Task Categories: text-classification
Languages: ar
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: unknown
Language Creators: other
Annotations Creators: crowdsourced
Source Datasets: original

Dataset Card for journalists_questions

Dataset Summary

The journalists_questions dataset supports question identification over Arabic tweets of journalists.

Supported Tasks and Leaderboards

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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

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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?

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Thanks to @MaramHasanain for adding this dataset.

Models trained or fine-tuned on journalists_questions

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