Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection
مجموعة بيانات Twitter متعددة الملصقات على نطاق واسع للكشف عن الإلكترونية وإساءة الاستخدام عبر الإنترنت
350
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذه الورقة، نقدم مجموعة بيانات جديدة تستند إلى Twitter للكشف عن السيبراني وإساءة استخدام عبر الإنترنت.تضم هذه البيانات التي تضم 62،587 تغريدات، تم الحصول على هذه البيانات من تويتر باستخدام شروط استعلام محددة تهدف إلى استرداد تغريدات مع احتمالات عالية من أشكال مختلفة من البلطجة والمحتوى المسيء، بما في ذلك الإهانة والتصيد والبهجة والسخرية والتهديد والإباحية والاستبعاد.لقد قامنا بتجنيد مجموعة من 17 ملقاة لأداء التعليق التوضيحي بحبائهم الجميلة على مجموعة بيانات كل تغريدة موضحة بمثابة ثلاثة محنوح.جميع الحناحيين لدينا هي مستخدمي التعليم العالي والمتكرر في المدرسة الثانوية.اتفاقية المشتركة بين الخصوصية لأن مجموعة البيانات التي تقاسها Krippendorff's ألفا هي 0.67.تم تأكيد التحليلات التي أجريتها في مجموعة بيانات الموضوعات الإلكترونية المشتركة التي أبلغت عن دراسات أخرى وكشفت علاقات مثيرة للاهتمام بين الطبقات.تم استخدام DataSet لتدريب عدد من نماذج التعلم العميقة المستندة إلى المحولات التي تعود إلى نتائج مثيرة للإعجاب.
In this paper, we introduce a new English Twitter-based dataset for cyberbullying detection and online abuse. Comprising 62,587 tweets, this dataset was sourced from Twitter using specific query terms designed to retrieve tweets with high probabilities of various forms of bullying and offensive content, including insult, trolling, profanity, sarcasm, threat, porn and exclusion. We recruited a pool of 17 annotators to perform fine-grained annotation on the dataset with each tweet annotated by three annotators. All our annotators are high school educated and frequent users of social media. Inter-rater agreement for the dataset as measured by Krippendorff's Alpha is 0.67. Analysis performed on the dataset confirmed common cyberbullying themes reported by other studies and revealed interesting relationships between the classes. The dataset was used to train a number of transformer-based deep learning models returning impressive results.
References used
https://aclanthology.org/
rate research
Read More
As the world continues to fight the COVID-19 pandemic, it is simultaneously fighting an infodemic' -- a flood of disinformation and spread of conspiracy theories leading to health threats and the division of society. To combat this infodemic, there i
s an urgent need for benchmark datasets that can help researchers develop and evaluate models geared towards automatic detection of disinformation. While there are increasing efforts to create adequate, open-source benchmark datasets for English, comparable resources are virtually unavailable for German, leaving research for the German language lagging significantly behind. In this paper, we introduce the new benchmark dataset FANG-COVID consisting of 28,056 real and 13,186 fake German news articles related to the COVID-19 pandemic as well as data on their propagation on Twitter. Furthermore, we propose an explainable textual- and social context-based model for fake news detection, compare its performance to black-box'' models and perform feature ablation to assess the relative importance of human-interpretable features in distinguishing fake news from authentic news.
Online abuse and offensive language on social media have become widespread problems in today's digital age. In this paper, we contribute a Reddit-based dataset, consisting of 68,159 insults and 51,102 compliments targeted at individuals instead of ta
rgeting a particular community or race. Secondly, we benchmark multiple existing state-of-the-art models for both classification and unsupervised style transfer on the dataset. Finally, we analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.
Recent works have found evidence of gender bias in models of machine translation and coreference resolution using mostly synthetic diagnostic datasets. While these quantify bias in a controlled experiment, they often do so on a small scale and consis
t mostly of artificial, out-of-distribution sentences. In this work, we find grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments (e.g., female nurses versus male dancers) in corpora from three domains, resulting in a first large-scale gender bias dataset of 108K diverse real-world English sentences. We manually verify the quality of our corpus and use it to evaluate gender bias in various coreference resolution and machine translation models. We find that all tested models tend to over-rely on gender stereotypes when presented with natural inputs, which may be especially harmful when deployed in commercial systems. Finally, we show that our dataset lends itself to finetuning a coreference resolution model, finding it mitigates bias on a held out set. Our dataset and models are publicly available at github.com/SLAB-NLP/BUG. We hope they will spur future research into gender bias evaluation mitigation techniques in realistic settings.
People utilize online forums to either look for information or to contribute it. Because of their growing popularity, certain online forums have been created specifically to provide support, assistance, and opinions for people suffering from mental i
llness. Depression is one of the most frequent psychological illnesses worldwide. People communicate more with online forums to find answers for their psychological disease. However, there is no mechanism to measure the severity of depression in each post and give higher importance to those who are diagnosed more severely depressed. Despite the fact that numerous researches based on online forum data and the identification of depression have been conducted, the severity of depression is rarely explored. In addition, the absence of datasets will stymie the development of novel diagnostic procedures for practitioners. From this study, we offer a dataset to support research on depression severity evaluation. The computational approach to measure an automatic process, identified severity of depression here is quite novel approach. Nonetheless, this elaborate measuring severity of depression in online forum posts is needed to ensure the measurement scales used in our research meets the expected norms of scientific research.
We introduce MULTI-EURLEX, a new multilingual dataset for topic classification of legal documents. The dataset comprises 65k European Union (EU) laws, officially translated in 23 languages, annotated with multiple labels from the EUROVOC taxonomy. We
highlight the effect of temporal concept drift and the importance of chronological, instead of random splits. We use the dataset as a testbed for zero-shot cross-lingual transfer, where we exploit annotated training documents in one language (source) to classify documents in another language (target). We find that fine-tuning a multilingually pretrained model (XLM-ROBERTA, MT5) in a single source language leads to catastrophic forgetting of multilingual knowledge and, consequently, poor zero-shot transfer to other languages. Adaptation strategies, namely partial fine-tuning, adapters, BITFIT, LNFIT, originally proposed to accelerate fine-tuning for new end-tasks, help retain multilingual knowledge from pretraining, substantially improving zero-shot cross-lingual transfer, but their impact also depends on the pretrained model used and the size of the label set.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
