Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCharacterizing English Variation across Social Media Communities with BERT
وصف تباين اللغة الإنجليزية عبر مجتمعات وسائل التواصل الاجتماعي مع بيرت
396
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
مجردة الكثير من العمل السابق الذي تميز تباين اللغة عبر الإنترنت، ركزت مجموعات الاجتماعية على الإنترنت على أنواع الكلمات التي تستخدمها هذه المجموعات.نحن نقدم هذا النوع من الدراسة من خلال توظيف بيرت لتوصيف الاختلاف في حواس الكلمات أيضا، وتحليل شهرين من التعليقات الإنجليزية في 474 مجتمعات Reddit.يتم استخدام خصوصية مجموعات الشعور المختلفة للمجتمع، جنبا إلى جنب مع خصوصية أنواع الكلمات الفريدة للمجتمع، لتحديد الحالات التي تنحرف فيها لغة مجموعة اجتماعية عن القاعدة.نحن نقوم بالتحقق من صحة مقاييسنا باستخدام المعلقات التي تم إنشاؤها من قبل المستخدم وارسم النظريات الاجتماعية لتوصيل تباين اللغة بالاتجاهات في سلوك المجتمع.نجد أن المجتمعات ذات اللغة المميزة للغاية هي متوسطة الحجم، وتفاعل المستخدمين المواليين والمخروطين للغاية في الشبكات الكثيفة.
Abstract Much previous work characterizing language variation across Internet social groups has focused on the types of words used by these groups. We extend this type of study by employing BERT to characterize variation in the senses of words as well, analyzing two months of English comments in 474 Reddit communities. The specificity of different sense clusters to a community, combined with the specificity of a community's unique word types, is used to identify cases where a social group's language deviates from the norm. We validate our metrics using user-created glossaries and draw on sociolinguistic theories to connect language variation with trends in community behavior. We find that communities with highly distinctive language are medium-sized, and their loyal and highly engaged users interact in dense networks.
References used
https://aclanthology.org/
rate research
Read More
This paper describes the Helsinki--Ljubljana contribution to the VarDial 2021 shared task on social media variety geolocation. Following our successful participation at VarDial 2020, we again propose constrained and unconstrained systems based on the
BERT architecture. In this paper, we report experiments with different tokenization settings and different pre-trained models, and we contrast our parameter-free regression approach with various classification schemes proposed by other participants at VarDial 2020. Both the code and the best-performing pre-trained models are made freely available.
Language use differs between domains and even within a domain, language use changes over time. For pre-trained language models like BERT, domain adaptation through continued pre-training has been shown to improve performance on in-domain downstream t
asks. In this article, we investigate whether temporal adaptation can bring additional benefits. For this purpose, we introduce a corpus of social media comments sampled over three years. It contains unlabelled data for adaptation and evaluation on an upstream masked language modelling task as well as labelled data for fine-tuning and evaluation on a downstream document classification task. We find that temporality matters for both tasks: temporal adaptation improves upstream and temporal fine-tuning downstream task performance. Time-specific models generally perform better on past than on future test sets, which matches evidence on the bursty usage of topical words. However, adapting BERT to time and domain does not improve performance on the downstream task over only adapting to domain. Token-level analysis shows that temporal adaptation captures event-driven changes in language use in the downstream task, but not those changes that are actually relevant to task performance. Based on our findings, we discuss when temporal adaptation may be more effective.
Social media texts such as blog posts, comments, and tweets often contain offensive languages including racial hate speech comments, personal attacks, and sexual harassment. Detecting inappropriate use of language is, therefore, of utmost importance
for the safety of the users as well as for suppressing hateful conduct and aggression. Existing approaches to this problem are mostly available for resource-rich languages such as English and German. In this paper, we characterize the offensive language in Nepali, a low-resource language, highlighting the challenges that need to be addressed for processing Nepali social media text. We also present experiments for detecting offensive language using supervised machine learning. Besides contributing the first baseline approaches of detecting offensive language in Nepali, we also release human annotated data sets to encourage future research on this crucial topic.
Cross-language authorship attribution is the challenging task of classifying documents by bilingual authors where the training documents are written in a different language than the evaluation documents. Traditional solutions rely on either translati
on to enable the use of single-language features, or language-independent feature extraction methods. More recently, transformer-based language models like BERT can also be pre-trained on multiple languages, making them intuitive candidates for cross-language classifiers which have not been used for this task yet. We perform extensive experiments to benchmark the performance of three different approaches to a smallscale cross-language authorship attribution experiment: (1) using language-independent features with traditional classification models, (2) using multilingual pre-trained language models, and (3) using machine translation to allow single-language classification. For the language-independent features, we utilize universal syntactic features like part-of-speech tags and dependency graphs, and multilingual BERT as a pre-trained language model. We use a small-scale social media comments dataset, closely reflecting practical scenarios. We show that applying machine translation drastically increases the performance of almost all approaches, and that the syntactic features in combination with the translation step achieve the best overall classification performance. In particular, we demonstrate that pre-trained language models are outperformed by traditional models in small scale authorship attribution problems for every language combination analyzed in this paper.
Rumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use. However, on the one hand, rumor detection datasets in Chinese companies with comments are rare; on the other han
d, intensive interaction of attention on Transformer-based models like BERT may hinder performance improvement. To alleviate these problems, we build a new Chinese microblog dataset named Weibo20 by collecting posts and associated comments from Sina Weibo and propose a new ensemble named STANKER (Stacking neTwork bAsed-on atteNtion-masKed BERT). STANKER adopts two level-grained attention-masked BERT (LGAM-BERT) models as base encoders. Unlike the original BERT, our new LGAM-BERT model takes comments as important auxiliary features and masks co-attention between posts and comments on lower-layers. Experiments on Weibo20 and three existing social media datasets showed that STANKER outperformed all compared models, especially beating the old state-of-the-art on Weibo dataset.
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
