Journal articles: 'Langage abusif'

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Nomura, Tatsuya, Takayuki Kanda, Hiroyoshi Kidokoro, Yoshitaka Suehiro, and Sachie Yamada. "Why do children abuse robots?" Interaction Studies 17, no. 3 (December 31, 2016): 347–69. http://dx.doi.org/10.1075/is.17.3.02nom.

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Abstract We found that children sometimes abused a social robot placed in a shopping mall hallway. They verbally abused the robot, repeatedly obstructed its path, and sometimes even kicked and punched the robot. To investigate the reasons for the abuse, we conducted a field study in which we interviewed visiting children who exhibited serious abusive behaviors, including physical contact. We analyzed interview contents to determine whether the children perceived the robot as human-like, why they abused it, and whether they thought that the robot would suffer from their abusive behavior. We obtained valid interviews from 23 children (age range, 5–9 years old) over 13 days of observations. We found that 1) the majority of the children engaged in abuse because they were curious about the robot’s reactions or enjoyed abusing it and considered it human-like, and 2) about half of them believed the robot was capable of perceiving their abusive behaviors.

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Al Thobaiti, Fatmah. "Portraying the Male Abuser in Contemporary Women’s Fiction." ELOPE: English Language Overseas Perspectives and Enquiries 19, no. 2 (December 31, 2022): 197–210. http://dx.doi.org/10.4312/elope.19.2.197-210.

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Newspaper headlines show that awareness of intimate partner violence is a complicated issue that needs further examination. Works of fiction narrated by women trapped in abusive relationships are useful sites for the exploration of what intimate partner violence usually includes, and the identification of subtle behaviours that can be defined as violent and abusive but usually go unnoticed. This article submits two contemporary works of fiction, First Love and the Fifty Shades series, for a study of the covert mechanisms of emotional abuse. To understand such mechanisms, the article engages with feminist as well as postfeminist contemporary thinking on intimate partner violence. The analysis shifts the focus back to the male abuser by carefully depicting how he uses under-recognized, gendered forms of power to abuse his partner. The aim is to elucidate the capacity of first-person narratives to allow access to the abused woman’s mind, while simultaneously provoking questions about the abusers’ behaviours, making them a more powerful tool for understanding intimate partner violence than a newspaper report.

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Grant, Kellie, and Fred Gravestock. "Speech and language impairment: A neglected issue for abused and neglected children." Children Australia 28, no. 4 (2003): 4–11. http://dx.doi.org/10.1017/s1035077200005757.

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Speech and language impairment in the context of children who have experienced abuse and neglect is not well documented, with only a handful of controlled studies and research reviews on the relationships between maltreatment and children's communication development. More prevalent in the literature are reports on the deleterious effects of child abuse and neglect on overall childhood development, purporting effects on cognitive, affective, social and neurological development. However, the growth of communication skills is integral to a child's early development and is closely related to cognitive and social-emotional development. The experience of abuse and/or neglect has profound implications for a child's developing communication, supporting the inclusion of speech pathology in approaches to working with children and families in abusive and neglectful environments. This article reviews the literature on the relationship between communication development and childhood abuse and neglect. Data collected during the course of speech pathology intervention for abused and neglected children attending the Abused Child Trust's (Queensland) counselling service is also presented in order to highlight the need for the inclusion of this therapeutic modality in managing the developmental needs of this population of children.

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Naseem, Usman, Shah Khalid Khan, Farasat, Madiha, and Farasat Ali. "Abusive Language Detection: A Comprehensive Review." Indian Journal of Science and Technology 12, no. 45 (December 10, 2019): 01–13. http://dx.doi.org/10.17485/ijst/2019/v12i45/146538.

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Al-Rawi, Ahmed, Betty B. B. Ackah, and Wendy H. K. Chun. "The Intersectionality of Twitter Responses to Black Canadian Politicians." Social Media + Society 9, no. 1 (January 2023): 205630512311572. http://dx.doi.org/10.1177/20563051231157290.

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Research has shown that Black politicians in the Global North contend with higher instances of abusive language on social media platforms. The study investigates how public interactions engage with the intersectional positionalities of nine Black Canadian politicians. We collected all the replies to tweets posted by the politicians from 2006 to 2021. Results from the manual analysis showed that 56% of the tweets had a neutral tone, meaning that even if they contained abusive language, they did not directly address the politician. They were also not complimentary. There were more negative tweets than positive ones; 23% versus 21%. The themes of the tweets with negative tones centered on opposition to the politicians’ discussion of racial inequalities or racism, or their party affiliations, especially affiliation to the Liberal Party or relationship with Prime Minister Trudeau. The manual analysis showed women politicians received higher rates of abuses, while in the sentiment analysis stage that covered the entire data set, men were more trolled with 66.6% of words directed at them being negative, compared to 55.7% for the women.

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Bayisa, Gemechu, Tesfaye Dagnew, and Tesfamariam Gebremeskel. "An Analysis of The Presentation of Human and Democratic Rights Abuses in AMELMAL’S YÄLTÄKOÄCHE GUZO (1974- 2018)." Ethiopian Renaissance Journal of Social Sciences and Humanities 10, no. 1 (August 1, 2023): 173–84. http://dx.doi.org/10.4314/erjssh.v10i1.10.

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The main objective of the study is to examine the depiction of democratic and human rights abuses in Amelmal’s, Yältäkoäche Guzo (Unfinished Journey). One of the considerable importances of literature all over the world is its use of sympathetic and attractive literary language to expose basic human right abuses since human rights are also part of human life. Therefore, analyzing the roles of literatures that are written in Amharic language in portraying basic human rights violations is paramount. However, literatures written in Amharic language are not well studied from human rights violation perspectives. This article, therefore, explores how the violations of basic human rights are depicted in the selected novel. Because there was no computable data used, the study considered qualitative research method. Regardless of the system by which a country is ruled, human rights are inviolable because they are innate. The analysis of the novel, however, depicts that the inviolable human and democratic rights of human beings have been violated. The finding of the study showed that the depiction of human and democratic rights violations was explicit. It showed that people’s rights to life, rights to equality, rights to liberty, and rights to election were abused during the two political regimes.

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Salem, Mahmoud, Anatoly Liberman, Thomas Hylland Eriksen, Saksith Saiyasombut, Lee Bollinger, Adewale Maja-Pearce, and Nazifullah Salarzai. "The Big Question: Abusing Language." World Policy Journal 29, no. 1 (2012): 3–6. http://dx.doi.org/10.1177/0740277512443790.

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Gong, Hongyu, Alberto Valido, Katherine M. Ingram, Giulia Fanti, Suma Bhat, and Dorothy L. Espelage. "Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attention." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 14804–12. http://dx.doi.org/10.1609/aaai.v35i17.17738.

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Abusive language is a massive problem in online social platforms. Existing abusive language detection techniques are particularly ill-suited to comments containing heterogeneous abusive language patterns, i.e., both abusive and non-abusive parts. This is due in part to the lack of datasets that explicitly annotate heterogeneity in abusive language. We tackle this challenge by providing an annotated dataset of abusive language in over 11,000 comments from YouTube. We account for heterogeneity in this dataset by separately annotating both the comment as a whole and the individual sentences that comprise each comment. We then propose an algorithm that uses a supervised attention mechanism to detect and categorize abusive content using multi-task learning. We empirically demonstrate the challenges of using traditional techniques on heterogeneous content and the comparative gains in performance of the proposed approach over state-of-the-art methods.

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Haq, Nauman Ul, Mohib Ullah, Rafiullah Khan, Arshad Ahmad, Ahmad Almogren, Bashir Hayat, and Bushra Shafi. "USAD: An Intelligent System for Slang and Abusive Text Detection in PERSO-Arabic-Scripted Urdu." Complexity 2020 (November 30, 2020): 1–7. http://dx.doi.org/10.1155/2020/6684995.

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The use of slang, abusive, and offensive language has become common practice on social media. Even though social media companies have censorship polices for slang, abusive, vulgar, and offensive language, due to limited resources and research in the automatic detection of abusive language mechanisms other than English, this condemnable act is still practiced. This study proposes USAD (Urdu Slang and Abusive words Detection), a lexicon-based intelligent framework to detect abusive and slang words in Perso-Arabic-scripted Urdu Tweets. Furthermore, due to the nonavailability of the standard dataset, we also design and annotate a dataset of abusive, offensive, and slang word Perso-Arabic-scripted Urdu as our second significant contribution for future research. The results show that our proposed USAD model can identify 72.6% correctly as abusive or nonabusive Tweet. Additionally, we have also identified some key factors that can help the researchers improve their abusive language detection models.

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Khalid, Aemen, Taimoor Ul Hassan, and Ghulam Shabir. "Cultivation Effects of Social Media on Cognitive, Social and Moral Skills of Adolescents in Pakistan." Journal of Business and Social Review in Emerging Economies 6, no. 2 (May 22, 2020): 419–36. http://dx.doi.org/10.26710/jbsee.v6i2.1146.

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This research work aims to find out social, cognitive and moral effects of facebook on adolescents in Pakistan, because about 65% of the country comprises on adolescents. The researcher applied quantitative research methods, while survey was conducted for the collection of data. Structured questionnaires were used for collection of data. Data was collected through purposive sampling method. Data was analyzed by Statistical Package for Social Sciences. The findings revealed that social media, particularly Facebook has cognitive, social and moral effects, which improve their skills to in-touch with relatives, peers and friends to improve social circle, cognitive effects to enhance creativity and mental level during the studies and moral effects where, they bear to respond the slang and abusive language as well as abusive comments on other posts related to their political and religious beliefs. This study also tests the cultivation theory with regard to social media and generated user categories according to their usage of facebook for future researchers. The results of the study justified the objectives and hypotheses of the study, where it has been recommended to parents, teachers and government to regulate the social media in the country to overcome the abuses.

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Schottenius Cullhed, Sigrid. "Persefone, missbrukare eller missbrukad?" Tidskrift för litteraturvetenskap 47, no. 1 (January 1, 2017): 21–33. http://dx.doi.org/10.54797/tfl.v47i1.8440.

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Persephone, Abuser or Abused This article addresses the mystifying reinterpretations of sexual violence that we often encounter in the Nachleben of ancient mythology. In the Homeric Hymn to Demeter, Hades’ abduction of his niece Persephone is described with language reminiscent of sex slavery in the Iliad. The maiden continued to be represented as a victim of male and divine violence in Ancient and Early Modern retellings of the myth, but in Romantic and Decadent portrayals her innocence faded. The poppy, which is associated with Demeter in Ovid’s Fasti, now came to dominate representations of her. In Algernon Charles Swinburne’s ”The Garden of Proserpine” (1866) and other creative receptions from this period, she is depicted as a queen of opium and a femme fatale. This process transformed the rape of Persephone. The aggression of Hades and Zeus is blurred as Persephone becomes not abused but an abuser, both victim and perpetrator. The change can be related to actual drug use among women of this period. The neutralization of Hades continued and increased during the twentieth century, as the blame was even shifted to Demeter. In psychoanalysis as well as in contemporary poetry, music and novels, Persephone prefers following Hades to the underworld over staying under the protection of her smothering mother. The article connects this retelling of the ancient myth to another modern myth: a daughter must allegedly break the bonds with her mother in order to become an independent individual. Empirical studies on daughter-mother relationships, however, suggest that this view is mistaken. It is more common that young women feel that a good relationship to their mothers improves their chances of achieving independence.

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Jinwen Zeng, David Marlow, and Calvin Odhiambo. "Abusive Language in Chinese and English." Cross-Cultural Studies 28, no. ll (September 2012): 141–61. http://dx.doi.org/10.21049/ccs.2012.28..141.

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Chandu, Burre, Kaza Phani Rohitha, Nampally Nihal, and K. Hima Bindu. "Abusive language detection using customised BERT." International Journal of Swarm Intelligence 7, no. 1 (2022): 25. http://dx.doi.org/10.1504/ijsi.2022.121094.

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Bindu, K. Hima, Nampally Nihal, Burre Chandu, and Kaza Phani Rohitha. "Abusive language detection using customised BERT." International Journal of Swarm Intelligence 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijsi.2021.10041159.

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Long, Kevin Jay. "Abused statistical language in journal articles." American Journal of Medicine 83, no. 3 (September 1987): 607–8. http://dx.doi.org/10.1016/0002-9343(87)90793-5.

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Rathore, Anurag, Anmol Kumar, Aman Negi, and Nidhi Chandra. "Abusive Language Detection in Speech Dataset." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 436–39. http://dx.doi.org/10.22214/ijraset.2023.51463.

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Abstract: In the topic, speech recognition there is a rapid increasing in it into area of engineering technology. Speech recognition delivers many different kinds of pros and it's been uses in a multiple field. Having a different type of language placed a restriction of talking between people. From this project we will going to create and develop to supports different systems that allows persons in that place or situation to change of data through interacting with end device users by voice or speech, after developing this project we will destroy the barriers of communication. This project takes that into consideration and makes an attempt to guarantee that it can identify speech and transform audio input into text. The speech is converted into text format. To overcome the offensive content in real-time every social media platform should implement an effectual hate speech detection system. There are many ways from that we can classify hate speech such as Machine Learning, Rule Based, Deep Learning Based and Hybrid

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Dhankar, Simran, Utkarsh Jain, and Vansh Bansal. "Automated Detection and Symbolic Replacement of Abusive Language using Deep Learning in Online Platforms." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (December 31, 2023): 1439–44. http://dx.doi.org/10.22214/ijraset.2023.57629.

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Abstract: This research addresses the pressing challenge of curbing abusive language in online platforms through the implementation of advanced deep learning techniques. Focused on Natural Language Processing (NLP), this study aims to develop a robust automated censorship system capable of swiftly detecting and mitigating abusive content. By leveraging the prowess of deep learning algorithms, particularly in neural network architectures, the proposed system aims to proactively identify and censor abusive language across various online platforms. Key components involve training models to comprehend contextual nuances, enabling accurate recognition of abusive language patterns. Through this approach, the research aims to significantly contribute to online moderation mechanisms, ensuring a safer and more respectful online environment. The integration of deep learning methodologies within automated censorship systems represents a pivotal step towards mitigating the spread of abusive language, thereby fostering healthier and more inclusive online communities.

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Bartneck, Christoph, and Jun Hu. "Exploring the abuse of robots." Interaction Studies 9, no. 3 (December 5, 2008): 415–33. http://dx.doi.org/10.1075/is.9.3.04bar.

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Robots have been introduced into our society, but their social role is still unclear. A critical issue is whether the robot’s exhibition of intelligent behaviour leads to the users’ perception of the robot as being a social actor, similar to the way in which people treat computers and media as social actors. The first experiment mimicked Stanley Milgram’s obedience experiment, but on a robot. The participants were asked to administer electric shocks to a robot, and the results show that people have fewer concerns about abusing robots than about abusing other people. We refined the methodology for the second experiment by intensifying the social dilemma of the users. The participants were asked to kill the robot. In this experiment, the intelligence of the robot and the gender of the participants were the independent variables, and the users’ destructive behaviour towards the robot the dependent variable. Several practical and methodological problems compromised the acquired data, but we can conclude that the robot’s intelligence had a significant influence on the users’ destructive behaviour. We discuss the encountered problems and suggest improvements. We also speculate on whether the users’ perception of the robot as being “sort of alive” may have influenced the participants’ abusive behaviour.

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Song, Rui, Fausto Giunchiglia, Yingji Li, Lida Shi, and Hao Xu. "Measuring and mitigating language model biases in abusive language detection." Information Processing & Management 60, no. 3 (May 2023): 103277. http://dx.doi.org/10.1016/j.ipm.2023.103277.

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Alderton, Kirsty, and Carol A. Ireland. "Child Sexual Exploitation: Definition and the importance of language." Abuse: An International Impact Journal 1, no. 1 (April 14, 2020): 49–58. http://dx.doi.org/10.37576/abuse.2020.004.

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Halper, Thomas. "Orwellian Opinions: The Language of Power and the Power of Language." British Journal of American Legal Studies 11, no. 1 (June 1, 2021): 153–71. http://dx.doi.org/10.2478/bjals-2022-0003.

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Abstract In 1984 and other writings, George Orwell explored the language of power and the power of language. As illustrations of the abuses he identified, this essay analyzes a pair of famous constitutional opinions, Justice Brown's Plessy v. Ferguson and Justice Douglas’ Griswold v. Connecticut.

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Culp, Rex E., Ruth V. Watkins, Harriet Lawrence, Dana Letts, Donna J. Kelly, and Mabel L. Rice. "Maltreated children's language and speech development: abused, neglected, and abused and neglected." First Language 11, no. 33 (October 1991): 377–89. http://dx.doi.org/10.1177/014272379101103305.

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Andersen, Torbjørn Herlof. "Speaking About the Unspeakable: Sexually Abused Men Striving Toward Language." American Journal of Men's Health 2, no. 1 (November 7, 2007): 25–36. http://dx.doi.org/10.1177/1557988307308107.

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Traditionally, sexual abuse of males has not been an issue of priority among politicians or researchers. When addressed, focus is often on context or harmful effects of the abuse. This article is based on the idea of reality as socially constructed, examining possible ways for sexually abused males to come to terms with their experiences. The emphasis is on accessible discursive resources on “the abused male” and how cultural stereotypes of manliness influence and limit individual and societal constructions. An important key to reconstruction of abuse history and selfhood lies in acceptance of the idea of men as suppressed. Sexually abused males tend to feel marginalized and different. However, when given the opportunity, they offer alternative discourses of manliness with the potential for bringing sexually abused males out of the shadows, assisting them in better understanding, dealing with, and explaining their experiences to themselves and others. This article brings out the importance of a gender-sensitive approach to working politically as well as directly with men who have been sexually abused. The horizon of understanding in professional social work needs to include attention to stereotypical constructions of manliness that reject men's experiences of being “victims.”

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Allen, Rhianon, and Gail A. Wasserman. "Origins of language delay in abused infants." Child Abuse & Neglect 9, no. 3 (January 1985): 335–40. http://dx.doi.org/10.1016/0145-2134(85)90029-8.

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Sylvestre, Audette, Ève-Line Bussières, and Caroline Bouchard. "Language Problems Among Abused and Neglected Children." Child Maltreatment 21, no. 1 (November 30, 2015): 47–58. http://dx.doi.org/10.1177/1077559515616703.

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Amjad, Maaz, Noman Ashraf, Grigori Sidorov, Alisa Zhila, Liliana Chanona-Hernandez, and Alexander Gelbukh. "Automatic Abusive Language Detection in Urdu Tweets." Acta Polytechnica Hungarica 19, no. 10 (2022): 143–63. http://dx.doi.org/10.12700/aph.19.10.2022.10.9.

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Künnapas, Kaido. "Legal Engineering of the Anti-Abuse Rule in ATAD: Architecture of the Regression Tree Model." TalTech Journal of European Studies 11, no. 2 (September 1, 2021): 65–82. http://dx.doi.org/10.2478/bjes-2021-0015.

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Abstract Every taxable arrangement is subject to an anti-abuse test. Abusive arrangements are treated as not valid for tax purposes, which is similar to the treatment of artificial arrangements in civil law. The European Union has introduced in its Anti-Tax Avoidance Directive a general anti-abuse test which must be transposed into the domestic laws of Member States. Such a test has its inner structure, consisting of an elimination and requalification stage, while the elimination stage entails genuineness and a tax benefit test. The general anti-abuse test has a great potential (or scalability when speaking in the language of start-ups) of being automated and integrated into different legal application processes (such as taxpayer self-assessment systems, transactions certified by public notary or merger and acquisition deals) to discover debt push down abuses or other arrangement structures which may have abusive content. While the best method for create a reliable algorithm is a decision tree type model, the inner ambiguity of the general anti-abuse test prevents using the full benefits of automation of tax laws. The purpose of this article is to design a decision tree type model for the test and address the main challenges of such a model, both from the perspective of the clarity of concepts and the quality of input information such an engine would use.

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Alrashidi, Bedour, Amani Jamal, and Ali Alkhathlan. "Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models." Applied Sciences 13, no. 10 (May 9, 2023): 5825. http://dx.doi.org/10.3390/app13105825.

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Different social media platforms have become increasingly popular in the Arab world in recent years. The increasing use of social media, however, has also led to the emergence of a new challenge in the form of abusive content, including hate speech, offensive language, and abusive language. Existing research work focuses on automatic abusive content detection as a binary classification problem. In addition, the existing research work on the automatic detection task surrounding abusive Arabic content fails to tackle the dialect-specific phenomenon. Consequently, this has led to two important issues in the automatic abusive Arabic content detection task. In this study, we used a multi-aspect annotation schema to tackle the automatic abusive content detection problem in Arabic countries, based on the multi-class classification task and the dialectal Arabic (DA)-specific phenomenon. More precisely, the multi-aspect annotation schema includes five attributes: directness, hostility, target, group, and annotator. We specifically developed a framework to automatically detecting abusive content on Twitter using natural language processing (NLP) techniques. The developed framework used different models of machine learning (ML), deep learning (DL), and pretrained Arabic language models (LMs) using the multi-aspect annotation dataset. In addition, to investigate the impact of the other approaches, such as multi-task learning (MTL), we developed four MTL models built on top of a pretrained DA language model (called MARBERT) and trained on the multi-aspect annotation dataset. Our MTL models and pretrained Arabic LMs enhanced the performance compared to the existing DL model mentioned in the literature.

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Alrashidi, Bedour, Amani Jamal, Imtiaz Khan, and Ali Alkhathlan. "A review on abusive content automatic detection: approaches, challenges and opportunities." PeerJ Computer Science 8 (November 9, 2022): e1142. http://dx.doi.org/10.7717/peerj-cs.1142.

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The increasing use of social media has led to the emergence of a new challenge in the form of abusive content. There are many forms of abusive content such as hate speech, cyberbullying, offensive language, and abusive language. This article will present a review of abusive content automatic detection approaches. Specifically, we are focusing on the recent contributions that were using natural language processing (NLP) technologies to detect the abusive content in social media. Accordingly, we adopt PRISMA flow chart for selecting the related papers and filtering process with some of inclusion and exclusion criteria. Therefore, we select 25 papers for meta-analysis and another 87 papers were cited in this article during the span of 2017–2021. In addition, we searched for the available datasets that are related to abusive content categories in three repositories and we highlighted some points related to the obtained results. Moreover, after a comprehensive review this article propose a new taxonomy of abusive content automatic detection by covering five different aspects and tasks. The proposed taxonomy gives insights and a holistic view of the automatic detection process. Finally, this article discusses and highlights the challenges and opportunities for the abusive content automatic detection problem.

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Jadmiko, Rahmad Setyo, and Rian Damariswara. "Analisis Bahasa Kasar yang Ditirukan Anak Remaja dari Media Sosial Tiktok di Desa Mojoarum Kecamatan Gondang Kabupaten Tulungagung." Stilistika: Jurnal Pendidikan Bahasa dan Sastra 15, no. 2 (July 31, 2022): 227. http://dx.doi.org/10.30651/st.v15i2.13162.

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Analysis of Abusive Language Imitated By Adolescents From Tiktok Social Media in Mojoarum Village, Gondang District, Tulungagung RegencyABSTRAKSelama masa pandemi, eksistensi aplikasi Tiktok semakin kuat. Aplikasi Tiktok mempermudah para konten kreator pemula untuk membuat video singkat yang menarik perhatian. Banyak isi dari video tersebut yang berisi ujaran-ujaran tidak pantas, salah satunya bahasa kasar. Anak remaja yang melihat isi konten tersebut, tentu saja belum bisa mempertimbangkan apakah ujaran tersebut layak jika diucapkan di khalayak umum. Banyak dari mereka menirukan bahasa kasar dari konten aplikasi Tiktok. Fokus penelitian ini adalah, mengetahui jenis-jenis bahasa kasar apa saja yang mereka tirukan dari konten aplikasi Tiktok. Jenis penelitian ini adalah kualitatif, dengan subjek sekumpulan anak remaja di Desa Mojoarum, Kecamatan Gondang, Kabupaten Tulungagung, Dari hasil penelitian, banyak ditemukan jenis-jenis bahasa kasar yang mereka ucapkan. Jenis bahasa kasar tersebut dapat digolongkan menjadi lima jenis yaitu kondisi, nama hewan, makhluk astral, sebuah objek, dan bagian organ tubuh manusia. Mereka mengaku bahwa, dari konten Tiktok mereka menirukan dan ikut memviralkan ujaran-ujaran bahasa kasar tersebut. Anak-anak remaja tersebut mengaku wajar dan lumrah jika mengucapkan bahasa kasar tersebut.Kata kunci: aplikasi Tiktok, anak remaja, bahasa kasarABSTRACTDuring the pandemic, the existence of the Tiktok application is getting stronger. The Tiktok app makes it easy for beginner content creators to create short, eye-catching videos. Many of the contents of the video contain inappropriate utterances, one of which is abusive language. Teenagers who see the contents of the content, certainly, cannot consider whether the utterance is appropriate if it is uttered in the general public. Many of them imitate the abusive language of the content in the Tiktok application. The focus of this research is to find out what types of abusive language which they imitate from the content of the Tiktok application. The type of this research is qualitative, with the subject of a teenager group in Mojoarum Village, Gondang District, Tulungagung Regency. Based on the the study results, many types of abusive language were found. This type of abusive language can be classified into five types, namely conditions, names of animals, astral beings, an object, and parts of the human body. They admitted that, from their Tiktok content, they imitated and took part in the viral utterances of the abusive language. The teenagers admitted that it was natural and normal for them to say abusive language.Keyword: Tiktok app, teenager, abusive language

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Haritha, T., and M. Divya. "Unraveling the Tyranny: Exploring the Abusive Use of Power in Animal Farms." Shanlax International Journal of Arts, Science and Humanities 11, S2-March (March 30, 2024): 79–81. http://dx.doi.org/10.34293/sijash.v11is2-march.7517.

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It is common for strong leaders to take advantage of common people, frequently by abusing their position of control and keeping the public silent. Similar to what transpired in the Soviet Union, George Orwell illustrates in Animal Farm how education and knowledge may be used as instruments of ultimate authority, resulting in the oppression and misery of the ignorant. The purposeful employment of ambiguous language and intimidation techniques allowed leaders to control people for their personal benefit, creating laws and disseminating false information to further their agendas. Power might have been used for the good of society as a whole, but it was instead abused and manipulated. To explore these elements, this research takes a historical approach. The persuasive examination of the misuse of power provided by this allegorical novella is impressive. Examining the people, occasions, and ideas that highlight the tyranny and oppression that are present everywhere, this investigation digs into the complex layers of Animal Farm.

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Eronen, Juuso, Michal Ptaszynski, Fumito Masui, Masaki Arata, Gniewosz Leliwa, and Michal Wroczynski. "Transfer language selection for zero-shot cross-lingual abusive language detection." Information Processing & Management 59, no. 4 (July 2022): 102981. http://dx.doi.org/10.1016/j.ipm.2022.102981.

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Tartory, Raeda, Ogareet Khoury, Anoud Tayyeb, Areen Al-Qudah, and Nuwar Al-Akash. "Critical Discourse Analysis of Verbal Violence in William Shakespeare's Merchant of Venice." Theory and Practice in Language Studies 12, no. 9 (September 1, 2022): 1900–1910. http://dx.doi.org/10.17507/tpls.1209.24.

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The combination of Critical Discourse Analysis and verbal violence is an entirely new field that needs to be widely explored and this study takes an in-depth dive into this using the literature, ‘Merchant of Venice' by one of the canons of literature, William Shakespeare. In doing this, this study identifies verbally abusive speeches from the text, categorizes and analyzes them to reveal the common patterns of violence in the speeches of abusers. This analysis aims to reveal the structure abusers use and the effect that verbal abuses have on their victims. Following the tradition of Critical Discourse, the study investigates, in an exegetical pattern, how violence in the form of verbal expression can cause harm. This is situated within the context William Shakespeare’s, ‘Merchant of Venice'—these investigations are done using the social and cultural realities/contexts within which Shakespeare wrote his story/narration. Seeing “Discourse” as a social critical theory that emphasizes the place of language in the making of ideas within society, Critical Discourse Analysis [especially as used within the current study] investigates language within Shakespeare’s ‘Merchant of Venice’ and insists that language plays a viable role in society’s communication patterns, and as such, should be taken seriously in the critique of verbal violence within Shakespeare’s corpus—as this critique is being appropriated within current times.

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Vidgen, Bertie, and Leon Derczynski. "Directions in abusive language training data, a systematic review: Garbage in, garbage out." PLOS ONE 15, no. 12 (December 28, 2020): e0243300. http://dx.doi.org/10.1371/journal.pone.0243300.

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Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets.

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Ashford, Robert David, Austin Brown, and Brenda Curtis. "Expanding language choices to reduce stigma." Health Education 119, no. 1 (January 7, 2019): 51–62. http://dx.doi.org/10.1108/he-03-2018-0017.

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Purpose Public perception has been found to be influenced by the words used to describe those with behavioral health disorders, such that using terms like “substance abuser” can lead to higher levels of stigma. The purpose of this paper is to identify additional stigmatizing and empowering terms that are commonly used by different stakeholders. Design/methodology/approach Using digital Delphi groups, the paper identifies positive and negative terms related to substance use disorder (SUD) from three distinct stakeholder groups: individuals in recovery, impacted family members and loved ones, and professionals in the treatment field. Findings Participants identified 60 different terms that are considered stigmatizing or positive. Previously identified stigmatizing terms (abuser, addict) were present for all stakeholder groups, as was the positive term person with a SUD. Additional stigmatizing terms for all groups included junkie and alcoholic. Additional positive terms for all groups included long-term recovery. Social implications The results suggest that the continued use of terms like addict, alcoholic, abuser and junkie can induce stigma in multiple stakeholders. The use of more positive terms such as person with a SUD or person in recovery is suggested to reduce stigma. Originality/value The use of digital Delphi groups to solicit feedback from multiple stakeholder groups from the substance use community is innovative and allows for the comparison of linguistics among and between the groups.

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Ashraf, Noman, Arkaitz Zubiaga, and Alexander Gelbukh. "Abusive language detection in youtube comments leveraging replies as conversational context." PeerJ Computer Science 7 (October 8, 2021): e742. http://dx.doi.org/10.7717/peerj-cs.742.

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Nowadays, social media experience an increase in hostility, which leads to many people suffering from online abusive behavior and harassment. We introduce a new publicly available annotated dataset for abusive language detection in short texts. The dataset includes comments from YouTube, along with contextual information: replies, video, video title, and the original description. The comments in the dataset are labeled as abusive or not and are classified by topic: politics, religion, and other. In particular, we discuss our refined annotation guidelines for such classification. We report a number of strong baselines on this dataset for the tasks of abusive language detection and topic classification, using a number of classifiers and text representations. We show that taking into account the conversational context, namely, replies, greatly improves the classification results as compared with using only linguistic features of the comments. We also study how the classification accuracy depends on the topic of the comment.

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Shou, Sihan. "Psychology Behind Aggressive Language Used in Honor of Kings." Communications in Humanities Research 7, no. 1 (October 31, 2023): 193–200. http://dx.doi.org/10.54254/2753-7064/7/20230883.

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Multiplayer Online Battle Arena (MOBA) on mobile phones is one of the most popular gaming styles in recent years. One of the representative games is Honor of Kings. People search for entertainment and emotional release in the game. This creates the problem of aggressive language use in Honor of Kings. This paper investigates the phenomenon of aggressive language in Honor of Kings and the psychology and reasons behind it. Analysis of existing literature and research on similar topics were used in this paper to examine the psychology and reasons. Factors like anonymity, competitiveness, and sexism were identified and analyzed in this paper as potential influencers of abusive language use in Honor of Kings. Based on this result, this paper provided several suggestions to help improve the gaming environment, including strengthening the reporting mechanism, strengthening the audit of the abusive language and increasing education and awareness of the use of abusive language.

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Leung, Lai-Ching. "Deconstructing the Myths About Intimate Partner Violence: A Critical Discourse Analysis of News Reporting in Hong Kong." Journal of Interpersonal Violence 34, no. 11 (July 20, 2016): 2227–45. http://dx.doi.org/10.1177/0886260516660298.

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This article depicts the dominant discourses on intimate partner violence (IPV) in newspaper reports and discusses how the myths about IPV are perpetuated in news reporting in Hong Kong. The myths about IPV consist of a set of prevalent assumptions in society that adversely affect the help-seeking behavior of survivors and impede social change. It is sometimes assumed that the victims cause the abuse and are personally responsible for solving the problem. This study reveals how news reporting in Hong Kong perpetuates the myths about IPV by engendering unequal power relations through the language and text used in newspapers. A critical discourse analysis is performed to depict the language used in the text and the embedded meanings in discourses on IPV in two popular local newspapers, Apple Daily and Ming Pao. The findings indicate that the two newspapers tend to use five major discursive frameworks in their reporting on IPV, namely, (a) gender symmetry, (b) stereotyping the abuser, (c) labeling the abused, (d) blaming the victim, and (e) ignoring women’s rights. The study reveals evidence of the systematic stereotyping of IPV abusers and blaming of survivors in newspaper reporting. These powerful discourses may perpetuate the myths about IPV and marginalize IPV survivors in society.

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Song, Rui, Fausto Giunchiglia, Qiang Shen, Nan Li, and Hao Xu. "Improving Abusive Language Detection with online interaction network." Information Processing & Management 59, no. 5 (September 2022): 103009. http://dx.doi.org/10.1016/j.ipm.2022.103009.

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Ashford, Robert D., Austin M. Brown, and Brenda Curtis. "“Abusing Addiction”: Our Language Still Isn’t Good Enough." Alcoholism Treatment Quarterly 37, no. 2 (September 5, 2018): 257–72. http://dx.doi.org/10.1080/07347324.2018.1513777.

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Cooper, M. Townsend, Edgardo Szyld, and Paul M. Darden. "Abusive head trauma in Spanish language medical literature." Child Abuse & Neglect 58 (August 2016): 173–79. http://dx.doi.org/10.1016/j.chiabu.2016.07.001.

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Bridgers, Sue Ellen. "Learning a Language of Nonviolence." English Journal 89, no. 5 (May 1, 2000): 71–73. http://dx.doi.org/10.58680/ej2000602.

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Describes the author’s experience writing a novel which she believed was a love story, but came to realize was a story about domestic violence. Argues for a strong role for young adult literature in examining the realities of abusive, confining relationships. Notes that young people have been helped in dealing with their own dilemmas by seeing themselves in such stories.

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Fox, Lynn, Steven H. Long, and Aimée Langlois. "Patterns of Language Comprehension Deficit in Abused and Neglected Children." Journal of Speech and Hearing Disorders 53, no. 3 (August 1988): 239–44. http://dx.doi.org/10.1044/jshd.5303.239.

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The similarity between factors associated with child abuse/neglect and those associated with language disability suggests that maltreated children are a population at risk for language problems. This study investigated the performance of three groups of abused/neglected children and a matched group of nonmaltreated subjects on three tests of language comprehension. Results revealed significant differences among groups for all measures. Severely neglected children obtained the lowest scores on all tests; the abused children consistently obtained lower scores than the controls; and generally neglected children showed the smallest difference in performance from the control group. These findings suggest a model for understanding relationships between abuse/neglect and language disability

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Ikeke, Mark Omorovie. "The (Mis) Use of Language in Nigerian Political Campaigns (NPC): Moral Philosophical Perspectives." East African Journal of Arts and Social Sciences 3, no. 1 (January 5, 2021): 1–11. http://dx.doi.org/10.37284/eajass.3.1.260.

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Language is an important instrument of communication in every society. Through it, human beings interact, relate, and forge ahead. Through language, human beings are able to adapt to their environment and make meaning out of life. It can be used for good or for evil. Language can be used properly or it can be abused or misused. This paper examines the use of language in political campaigns in Nigeria. It argues that language is often misused or abused in political campaigns in Nigeria. Language is used to cajole, intimidate, demean and degrade human dignity in Nigeria. Ethically the way language is used in political campaigns in Nigeria is unacceptable. The paper used a critical analytic and hermeneutic method to decipher the issues involved. It finds and concludes that there is a need for politicians to behave morally in their use of language during political campaigns to build a better society.

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Campbell, Isabel. "How emerging trends in historiography expose the Canadian Army’s past discriminatory practices and provide hope for future change." International Journal: Canada's Journal of Global Policy Analysis 76, no. 3 (September 2021): 465–76. http://dx.doi.org/10.1177/00207020211050330.

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This “lessons learned” article examines how emerging trends over time in the historiography of the Canadian Army have challenged and continue to challenge the white Anglophone masculine heterosexual culture which is especially associated with its combat units. This study began as an examination of the intersection between the historiography and the current priorities for sufficient female participation in the Canadian Armed Forces (CAF) which are intended to improve past abusive patriarchal practices and create effective and safe international interventions. Gender and sexual abuses were the initial foci, but the historiography revealed the interconnectedness of widespread discriminations against all “others”—defined here as anyone with a different gender, sexuality, race, language, religion, or culture. The article opens with a brief summary of evolving feminist ideas about security forces in general. It then delves into the historiographical trends which have demonstrated how systemic discriminations have privileged white Anglo men in combat roles while underplaying their contributions and the contributions of “others” in support roles in the Canadian Army over time. The key lesson learned from this work is that gender balance alone is not enough to address the profound cultural issues which plague the Canadian Army.

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Noll, J. G., C. E. Shenk, M. T. Yeh, J. Ji, F. W. Putnam, and P. K. Trickett. "Receptive Language and Educational Attainment for Sexually Abused Females." PEDIATRICS 126, no. 3 (August 9, 2010): e615-e622. http://dx.doi.org/10.1542/peds.2010-0496.

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Wich, Maximilian, Adrian Gorniak, Tobias Eder, Daniel Bartmann, Burak Enes Çakici, and Georg Groh. "Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 1133–44. http://dx.doi.org/10.1609/icwsm.v16i1.19364.

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Because traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not moderate user content to the same degree. One popular platform relevant for the German community is Telegram for which limited research efforts have been made so far. This study aimed to develop a broad framework comprising (i) an abusive language classification model for German Telegram messages and (ii) a classification model for the hatefulness of Telegram channels. For the first part, we use existing abusive language datasets containing posts from other platforms to develop our classification models. For the channel classification model, we develop a method that combines channel-specific content information collected from a topic model with a social graph to predict the hatefulness of channels. Furthermore, we complement these two approaches for hate speech detection with insightful results on the evolution of German speaking communities focused on hateful content on the Telegram platform. We also propose methods for conducting scalable network analyses for social media platforms to the hate speech research community. As an additional output of this study, we provide an annotated abusive language dataset containing 1,149 annotated Telegram messages.

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Ihsan, Fauzi, Iwan Iskandar, Nazruddin Safaat Harahap, and Surya Agustian. "Decision tree algorithm for multi-label hate speech and abusive language detection in Indonesian Twitter." Jurnal Teknologi dan Sistem Komputer 9, no. 4 (August 8, 2021): 199–204. http://dx.doi.org/10.14710/jtsiskom.2021.13907.

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Hate speech and abusive language are easily found in written communications in social media like Twitter. They often cause a dispute between parties, the victims, and the first who write the tweet. However, it is also difficult to distinguish whether a tweet contains hate speech and/or abusive language for those who take sides. This research aims to develop a method to classify the tweets into abusive and/or contain hate speech classes. If hate speech is detected, then the system will measure the hardness level of hatred. The dataset includes 13,126 real tweets data. Word embeddings are used for featuring text input. For the tweets classification, we use a Decision Tree algorithm. Some engineering of features and parameters tuning has improved the classification of the three classes: hate speech class, abusive words, and hate speech level. The lexicon feature in the Decision Tree classification produces the highest accuracy for detecting the three classes rather than engineering special features and textual features. The average accuracy of the three classes increased from 69.77 % to 70.48 % for the training-testing composition of 90:10, and another 69.35 % to 69.54 % for 80:20 respectively.

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Murphy, Katharine. "An Epidemic of Apathy: Abulia and the Language of Pathology in Baroja’s Early Fiction." Hispanic Review 91, no. 3 (June 2023): 387–410. http://dx.doi.org/10.1353/hir.2023.a903835.

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ABSTRACT: The literary exposition of abulia in Pío Baroja’s early novels, especially La lucha por la vida trilogy, illuminates the ways in which diagnostic language from psychopathology was adapted, assimilated, and disseminated through the trajectories of fictional characters who suffer from a loss of volition. This article analyzes cultural narratives about abulia in Baroja’s early fiction, demonstrating that they constitute a resonant pathological metaphor during a period in Spain’s history defined by national introspection and regenerationist debates. By tracing metaphorical explanations for social, political, and economic circumstances conveyed through the literary appropriation of medical terminology, this study explores comparisons between abulia and the gendered and class-based associations of neurasthenia at the turn of the twentieth century. Although the assumed causes of each condition are different, this process of transposition between medicine and metaphor anticipates the contemporary social, cultural, and ideological shaping of concepts such as stress and burnout.

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N, Joshi Padma, Peddada Anvitha, Poddaturu Sushmitha Reddy, Raperthi Tarun Chandra Bhoopathi, and Chevity Nagaraju. "DETECTING ABUSIVE AND INSULTING COMMENTS ON SOCIAL MEDIA." YMER Digital 21, no. 05 (May 6, 2022): 129–35. http://dx.doi.org/10.37896/ymer21.05/16.

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Now a days, Social media has become a very important mode of communication. Billions of people communicate through various social networking sites. People are using these sites as a medium to express their views and opinions. As the faces of the coin, there are both pros and cons by using this medium. One of the major problems is the insulting and the abusive comments that we get on the social media. Hence, the need for good quality automated abusive language classifiers becomes important. Our project “Detecting Abusive and Insulting Comments on Social Media” aims at designing a system for automatically detecting comments on social media as abusive or non-abusive using machine learning algorithms such as Random Forest classifier and Artificial Neural Network techniques (ANN).

Journal articles on the topic 'Langage abusif'

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