Predicting and Analyzing Privacy Settings and Categories for Posts on Social Media

Hsin Yu Chen, Cheng Te Li

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

While social media is prevalent in people's daily life, privacy control of user-generated posts is becoming increasingly important. In this paper, we propose to enable automatic privacy control for social media posts through two tasks, predicting privacy settings and predicting privacy categories. The former is to recommend the proper settings of privacy levels, including family, close, casual, and outside, for a post. The latter is to predict the categories of privacy concerns for a post. We propose a multi-task learning-based approach, along with learning feature representation of each post, for such two tasks. Experiments conducted on a real dataset with tweet posts exhibit promising performance of our model, and thus encourage further investigation of privacy-related tasks for privacy control on social media. We also provide a series of extensive analysis with insights that reveal the hidden correlation between privacy settings/categories and post texts.

原文English
主出版物標題Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
編輯Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
發行者Institute of Electrical and Electronics Engineers Inc.
頁面5692-5697
頁數6
ISBN(電子)9781665480451
DOIs
出版狀態Published - 2022
事件2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
持續時間: 2022 12月 172022 12月 20

出版系列

名字Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference2022 IEEE International Conference on Big Data, Big Data 2022
國家/地區Japan
城市Osaka
期間22-12-1722-12-20

All Science Journal Classification (ASJC) codes

  • 建模與模擬
  • 電腦網路與通信
  • 資訊系統
  • 資訊系統與管理
  • 安全、風險、可靠性和品質
  • 控制和優化

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