Journal
INTERNATIONAL JOURNAL OF AMBIENT COMPUTING AND INTELLIGENCE
Volume 12, Issue 2, Pages 21-32Publisher
IGI GLOBAL
DOI: 10.4018/IJACI.2021040102
Keywords
BERT; Deep Learning Model; ELMO; Feature Extractor; Glove
Categories
Ask authors/readers for more resources
The authors present a clickbait identification system combining deep learning and external knowledge, training on social media posts using pre-trained ELMO and BERT models. They conducted experiments with different regularization techniques and achieved a clickbait identification accuracy of 0.847, improving on previous results.
Clickbait is an elusive challenge with the prevalence of social media such as Facebook and Twitter that misleads the readers while clicking on headlines. Limited annotated data makes it onerous to design an accurate clickbait identification system. The authors address this problem by purposing deep learning-based architecture with external knowledge which trains on social media post and descriptions. The pre-trained ELMO and BERT model obtains the sentence level contextual feature as knowledge; moreover, the LSTM layer helps to prevail the word level contextual feature. Training has done at different experiments (model with EMLO, model with BERT) with different regularization techniques such as dropout, early stopping, and finetuning. Forward context-aware clickbait tweet identification system (FCCTI) with BERT finetuning and model with ELMO using glove pre-trained embedding is the best model and achieves a clickbait identification accuracy of 0.847, improving on the previous baseline for this task.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available