The 7th Workshop on Online Abuse and Harms (WOAH)最新文献

Factoring Hate Speech: A New Annotation Framework to Study Hate Speech in Social Media 分解仇恨言论:研究社交媒体中仇恨言论的新注释框架

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 2023-11-07 DOI: 10.18653/v1/2023.woah-1.21

Gal Ron, Effi Levi, Odelia Oshri, Shaul R. Shenhav

引用次数: 0

Benchmarking Offensive and Abusive Language in Dutch Tweets 对荷兰语推特中的攻击性和辱骂性语言进行基准测试

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.woah-1.7

Tommaso Caselli, H. van der Veen

引用次数: 0

HOMO-MEX: A Mexican Spanish Annotated Corpus for LGBT+phobia Detection on Twitter HOMO-MEX:用于Twitter上LGBT+恐惧症检测的墨西哥西班牙语注释语料库

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.woah-1.20

Juan Vásquez, S. Andersen, G. Bel-Enguix, Helena Gómez-Adorno, Sergio-Luis Ojeda-Trueba

引用次数: 0

Respectful or Toxic? Using Zero-Shot Learning with Language Models to Detect Hate Speech 恭敬还是有害?使用零射击学习和语言模型来检测仇恨言论

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.woah-1.6

F. Plaza-Del-Arco, Debora Nozza, Dirk Hovy

引用次数: 4

Towards Safer Communities: Detecting Aggression and Offensive Language in Code-Mixed Tweets to Combat Cyberbullying 迈向更安全的社区:检测混合代码推文中的攻击性和攻击性语言以打击网络欺凌

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.woah-1.3

Nazia Nafis, Diptesh Kanojia, Naveen Saini, Rudra Murthy

{"title":"Towards Safer Communities: Detecting Aggression and Offensive Language in Code-Mixed Tweets to Combat Cyberbullying","authors":"Nazia Nafis, Diptesh Kanojia, Naveen Saini, Rudra Murthy","doi":"10.18653/v1/2023.woah-1.3","DOIUrl":"https://doi.org/10.18653/v1/2023.woah-1.3","url":null,"abstract":"Cyberbullying is a serious societal issue widespread on various channels and platforms, particularly social networking sites. Such platforms have proven to be exceptionally fertile grounds for such behavior. The dearth of high-quality training data for multilingual and low-resource scenarios, data that can accurately capture the nuances of social media conversations, often poses a roadblock to this task. This paper attempts to tackle cyberbullying, specifically its two most common manifestations - aggression and offensiveness. We present a novel, manually annotated dataset of a total of 10,000 English and Hindi-English code-mixed tweets, manually annotated for aggression detection and offensive language detection tasks. Our annotations are supported by inter-annotator agreement scores of 0.67 and 0.74 for the two tasks, indicating substantial agreement. We perform comprehensive fine-tuning of pre-trained language models (PTLMs) using this dataset to check its efficacy. Our challenging test sets show that the best models achieve macro F1-scores of 67.87 and 65.45 on the two tasks, respectively. Further, we perform cross-dataset transfer learning to benchmark our dataset against existing aggression and offensive language datasets. We also present a detailed quantitative and qualitative analysis of errors in prediction, and with this paper, we publicly release the novel dataset, code, and models.","PeriodicalId":378248,"journal":{"name":"The 7th Workshop on Online Abuse and Harms (WOAH)","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132862017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resources for Automated Identification of Online Gender-Based Violence: A Systematic Review 网络性别暴力自动识别资源:系统回顾

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.woah-1.17

Gavin Abercrombie, Aiqi Jiang, Poppy Gerrard-abbott, Ioannis Konstas, Verena Rieser

引用次数: 1

Aporophobia: An Overlooked Type of Toxic Language Targeting the Poor 恐空症:一种被忽视的针对穷人的有毒语言

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.woah-1.12

S. Kiritchenko, Georgina Curto Rex, I. Nejadgholi, Kathleen C. Fraser

引用次数: 0

Relationality and Offensive Speech: A Research Agenda 关系与攻击性言语:一个研究议程

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.woah-1.8

Razvan Amironesei, Mark Díaz

引用次数: 0

“Female Astronaut: Because sandwiches won’t make themselves up there”: Towards Multimodal misogyny detection in memes “女宇航员:因为三明治不会在那里自己做”:在表情包中发现多模态厌女症

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.woah-1.15

Smriti Singh, Amritha Haridasan, R. Mooney

{"title":"“Female Astronaut: Because sandwiches won’t make themselves up there”: Towards Multimodal misogyny detection in memes","authors":"Smriti Singh, Amritha Haridasan, R. Mooney","doi":"10.18653/v1/2023.woah-1.15","DOIUrl":"https://doi.org/10.18653/v1/2023.woah-1.15","url":null,"abstract":"A rise in the circulation of memes has led to the spread of a new form of multimodal hateful content. Unfortunately, the degree of hate women receive on the internet is disproportionately skewed against them. This, combined with the fact that multimodal misogyny is more challenging to detect as opposed to traditional text-based misogyny, signifies that the task of identifying misogynistic memes online is one of utmost importance. To this end, the MAMI dataset was released, consisting of 12000 memes annotated for misogyny and four sub-classes of misogyny - shame, objectification, violence and stereotype. While this balanced dataset is widely cited, we find that the task itself remains largely unsolved. Thus, in our work, we investigate the performance of multiple models in an effort to analyse whether domain specific pretraining helps model performance. We also investigate why even state of the art models find this task so challenging, and whether domain-specific pretraining can help. Our results show that pretraining BERT on hateful memes and leveraging an attention based approach with ViT outperforms state of the art models by more than 10%. Further, we provide insight into why these models may be struggling with this task with an extensive qualitative analysis of random samples from the test set.","PeriodicalId":378248,"journal":{"name":"The 7th Workshop on Online Abuse and Harms (WOAH)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132810091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harmful Language Datasets: An Assessment of Robustness 有害语言数据集:鲁棒性评估

The 7th Workshop on Online Abuse and Harms (WOAH) Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.woah-1.24

Katerina Korre, John Pavlopoulos, Jeffrey Sorensen, Leo Laugier, I. Androutsopoulos, Lucas Dixon, Alberto Barrón-Cedeño

引用次数: 0