Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLet-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language
Let-Mi: مجموعة بيانات Twitter العربية ل Leavantine لغوي كراهية
311
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
أصبح على الإنترنت على الإنترنت مصدر قلق متزايد للنساء العربيات الذين يعانون من سوء المعاملة على الإنترنت على أساس الجنس على أساس يومي. يمكن أن تساعد أنظمة الكشف الأوتوماتيكية في كراثيلي في حظر المحتوى السام العربي للمرأة. يتم إعاقة تطوير هذه النظم من خلال عدم وجود مجموعات البيانات العربية القياسية. في هذه الورقة، نقدم مجموعة بيانات Twitter العربية ل Leavantine للغة الكهثرية (LET-MI) ليكون أول مجموعة بيانات معيار للكريساني. نوفر كذلك مراجعة تفصيلية لخلق البيانات ومراحل التوضيحية. تم التأكيد على اتساق التوضيحية الخاصة ب DataSet المقترحة من خلال تدابير تقييم الاتفاقية بين الخصوصية. علاوة على ذلك، تم استخدام دعونا كمادة بيانات تقييم من خلال مهام التصنيف الثنائية / متعددة المستهدفة التي أجرتها العديد من أنظمة تعلم الآلات ذات الجهاز من خلال تكوين التعلم المتعدد المهام (MTL). أشارت النتائج التي تم الحصول عليها إلى أن العروض التي حققتها النظم المستخدمة تتسق مع النتائج الحديثة لغات أخرى غير اللغة العربية، أثناء توظيف MTL تحسن أداء مهام التصنيف الكبرى / الهدف.
Online misogyny has become an increasing worry for Arab women who experience gender-based online abuse on a daily basis. Misogyny automatic detection systems can assist in the prohibition of anti-women Arabic toxic content. Developing such systems is hindered by the lack of the Arabic misogyny benchmark datasets. In this paper, we introduce an Arabic Levantine Twitter dataset for Misogynistic language (LeT-Mi) to be the first benchmark dataset for Arabic misogyny. We further provide a detailed review of the dataset creation and annotation phases. The consistency of the annotations for the proposed dataset was emphasized through inter-rater agreement evaluation measures. Moreover, Let-Mi was used as an evaluation dataset through binary/multi-/target classification tasks conducted by several state-of-the-art machine learning systems along with Multi-Task Learning (MTL) configuration. The obtained results indicated that the performances achieved by the used systems are consistent with state-of-the-art results for languages other than Arabic, while employing MTL improved the performance of the misogyny/target classification tasks.
References used
https://aclanthology.org/
rate research
Read More
In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that spans one year, covering the period from 27th of January 2020 till 31st of January 2021. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pan
demic that includes about 2.7M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and -liked). The propagation networks include both retweetsand conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world.In addition to the source tweets and the propagation networks, we also release the search queries and the language-independent crawler used to collect the tweets to encourage the curation of similar datasets.
This paper describes the annotation process of an offensive language data set for Romanian on social media. To facilitate comparable multi-lingual research on offensive language, the annotation guidelines follow some of the recent annotation efforts
for other languages. The final corpus contains 5000 micro-blogging posts annotated by a large number of volunteer annotators. The inter-annotator agreement and the initial automatic discrimination results we present are in line with earlier annotation efforts.
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 probabiliti
es 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.
In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020. We collected 138 verified claims, mostly from popular fact-c
hecking websites, and identified 9.4K relevant tweets to those claims. Tweets were manually-annotated by veracity to support research on misinformation detection, which is one of the major problems faced during a pandemic. ArCOV19-Rumors supports two levels of misinformation detection over Twitter: verifying free-text claims (called claim-level verification) and verifying claims expressed in tweets (called tweet-level verification). Our dataset covers, in addition to health, claims related to other topical categories that were influenced by COVID-19, namely, social, politics, sports, entertainment, and religious. Moreover, we present benchmarking results for tweet-level verification on the dataset. We experimented with SOTA models of versatile approaches that either exploit content, user profiles features, temporal features and propagation structure of the conversational threads for tweet verification.
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the
narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
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
