of teacher and child speech, and relatively low error rates on the majority of IDEAS language output measures. Study findings indicate that IDEAS may provide a useful measurement tool for advancing knowledge about children's classroom experiences and their role in shaping development.

Pemanfaatan Media Pembelajaran Audio Visual Berbasis Channel Youtube dalam Pembentukan Karakter Anak di Taman Pendidikan Al-Qur’an

Character education should be implemented from an early age because early age really determines a child's ability to develop their potential. Problems found at Al-Ikhsan TPA include not respecting ustadz, rarely reciting the Koran, behaving impolitely, and there are still people who say dirty words at Al-Ikhsan TPA, Terbanggi Besar District. This research is field research with a qualitative descriptive nature, while the data analysis method uses a deductive method. Utilization of Audio Visual Learning Media Based on YouTube Channels in Forming Children's Character at Al-Ikhsan TPA, namely First, Introduction by preparing equipment including TV, Wifi and YouTube channels. Second, Exploration, namely students have diverse characters, activated through active, innovative, creative, effective and fun learning methods. Third, restructuring, namely the concept of learning by watching YouTube channels and continuing with the question and answer method. Fourth, implementation, namely a 60 minute learning process by broadcasting a YouTube channel about the stories of prophets and apostles. Fifth, Review or Evaluation, namely a question and answer session to evaluate children's understanding of the meaning contained in the YouTube channel. The use of audio visual media based on YouTube channels can shape the character of children at Al-Ikhsan TPA, especially children who have religious, honest, curious, hard working, social care and responsibility characters. Learning using the YouTube channel has several obstacles, including an unstable network and bad weather, resulting in changes to the learning schedule. Therefore, researchers provide recommendations in the form of adding a learning schedule by utilizing the YouTube channel 2-3 times a week, this is done so that children's character formation becomes more effective and optimal.

Telugu language hate speech detection using deep learning transformer models: Corpus generation and evaluation

In today's digital era, social media has become a new tool for communication and sharing information, with the availability of high-speed internet it tends to reach the masses much faster. Lack of regulations and ethics have made advancement in the proliferation of abusive language and hate speech has become a growing concern on social media platforms in the form of posts, replies, and comments towards individuals, groups, religions, and communities. However, the process of classification of hate speech manually on online platforms is cumbersome and impractical due to the excessive amount of data being generated. Therefore, it is crucial to automatically filter online content to identify and eliminate hate speech from social media. Widely spoken resource-rich languages like English have driven the research and achieved the desired result due to the accessibility of large corpora, annotated datasets, and tools. Resource-constrained languages are not able to achieve the benefits of advancement due to a lack of data corpus and annotated datasets. India has diverse languages that change with demographics and languages that have limited data availability and semantic differences. Telugu is one of the low-resource Dravidian languages spoken in the southern part of India.In this paper, we present a monolingual Telugu corpus consisting of tweets posted on Twitter annotated with hate and non-hate labels and experiments to provide a comparison of state-of-the-art fine-tuned deep learning models (mBERT, DistilBERT, IndicBERT, NLLB, Muril, RNN+LSTM, XLM-RoBERTa, and Indic-Bart). Through transfer learning and hyperparameter tuning, the models are compared for their effectiveness in classifying hate speech in Telugu text. The fine-tuned mBERT model outperformed all other fine-tuned models achieving an accuracy of 98.2. The authors also propose a deployment model for social media accounts.

Holistic Approaches to Bullying Prevention: The Mediating Role of School Well-Being, Self-Management, and Empathy

This research aims to examine the influence of self-management and empathy on the tendency of bullying behavior among teenage students through the mediating role of the school well-being variable for students in the three largest Islamic boarding schools in Jember Regency, East Survey Province. This research is a non-experimental quantitative research with a survey design. A total of 160 randomly selected students participated in this survey research. The data that has been collected is then analyzed using the WARP PLS 7 application and the IBM Statistical Package for Social Science (SPSS) version 23 application. The analysis results using WARP PLS 7. Found values that show that: 1) self-management influences the sixth tendency of bullying through school. Well-Being is 13.5%. This result means that the higher the student’s self-control, demonstrated by the ability to control their emotions when teased by friends, the better the social relations between students, teachers, and school employees, which will impact their behavior in avoiding the use of bad words. Put down friends you do not like. 2) empathy influences the tendency for bullying behavior through School Well Being by 23.8%. This result means that the higher the perspective shown by the attitude of trying to see a problem from another person’s point of view, the better the social relations between students and teachers and school employees so that it has an impact on behavior to avoid using bad words to demean friends who are not liked.

A Scoping Review of Research on English Academic Writing for Chinese Graduate Students

Based on 16 studies on Chinese graduate students' English academic writing, this paper summarizes the current research trends in China from the aspect of research methods and content. After combing and summarizing, it is found that: (1) most relevant researches in China have adopted three forms of research methods, which are: questionnaire survey or interview survey, classroom observation and text analysis. (2) currently, challenges in the English academic writing process for Chinese graduate students primarily revolve around inappropriate language usage and insufficient writing skills. (3) The solutions proposed at present mainly include improving students' writing ability in practical exercises, implementing new teaching models and introducing new teaching methods. It is helpful to find and summarize the problems existing in the current English academic writing courses in China, as well as the difficulties existing in the process of English academic writing for Chinese graduate students, so as to help them improve their English academic writing ability.

Curse words and turpiloquies galore: streaming services and the new translation norms for subtitling texts

Curse words and turpiloquies galore: streaming services and the new translation norms for subtitling texts

Hearing parents as sign language learners: describing and evaluating the ASL skills of parents learning ASL with their deaf children

ABSTRACT Hearing parents who endeavour to learn American Sign Language (ASL) to communicate with their deaf children are a unique but understudied population of adult language learners. In this study, we assessed the expressive and receptive ASL skills of hearing parents of deaf children (n = 55). Expressive ASL skills were evaluated through elicited language samples, and scored using adapted rubrics from the Common European Framework of Reference for Languages. For a subset of participants (n = 20), an additional language sample was obtained and coded for lexical and grammatical features of ASL. Receptive ASL skills were measured using the ASL-Comprehension Test. Across measures, participants demonstrated language skills in the beginner to intermediate range. Parents’ signing samples showed a range of ASL linguistic features including classifier constructions. Our findings suggest that hearing parents can learn ASL and should be encouraged to do so to support language development in their deaf children.

Masked Translation Priming Effects for Chinese-English-Japanese Triple Cognates in Lexical Decision Tasks.

Previous research has demonstrated cognate translation priming effects in masked priming lexical decision tasks (LDTs) even when a bilingual's two languages have different scripts. Because those effect sizes are normally larger than with noncognates, the effects have been partially attributed to the impact of prime-target phonological similarity. The present research extended that work by examining priming effects when using triple different-script cognates, i.e., /ka1 feɪ1/-coffee-コーヒー/KoRhiR/. Specifically, masked cognate priming effects were examined in six different priming directions (i.e., L1↔L2, L1↔L3, and L2↔L3) for Chinese-English-Japanese trilinguals using LDTs. Significant priming effects were observed only when the primes were from the stronger language. This asymmetric pattern suggests that the phonological similarity of cognate primes only facilitates the processing of different-script triple cognates to the extent that the processing of the prime is robust enough to make phonology available before target processing is finished.

Exploring the influence of user characteristics on verbal aggression towards social chatbots

ABSTRACT Chatbots possess great potential benefits, yet concerns persist regarding users adopting inappropriate, offensive language. This research delved into the influence of user characteristics on verbally aggressive behaviours towards social chatbots. Employing a mixed-method study, we examined individual characteristics such as personal dispositions, offensive language patterns, academic majors, and prior experiences with conversational agents. Findings from a ten-day field experiment involving 33 participants using a real-world Telegram-based chatbot app unveiled that users' anthropomorphism, computer-related major, and gender significantly impact their moral emotions and evaluations of the chatbot's capabilities. Moreover, employing offensive language towards the chatbot detrimentally impacted users' perceptions of its abilities, helpfulness, and likability. The research findings advocate for ongoing monitoring and effective resolution of users' behaviours regarding the use of offensive language in their interactions with a chatbot. Additionally, the results underscore the importance of incorporating diverse perspectives into chatbot design to address biases and offensive utterances.

Evaluating the delivery of physical activity for people with developmental disabilities using an online knowledge translation approach: part 2 – content quality

Objective Web-based platforms for delivering physical activity (PA) to people with developmental disabilities have a great potential to improve the lives of many. However, their design, including the content design, lacks sufficient investigation. This study aims to evaluate three online platforms for delivering PA to people with developmental disabilities in terms of content quality and identify relevant barriers and facilitators of PA delivery. Methods The study used a methodological triangulation approach which involved quantitative evaluations by experts using an evaluation rubric, on-site observations and in-depth interviews with recruited participants. The participants consisted of 15 pairs of individuals, each consisting of a person with developmental disabilities and their primary caregiver. They were instructed to watch and follow five PA video content from each of the three platforms. The on-site observations and interviews were conducted in a large computer-equipped meeting room setting. Results The quantitative and qualitative analysis identified a set of barriers and facilitators of PA delivery related to content quality. Key barriers identified include a lack of content diversity, insufficient understanding of developmental disabilities among content creators, inappropriate language usage, and resistance to engaging in physical activities in home settings. Significant facilitators were pinpointed, such as incorporating engaging elements for individuals with developmental disabilities, utilising easy-read language, and ensuring sufficient repetition for effective learning. The results from the triangulation showed that the multiple methods were complementary and converged on the same outcome. Discussion The study findings could contribute to the development of adequately adapted PA content to distribute knowledge to populations with developmental disabilities.

Towards Optimal NLP Solutions: Analyzing GPT and LLaMA-2 Models Across Model Scale, Dataset Size, and Task Diversity

This study carries out a comprehensive comparison of fine-tuned GPT models (GPT-2, GPT-3, GPT-3.5) and LLaMA-2 models (LLaMA-2 7B, LLaMA-2 13B, LLaMA-2 70B) in text classification, addressing dataset sizes, model scales, and task diversity. Since its inception in 2018, the GPT series has been pivotal in advancing NLP, with each iteration introducing substantial enhancements. Despite its progress, detailed analyses, especially against competitive open-source models like the LLaMA-2 series in text classification, remain scarce. The current study fills this gap by fine-tuning these models across varied datasets, focusing on enhancing task-specific performance in hate speech and offensive language detection, fake news classification, and sentiment analysis. The learning efficacy and efficiency of the GPT and LLaMA-2 models were evaluated, providing a nuanced guide to choosing optimal models for NLP tasks based on architectural benefits and adaptation efficiency with limited data and resources. In particular, even with datasets as small as 1,000 rows per class, the F1 scores for the GPT-3.5 and LLaMA-2 models exceeded 0.9, reaching 0.99 with complete datasets. Additionally, the LLaMA-2 13B and 70B models outperformed GPT-3, demonstrating their superior efficiency and effectiveness in text classification. Both the GPT and LLaMA-2 series showed commendable performance on all three tasks, underscoring their ability to handle a diversity of tasks. Based on the size, performance, and resources required for fine-tuning the model, this study identifies LLaMA-2 13B as the most optimal model for NLP tasks.

Pengaruh Penggunaan Gadget terhadap Bahasa yang Digunakan oleh Anak Usia 6-10 Tahun dalam Berkomunikasi

In the increasingly advanced digital era, children are increasingly exposed to gadgets such as smartphones, tablets and computers which provide easy access to various online content. Almost all levels of society have gadgets. Excessive use of gadgets or without adequate supervision can have a negative impact on children's language development and use of Indonesian that is not appropriate for their age. Parents give gadgets to their children for several reasons, such as calming the child when they are fussy, making it easier for the child to sleep or eat, and giving parents free time so they can do household work or relax. The aim of this research is to analyze the influence of technological developments on the use of Indonesian by children in elementary schools in communicating and to identify forms of use of Indonesian that are not appropriate for the age of children in elementary schools due to technological developments. The research method used is analytical descriptive, namely research that attempts to describe phenomena that occur realistically, real and contemporary, because this research consists of making systematic, factual and precise descriptions, drawings or paintings regarding the facts, characteristics and relationships between the phenomena being studied. . The research results show that uncontrolled use of gadgets and lack of supervision can contribute to children using inappropriate language in their communication. Children who spend too much time on gadgets tend to be exposed to content that is inappropriate or not appropriate for their age. The influence of a less controlled environment can also influence the use of inappropriate language. It is important for parents and the environment around them to supervise children's use of gadgets by providing appropriate restrictions and managing usage times and also providing a clear understanding of the appropriate and ethical use of language in communication. Parents and the surrounding environment can act as positive examples in the use of appropriate language and provide continuous guidance regarding the importance of using appropriate language in communication.

Upaya Membentuk Karakter Anak Sekami di Lingkungan Waitiu Paroki Santo Alfonsus Maria De Liguori Melalui Kegiatan Keagamaan

The activities of Dedication to the Society (PkM) with the aim of educating the children of Sekami in the Waitiu Parish of St. Alfonsus Maria de Liguori Lewotala are important activities for shaping the character of children of the Sekami. This is done because today many Sekami children are trapped in life behaviors that deviate from Christian teachings like stealing, making dirty words, disrespecting parents, teachers and peers, not engaging in spiritual activities and so on. The activity aims to cultivate and shape the character of the Sekami children in the Waitiu Parish of St. Alfonsus Maria de Liguori Lewotala through religious education activities. The method in this devotional activity is in the form of cultivation and education to the children of Sekami through presence, involvement and also sharing of faith. This dedication takes place every Saturday afternoon in May 2024. The result of this activity is the children of the Waitiu Parish of St. Alfonsus Maria de Liguori Lewotala.becoming increasingly aware and growing in himself the Christian character of life by engaging in various activities programmed by animators-animatrists in order to make himself a superior human being later on who is firmly attached to Christian teachings.

Tone Tracker

Abstract: Tone Tracker's integration of BERT and LSTM technologies allows it to predict and flag offensive language in both Tamil and English social media comments. BERT, a transformer-based model, enables the tool to understand the semantic context of text in multiple languages, ensuring accurate detection of inappropriate content regardless of language. The incorporation of LSTM further enhances this capability by capturing nuanced contextual information, refining the tool's proficiency in content moderation for both Tamil and English content. With its user-friendly features, Tone Tracker becomes accessible to a diverse user base, empowering them to swiftly remove offensive content in both languages and contribute to fostering a secure digital environment. This groundbreaking innovation not only boosts content moderation efficiency but also ensures scalability across various digital platforms, making it adaptable to the linguistic diversity of online communities. Ultimately, Tone Tracker, powered by LSTM and BERT, plays a pivotal role in cultivating positive online spaces where users can engage confidently and respectfully in both Tamil and English.

A comprehensive review on Arabic offensive language and hate speech detection on social media: methods, challenges and solutions

A comprehensive review on Arabic offensive language and hate speech detection on social media: methods, challenges and solutions