Datasets:
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: JournalistsQuestions
tags:
- question-identification
dataset_info:
features:
- name: tweet_id
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: label_confidence
dtype: float32
config_name: plain_text
splits:
- name: train
num_bytes: 342296
num_examples: 10077
download_size: 271039
dataset_size: 342296
Dataset Card for journalists_questions
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: http://qufaculty.qu.edu.qa/telsayed/datasets/
- Repository: [Needs More Information]
- Paper: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/download/13221/12856
- Leaderboard: [Needs More Information]
- Point of Contact: [Maram Hasanain] maram.hasanain@qu.edu.qa
Dataset Summary
The journalists_questions dataset supports question identification over Arabic tweets of journalists.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
Arabic
Dataset Structure
Data Instances
Our dataset supports question identification task. It includes 10K Arabic tweets crawled from journalists accounts. Tweets were labelled by crowdsourcing. Each tweet is associated with one label: question tweet or not. A question tweet is a tweet that has at least one interrogative question. Each label is associated with a number that represents the confidence in the label, given that each tweet was labelled by 3 annotators and an aggregation method was followed to choose the final label. Below is an example: { 'tweet_id': '493235142128074753', 'label': 'yes', 'label_confidence':0.6359 }
Data Fields
tweet_id: the Twitter assigned ID for the tweet object. label: annotation of the tweet by whether it is a question or not label_confidence: confidence score for the label given annotations of multiple annotators per tweet
Data Splits
[More Information Needed]
Dataset Creation
Curation Rationale
The dataset includes tweet IDs only due to Twitter content re-distribution policy. It was created and shared for research purposes for parties interested in understanding questions expecting answers by Arab journalists on Twitter.
Source Data
Initial Data Collection and Normalization
To construct our dataset of question tweets posted by journalists, we first acquire a list of Twitter accounts of 389 Arab journalists. We use the Twitter API to crawl their available tweets, keeping only those that are identified by Twitter to be both Arabic, and not retweets (as these would contain content that was not originally authored by journalists). We apply a rule-based question filter to this dataset of 465,599 tweets, extracting 49,119 (10.6%) potential question tweets from 363 (93.3%) Arab journalists.
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]
Contributions
Thanks to @MaramHasanain for adding this dataset.