TY - GEN
T1 - Natural Language Contents Evaluation System for Detecting Fake News using Deep Learning
AU - Ahn, Ye Chan
AU - Jeong, Chang Sung
N1 - Funding Information:
This work was supported by Basic Science Research Program the National Reseaech Foundation of Korea(NRF) funded by the Ministry of Education (2017R1D1A1B03035461)
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - This Recently, a lot of information is spreading rapidly on SNS. Inaccurate communication of news media includes fears about unreliable sources and fake news that lacks confirmation of facts. Fake news is spread through SNS, causing social confusion and further economic loss. The purpose of the news is accurate information transmission. In this regard, it is very important to judge the discrepancies in the contents of the text and the distorted reports. We try to solve the problem of judging whether the sentence to be verified is correct after collecting the facts. This paper defines the problem of extracting the related sentences from the input sentence in Fact Data Corpus which is assumed to be fact and judging whether the extracted sentence and the input sentence are true or false. In the various NLP tasks, we create a Korean-specific pre-Training model using state-of-The-Art BERT. Using this model, fine-Tuning is performed to match the data set detected by Korean fake news. The AUROC score of 83.8% is derived from the test set generated using the fine-Tuned model.
AB - This Recently, a lot of information is spreading rapidly on SNS. Inaccurate communication of news media includes fears about unreliable sources and fake news that lacks confirmation of facts. Fake news is spread through SNS, causing social confusion and further economic loss. The purpose of the news is accurate information transmission. In this regard, it is very important to judge the discrepancies in the contents of the text and the distorted reports. We try to solve the problem of judging whether the sentence to be verified is correct after collecting the facts. This paper defines the problem of extracting the related sentences from the input sentence in Fact Data Corpus which is assumed to be fact and judging whether the extracted sentence and the input sentence are true or false. In the various NLP tasks, we create a Korean-specific pre-Training model using state-of-The-Art BERT. Using this model, fine-Tuning is performed to match the data set detected by Korean fake news. The AUROC score of 83.8% is derived from the test set generated using the fine-Tuned model.
KW - BERT
KW - Fake news Detection
KW - NLP
UR - http://www.scopus.com/inward/record.url?scp=85074227967&partnerID=8YFLogxK
U2 - 10.1109/JCSSE.2019.8864171
DO - 10.1109/JCSSE.2019.8864171
M3 - Conference contribution
AN - SCOPUS:85074227967
T3 - JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence
SP - 289
EP - 292
BT - JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Joint Conference on Computer Science and Software Engineering, JCSSE 2019
Y2 - 10 July 2019 through 12 July 2019
ER -