TY - GEN
T1 - Deep Learning for Native Advertisement Detection in Electronic News
T2 - 11th Electrical Power, Electronics, Communications, Control, and Informatics Seminar, EECCIS 2022
AU - Darnoto, Brian Rizqi Paradisiaca
AU - Siahaan, Daniel
AU - Purwitasari, Diana
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Native advertising is a type of commercial hybrid content that has successfully targeted online consumers. Native ads have become popular, especially on online platforms. People often don't realize that they are reading sponsored and paid content making native advertising very effective. Despite these benefits, native advertising has provoked strong negative responses, often accompanied by criticism and avoidance of the ads. This study aims to investigate the performance of different word embedding methods (BERT, GloVe, FastText) when used in combination with different deep learning methods (BiLSTM, CNN, LSTM) in detecting native ads in electronic news. The dataset used in this study was collected from the news portal, detik.com. Among these models, the BERT-BiLSTM model achieved the highest accuracy, f1 score, recall, precision and AUC score, all with the same value of 95%, compared to the other models, showing that the BERT-BiLSTM model outperformed the other models.
AB - Native advertising is a type of commercial hybrid content that has successfully targeted online consumers. Native ads have become popular, especially on online platforms. People often don't realize that they are reading sponsored and paid content making native advertising very effective. Despite these benefits, native advertising has provoked strong negative responses, often accompanied by criticism and avoidance of the ads. This study aims to investigate the performance of different word embedding methods (BERT, GloVe, FastText) when used in combination with different deep learning methods (BiLSTM, CNN, LSTM) in detecting native ads in electronic news. The dataset used in this study was collected from the news portal, detik.com. Among these models, the BERT-BiLSTM model achieved the highest accuracy, f1 score, recall, precision and AUC score, all with the same value of 95%, compared to the other models, showing that the BERT-BiLSTM model outperformed the other models.
KW - Comparative Study
KW - Deep Learning
KW - Electronic News
KW - Native Ads
KW - Word Embedding
UR - http://www.scopus.com/inward/record.url?scp=85140639721&partnerID=8YFLogxK
U2 - 10.1109/EECCIS54468.2022.9902953
DO - 10.1109/EECCIS54468.2022.9902953
M3 - Conference contribution
AN - SCOPUS:85140639721
T3 - Proceedings - 11th Electrical Power, Electronics, Communications, Control, and Informatics Seminar, EECCIS 2022
SP - 304
EP - 309
BT - Proceedings - 11th Electrical Power, Electronics, Communications, Control, and Informatics Seminar, EECCIS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 August 2022 through 25 August 2022
ER -
