Light and Shadow in Hate-Speech Cases: A Forensic Linguistics

Agustina, -; Nurizzati,; Ainim, Siti; Adek, Muhammad; Rahmi, Awliya

doi:10.2991/assehr.k.201109.003

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…Semiotic lanalysis and pragmaticl discourse methodsl are integratedl into thel data interpretationl and descriptionl process. Thel results ofl the analysisl concluded thatl the postsl from threel convicted KOLl (ADP, lJG, BY) lhave fulfilled thel element ofl hate speechl acts throughl illocutionary speechl acts lthat contain elementsl of lhumiliation, defamation, andl incitement ofl violence andl provocation thatl have thel potential lto create conflictsl between lreligions, ethnicities andl groupsl (Agustina., et. al.…”

Section: Resultsmentioning

confidence: 99%

Hate Speech in Indonesia: A Forensic Linguistics

Lubis,

Harahap,

Rahmawati

et al. 2023

EJEEE

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This study intends to examine the scientific article about the phenomenon of hate speech in pandemic era in Indonesia from forensic linguistics view. By using a meta-analysis technique on Google Schoolar, an official website intended as a collection of scientific articles in Indonesia. A total of 150 articles were accessed from Google Scholar. To assure the review's focus, a selection process for inclusion and exclusion criteria was carried out before it began. There are 20 eligible articles to be reviewed. Findings showed that most of the researchers used social media as the source of data, most of the researchers used the theory of speech act to analyse the data.

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Section: Resultsmentioning

confidence: 99%

Hate Speech in Indonesia: A Forensic Linguistics

Lubis,

Harahap,

Rahmawati

et al. 2023

EJEEE

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“…Sejumlah individu dengan motif yang tidak baik menyebarkan kebencian secara daring, menimbulkan dampak negatif dalam ekosistem digital. Sesuai dengan penelitian yang dilakukan oleh Ainim et al (2020). Pengguna internet yang tersebar ini, dengan mudahnya, merajut jaringan kebencian yang dapat menyebar dengan cepat di Twitter (Clarke, 2019).…”

Section: Pembahasanunclassified

Ujaran Kebencian Netizen Indonesia Pada Akun Twitter Es Teh: Tinjauan Linguistik Forensik

Syafruddin,

Thaba,

Ananda

2024

Semant. J. Ilm. Progr. Stud. Pendidik. Bhs. dan Sastra Indones

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This study aims to describe the form of netizens' hate speech on the @esteh_indonesia Twitter account which can be categorized as a violation of the law. This research is included in the qualitative research used to describe netizens' hate speech in @esteh_indonesia Twitter comments using forensic linguistic reviews. The methods and techniques used are the listening method and the note-taking technique. Listening is done by observing the hate speech of netizens on the Twitter account @esteh_indonesia. After that, the data that has been selected purposively is copied and then analyzed by collecting data that has been recorded in one folder, classifying and analyzing the data based on the form of hate speech with the guidelines of the Police circular regarding hate speech on social media. The results of the study prove that there are five forms of netizens' hate speech in the Twitter comments of the @esteh.indonesia account: 1) insults, 2) defamation, 3) blasphemy, 4) provoking, and 5) hoax. The results of this study can increase public awareness about the existence of hate speech on social media platforms, especially Twitter. This can encourage people to be more careful in interacting online.

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“…Criminal cases such as hate speech [99] may involve local nuances [101], and while global dimension may not be impossible [100], investigations should take into account regional differences. For instance, in a typical forensic linguistics investigation [95][96][97] (e.g., cyberbullying [102]), a language corpus plays a vital role.…”

Section: Dfai Datasetsmentioning

confidence: 99%

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

Solanke

Biasiotti²

2022

Künstl Intell

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The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process.

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Light and Shadow in Hate-Speech Cases: A Forensic Linguistics

Cited by 5 publications

References 12 publications

Hate Speech in Indonesia: A Forensic Linguistics

Hate Speech in Indonesia: A Forensic Linguistics

Ujaran Kebencian Netizen Indonesia Pada Akun Twitter Es Teh: Tinjauan Linguistik Forensik

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

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