6 papers
A Comprehensive Study on GDPR-Oriented Analysis of Privacy Policies: Taxonomy, Corpus and GDPR Concept Classifiers
Peng Tang, Xin Li, Yuxin Chen +5
Machine learning based classifiers that take a privacy policy as the input and predict relevant concepts are useful in different applications such as (semi-)automated compliance an…
Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation
Xin Yuan, Jie Guo, Weidong Qiu +2
Mis- and disinformation online have become a major societal problem as major sources of online harms of different kinds. One common form of mis- and disinformation is out-of-contex…
"I know myself better, but not really greatly": How Well Can LLMs Detect and Explain LLM-Generated Texts?
Jiazhou Ji, Jie Guo, Weidong Qiu +6
Distinguishing between human- and LLM-generated texts is crucial given the risks associated with misuse of LLMs. This paper investigates detection and explanation capabilities of c…
Detecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicated
Jiazhou Ji, Ruizhe Li, Shujun Li +6
As LLMs rapidly advance, increasing concerns arise regarding risks about actual authorship of texts we see online and in real world. The task of distinguishing LLM-authored texts i…
Using LLMs for Automated Privacy Policy Analysis: Prompt Engineering, Fine-Tuning and Explainability
Yuxin Chen, Peng Tang, Weidong Qiu +1
Privacy policies are widely used by digital services and often required for legal purposes. Many machine learning based classifiers have been developed to automate detection of dif…
Everyone's Privacy Matters! An Analysis of Privacy Leakage from Real-World Facial Images on Twitter and Associated User Behaviors
Yuqi Niu, Weidong Qiu, Peng Tang +5
Online users often post facial images of themselves and other people on online social networks (OSNs) and other Web 2.0 platforms, which can lead to potential privacy leakage of pe…