TY - GEN
T1 - Predicting Online News Authorship by an Authorship Embeddings Space Method
AU - Wen, Wanting
AU - Li, Qiudan
AU - Li, Junfeng
AU - Zhang, Xu
AU - Zeng, Daniel
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported in part by the Ministry of Science and Technology of China Major Grant #2016QY02D0305, NSFC Grant #71621002, NSFC Grant # U1636123ˈand CAS Key Grant #ZDRW-XH-2017-3. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Ministry of Science and Technology of China Major Grant, NSFC and CAS Key Grant.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - In this paper, we study the problem of authorship identification in online news data. Most of the existing approaches predict authorship via feature engineering, which cannot focus on important attributes. We designed an authorship identification method named Authorship Embeddings Space model (AES) to predict the online news authorship between online news and authors. First, we propose an authorship space to represent the deep semantic relationship of news content. Second, we use an embedding learning method to perform the relationship between authors and news. Finally, we formulated an authorship prediction algorithm to identify the news authors based on the authorship embeddings. Experimental results on the online news dataset reveal that the AES model outperforms the baseline models.
AB - In this paper, we study the problem of authorship identification in online news data. Most of the existing approaches predict authorship via feature engineering, which cannot focus on important attributes. We designed an authorship identification method named Authorship Embeddings Space model (AES) to predict the online news authorship between online news and authors. First, we propose an authorship space to represent the deep semantic relationship of news content. Second, we use an embedding learning method to perform the relationship between authors and news. Finally, we formulated an authorship prediction algorithm to identify the news authors based on the authorship embeddings. Experimental results on the online news dataset reveal that the AES model outperforms the baseline models.
KW - Authorship identification
KW - embeddings methods
KW - online news authorship prediction
UR - http://www.scopus.com/inward/record.url?scp=85085956974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085956974&partnerID=8YFLogxK
U2 - 10.1109/ICBDA49040.2020.9101269
DO - 10.1109/ICBDA49040.2020.9101269
M3 - Conference contribution
AN - SCOPUS:85085956974
T3 - 2020 5th IEEE International Conference on Big Data Analytics, ICBDA 2020
SP - 368
EP - 372
BT - 2020 5th IEEE International Conference on Big Data Analytics, ICBDA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Big Data Analytics, ICBDA 2020
Y2 - 8 May 2020 through 11 May 2020
ER -