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We now introduce IndicGLUE, the Indic General Language Understanding Evaluation Benchmark, which is a collection of various NLP tasks as de- scribed below. The goal is to provide an evaluation benchmark for natural language understanding ca- pabilities of NLP models on diverse tasks and mul- tiple Indian languages.
MasakhaNEWS is a benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. Source: MasakhaNEWS: News Topic Classification for African languages
This is a new dataset of news headlines and their frames related to the issue of gun violence in the United States. This Gun Violence Frame Corpus (GVFC) was curated and annotated by journalism and communication experts. The articles in this dataset are drawn from a sample of news articles from a list of 30 top U.S. news websites defined in terms of traffic to the websites; and collected from four time periods over the course of 2018 in order to capture a diversity of articles. We include in this dataset, headlines of news articles and their annotations, the accompanying images and text- and image-derived features. We also include the codebook protocol, which includes all of the variables for annotations and their definitions that are applied by the annotators.
Multilabeled News Dataset (MN-DS) is a dataset for news classification. It consists of 10,917 articles in 17 first-level and 109 second-level categories from 215 media sources.
The Headline Grouping dataset is a binary classification dataset on pairs of news headline. For each pair of headline, the binary label indicates whether the two headlines are part of the same group (and describe the same underlying event), or whether they are in distinct groups. The dataset contains a total of 20k annotated headline pairs, further split in a train, validation and test portions.
Articles originating from subreddits with explicitly stated ideologies are categorized into three groups: 72,488 articles in the Liberal class, 79,573 articles in the Conservative class, and 225,083 articles in the Restricted class. Conversely, articles from subreddits lacking a clearly defined ideology, including those with implicit or explicit ideologies, are merged to form a holdout dataset comprising 922,522 articles. Part 1 https://data.mendeley.com/datasets/2tdr9sjd83/3 Part 2 https://data.mendeley.com/datasets/dxpp5983yb/1 Part 3 https://data.mendeley.com/datasets/f7knr8r94w/1
Two news datasets (KINNEWS and KIRNEWS) for multi-class classification of news articles in Kinyarwanda and Kirundi, two low-resource African languages. The two languages are mutually intelligible. Source: KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi
N15News is a large-scale multimodal news dataset comprising 200K imagetext pairs and 15 categories, which exceeding the previous news dataset in both the number of categories and samples. Image source: https://arxiv.org/pdf/2108.13327v1.pdf
Verifee is a dataset of news articles with fine-grained trustworthiness annotations. It contains over 10, 000 unique articles from almost 60 Czech online news sources. These are categorized into one of the 4 classes across the credibility spectrum we propose, raging from entirely trustworthy articles all the way to the manipulative ones. Source: Fine-grained Czech News Article Dataset: An Interdisciplinary Approach to Trustworthiness Analysis Image Source:
A Filipino multi-modal language dataset for text+visual tasks. Consists of 351,755 Filipino news articles gathered from Filipino news outlets. Each entry contains: * body - Article text * title - Article title * website - Name of the news outlet * category - News category given by the news outlet * date - Date published * author - Article author * url - URL of the article * img_url - URL of the article image * img_path - Filename of the image in the dataset
The CIDII dataset is a binary classification, consisting of two classes of correct information and disinformation related to Islamic issues. The CIDII dataset belongs to our research (DISINFORMATION DETECTION ABOUT ISLAMIC ISSUES ON SOCIAL MEDIA USING DEEP LEARNING TECHNIQUES) published in MJCS journal in the link below: https://ejournal.um.edu.my/index.php/MJCS/article/view/41935 This dataset consists of five columns: 1- ID: Each article has a unique ID. 2- Article: The article contains text that is either facts related to Islamic issues if the information is correct, or posts targeting the Islamic religion if the information is false. Most posts contain only the body without a title. 3- Propagation Source: The source refers to the source of the article content, as it contains a Facebook link in the event that the post is disinformation, or it contains a link to Islamic websites in the event that the article refers to correct information (an explanation of a verse, a hadith, or an article related to the Islamic religion). 4- Article Type: This column contains the type of article published. Is it a post if the article is disinformation, or is it an Islamic article, a Quranic interpretation, or a hadith, in case of that the information is correct? 5- Class Type: This column shows whether the article belongs to the category of correct information or disinformation.
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