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VIDEO DOI: https://doi.org/10.48448/azk6-0m79

workshop paper

ACL 2024

August 16, 2024

Bangkok, Thailand

LexiconLadies at FIGNEWS 2024 Shared Task: Identifying Keywords for Bias Annotation Guidelines of Facebook News Headlines on the Israel-Palestine 2023 War

keywords:

collocation analysis

keyword classification

contextual understanding

annotation guidelines

bias detection

News bias is difficult for humans to identify, but even more so for machines. This is largely due to the lack of linguistically appropriate annotated datasets suitable for use by classifier algorithms. The FIGNEWS Subtask 1: Bias Annotation involved classifying bias through manually annotated 1800 headlines from social media. Our proposed guidelines investigated which combinations of keywords available for classification, across sentence and token levels, may be used to detect possible bias in a conflict where neutrality is highly undesirable. Much of the headlines’ percentage required contextual knowledge of events to identify criteria that matched biased or targeted language. The final annotation guidelines paved the way for a theoretical system which uses keyword and hashtag significance to classify major instances of bias. Minor instances with bias undertones or clickbait may require advanced machine learning methods which learn context through scraping user engagements on social media.

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Transcript English (automatic)

Next from ACL 2024

Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias
workshop paper

Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias

ACL 2024

+1
Abdusalam Nwesri and 3 other authors

16 August 2024

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