Identifying False Content and Hate Speech in Sinhala YouTube Videos by Analyzing the Audio
Sri Lanka Institute of Information Technology（斯里兰卡信息技术学院）
W. A. K. M. Wickramaarachchi, Sameeri Sathsara Subasinghe, K. K. Rashani Tharushika Wijerathna, A. Sahashra Udani Athukorala, Lakmini Abeywardhana, A. Karunasena
YouTube faces a global crisis with the dissemination of false information and hate speech. To counter these issues, YouTube has implemented strict rules against uploading content that includes false information or promotes hate speech. While numerous studies have been conducted to reduce offensive English-language content, there's a significant lack of research on Sinhala content. This study aims to address the aforementioned gap by proposing a solution to minimize the spread of violence and misinformation in Sinhala YouTube videos. The approach involves developing a rating system that assesses whether a video contains false information by comparing the title and description with the audio content and evaluating whether the video includes hate speech. The methodology encompasses several steps, including audio extraction using the Pytube library, audio transcription via the fine-tuned Whisper model, hate speech detection employing the distilroberta-base model and a text classification LSTM model, and text summarization through the fine-tuned BART-Large- XSUM model. Notably, the Whisper model achieved a 48.99\% word error rate, while the distilroberta-base model demonstrated an F1 score of 0.856 and a recall value of 0.861 in comparison to the LSTM model, which exhibited signs of overfitting.
Maulana Abul Kalam Azad University of Technology（毛拉纳·阿布尔·卡拉姆·阿扎德技术大学）；Carnegie Mellon University（卡内基梅隆大学）
On June 28, 2020, while presenting a chess podcast on Grandmaster Hikaru Nakamura, Antonio Radić's YouTube handle got blocked because it contained "harmful and dangerous" content. YouTube did not give further specific reason, and the channel got reinstated within 24 hours. However, Radić speculated that given the current political situation, a referral to "black against white", albeit in the context of chess, earned him this temporary ban. In this paper, via a substantial corpus of 681,995 comments, on 8,818 YouTube videos hosted by five highly popular chess-focused YouTube channels, we ask th…
Aston University（阿斯顿大学）；University of Wolverhampton（伍尔弗汉普顿大学）；Birmingham City University（伯明翰城市大学）；University of Münster（明斯特大学）；George Mason University（乔治梅森大学）
Shahjalal University of Science and Technology（沙贾拉尔科技大学）
Social media sites such as YouTube and Facebook have become an integral part of everyone's life and in the last few years, hate speech in the social media comment section has increased rapidly. Detection of hate speech on social media websites faces a variety of challenges including small imbalanced data sets, the findings of an appropriate model and also the choice of feature analysis method. further more, this problem is more severe for the Bengali speaking community due to the lack of gold standard labelled datasets. This paper presents a new dataset of 30,000 user comments tagged by crowd …
University of Information Technology（信息技术大学）；Vietnam National University Ho Chi Minh City（胡志明市越南国家大学）
In recent years, Vietnam witnesses the mass development of social network users on different social platforms such as Facebook, Youtube, Instagram, and Tiktok. On social medias, hate speech has become a critical problem for social network users. To solve this problem, we introduce the ViHSD - a human-annotated dataset for automatically detecting hate speech on the social network. This dataset contains over 30,000 comments, each comment in the dataset has one of three labels: CLEAN, OFFENSIVE, or HATE. Besides, we introduce the data creation process for annotating and evaluating the quality of …
ChildGuard: A Specialized Dataset for Combatting Child-Targeted Hate Speech
Macquarie University（麦考瑞大学）；MBZUAI（穆罕默德·本·拉希德人工智能研究所）；DSEU（德里国家理工学院）；Department of Information and Computer Science, KFUPM（科威特石油大学信息与计算机科学系）；Stanford University（斯坦福大学）
Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning
Center for Precision Psychiatry, Mass General Hospital Department of Psychiatry, Harvard Medical School（精准精神病学中心，麻省总医院精神病科，哈佛医学院）；D-Lab University of California, Berkeley（加州大学伯克利分校D实验室）
Qatar Computing Research Institute, Hamad Bin Khalifa University（哈马德·本·哈利法大学卡塔尔计算研究所）
We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to collect a large number of offensive tweets. We apply the proposed method on Arabic tweets and compare it with English tweets - analysing key cultural differences. We observed a constant usage of these emojis to represent offensiveness throughout different timespans on Twitter. We manually annotate and publicly release the largest Arabic dataset for offensive, fine-grained hate sp…
BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla
Research and Development, Penta Global Limited（研发部，Penta全球有限公司）；Islamic University of Technology（伊斯兰技术大学）；CCDS Lab, Independent University（CCDS实验室，独立大学）
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary in order to comply with the corresponding legislation and maintain a high level of service quality. A robu…
Institute of Management and Research, Jalgaon, Affil- KBC North Maharashtra University（贾尔冈管理与研究学院，隶属于KBC北马哈拉施特拉大学）；School of Computer Sciences, KBC North Maharashtra University（KBC北马哈拉施特拉大学计算机科学学院）；Copenhagen Business School（哥本哈根商学院）；BMCC college of commerce, Pune（BMCC商学院，浦那）
This paper describes the system submitted by our team, KBCNMUJAL, for Task 2 of the shared task Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), at Forum for Information Retrieval Evaluation, December 16-20, 2020, Hyderabad, India. The datasets of two Dravidian languages Viz. Malayalam and Tamil of size 4000 observations, each were shared by the HASOC organizers. These datasets are used to train the machine using different machine learning algorithms, based on classification and regression models. The datasets consist of tweets or YouTube comments with two c…
Aston University（阿斯顿大学）；Folding Space（Folding Space公司）；University of Wolverhampton（伍尔弗汉普顿大学）
In this paper, an approach for hate speech detection against women in Arabic community on social media (e.g. Youtube) is proposed. In the literature, similar works have been presented for other languages such as English. However, to the best of our knowledge, not much work has been conducted in the Arabic language. A new hate speech corpus (Arabic\_fr\_en) is developed using three different annotators. For corpus validation, three different machine learning algorithms are used, including deep Convolutional Neural Network (CNN), long short-term memory (LSTM) network and Bi-directional LSTM (Bi-…
Insight SFI Research Centre for Data Analytics（Insight SFI数据分析研究中心）；National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）；ULTRA Arts and Science College（ULTRA文理学院）；Indian Institute of Information Technology and Management-Kerala（印度信息技术与管理学院喀拉拉分校）；Sri Sivasubramaniya Nadar College of Engineering（斯里西瓦苏布拉马尼亚纳达尔工程学院）；Sri Krishna Adithya College of Arts and Science（斯里克里希纳阿迪蒂亚文理学院）；Hindusthan College of Arts and Science（印度斯坦文理学院）
The increased proliferation of abusive content on social media platforms has a negative impact on online users. The dread, dislike, discomfort, or mistrust of lesbian, gay, transgender or bisexual persons is defined as homophobia/transphobia. Homophobic/transphobic speech is a type of offensive language that may be summarized as hate speech directed toward LGBT+ people, and it has been a growing concern in recent years. Online homophobia/transphobia is a severe societal problem that can make online platforms poisonous and unwelcome to LGBT+ people while also attempting to eliminate equality, d…
Indian Institute of Information Technology Dharwad（印度信息技术学院达尔瓦德分校）
Since personal computers became widely available in the consumer market, the amount of harmful content on the internet has significantly expanded. In simple terms, harmful content is anything online which causes a person distress or harm. It may include hate speech, violent content, threats, non-hope speech, etc. The online content must be positive, uplifting and supportive. Over the past few years, many studies have focused on solving this problem through hate speech detection, but very few focused on identifying hope speech. This paper discusses various machine learning approaches to identif…
The Rohingya refugee crisis is one of the biggest humanitarian crises of modern times with more than 600,000 Rohingyas rendered homeless according to the United Nations High Commissioner for Refugees. While it has received sustained press attention globally, no comprehensive research has been performed on social media pertaining to this large evolving crisis. In this work, we construct a substantial corpus of YouTube video comments (263,482 comments from 113,250 users in 5,153 relevant videos) with an aim to analyze the possible role of AI in helping a marginalized community. Using a novel com…
