Cheng, Guixiang
Yan, Xin
Gao, Shengxiang
Xu, Guangyi
Miao, Xianghua
Chapter History
First Online: 17 September 2023
Ethical Statement
: The purpose of this paper is to explore efficient and effective methods for learning cascade graphs for popularity prediction while adhering to academic integrity and research ethics requirements. We used publicly available data from social media datasets that have been authorized by Twitter and Weibo officials. To ensure the confidentiality of personal information, all data is anonymized and stored securely. We obtained approval and permission from the ethics committee of our institution to conduct this research.The models and algorithms used in this study are based on publicly available data and previous research results, and we have thoroughly tested and verified them. We commit to conducting a transparent and fair evaluation of the algorithms and models used in this research, and we will present them fully in the paper.Throughout this study, we will adhere to academic standards and ethical requirements, striving to avoid any behavior that may violate these requirements. We hope that this research will contribute to the development of cascade graph learning and popularity prediction, promoting further research in this area.
Conference Information
Conference Acronym: ECML PKDD
Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Conference City: Turin
Conference Country: Italy
Conference Year: 2023
Conference Start Date: 18 September 2023
Conference End Date: 22 September 2023
Conference Number: 23
Conference ID: ecml2023
Conference URL: https://2023.ecmlpkdd.org/
Peer Review Information (provided by the conference organizers)
Type: Double-blind
Conference Management System: CMT
Number of Submissions Sent for Review: 829
Number of Full Papers Accepted: 196
Number of Short Papers Accepted: 0
Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.
Average Number of Reviews per Paper: 3.63
Average Number of Papers per Reviewer: 4.5
External Reviewers Involved: Yes
Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.