TY - GEN
T1 - Identifying Gender Bias in Online Crime News Indonesia Using Word Embedding
AU - Sulastri, Miftakhul Janah
AU - Aini Rakhmawati, Nur
AU - Indraswari, Rarasmaya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the digital era, news portals have become a primary source of information for millions of individuals. This study investigated the potential influence of word choice and gender representation in news on the public's perception of gender, emphasizing its implications for gender equality and human rights. Research has shown that the language used in news reporting can reflect gender bias, highlighting the significance of analyzing gender representation in crime-related news. A word-embedding model was employed to identify and mitigate bias in word representation and ensure fairness in the data analysis. This study aims to enhance our understanding of gender representation in Indonesian crime-related news and to apply word-embedding techniques to identify biases in word representation. The results indicate a potential bias in word embeddings, emphasizing the importance of addressing and mitigating biases in language models to avoid reinforcing unfair stereotypes.
AB - In the digital era, news portals have become a primary source of information for millions of individuals. This study investigated the potential influence of word choice and gender representation in news on the public's perception of gender, emphasizing its implications for gender equality and human rights. Research has shown that the language used in news reporting can reflect gender bias, highlighting the significance of analyzing gender representation in crime-related news. A word-embedding model was employed to identify and mitigate bias in word representation and ensure fairness in the data analysis. This study aims to enhance our understanding of gender representation in Indonesian crime-related news and to apply word-embedding techniques to identify biases in word representation. The results indicate a potential bias in word embeddings, emphasizing the importance of addressing and mitigating biases in language models to avoid reinforcing unfair stereotypes.
KW - Bias Gender
KW - Crime Online News
KW - PCA
KW - Word Embedding
KW - Word2vec
UR - http://www.scopus.com/inward/record.url?scp=85186492247&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA60881.2023.10427911
DO - 10.1109/ICAMIMIA60881.2023.10427911
M3 - Conference contribution
AN - SCOPUS:85186492247
T3 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
SP - 774
EP - 778
BT - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Y2 - 14 November 2023 through 15 November 2023
ER -
