Zhuo Chen, Jiawei Liu, Haotan Liu (Wuhan University)

Neural network models have been widely applied in the field of information retrieval, but their vulnerability has always been a significant concern. In retrieval of public topics, the problems posed by the vulnerability are not only returning inaccurate or irrelevant content, but also returning manipulated opinions. One can distort the original ranking order based on the stance of the retrieved opinions, potentially influencing the searcher’s perception of the topic, weakening the reliability of retrieval results and damaging the fairness of opinion ranking. Based on the aforementioned challenges, we combine stance detection methods with existing text ranking manipulation methods to experimentally demonstrate the feasibility and threat of opinion manipulation. Then we design a user experiment in which each participant independently rated the credibility of the target topic based on the unmanipulated or manipulated retrieval results. The experimental result indicates that opinion manipulation can effectively influence people’s perceptions of the target topic. Furthermore, we preliminarily propose countermeasures to address the issue of opinion manipulation and build more reliable and fairer retrieval ranking systems.

View More Papers

IdleLeak: Exploiting Idle State Side Effects for Information Leakage

Fabian Rauscher (Graz University of Technology), Andreas Kogler (Graz University of Technology), Jonas Juffinger (Graz University of Technology), Daniel Gruss (Graz University of Technology)

Read More

The impact of data-heavy, post-quantum TLS 1.3 on the...

Panos Kampanakis and Will Childs-Klein (AWS)

Read More

Decentralized Information-Flow Control for ROS2

Nishit V. Pandya (Indian Institute of Science Bangalore), Himanshu Kumar (Indian Institute of Science Bangalore), Gokulnath M. Pillai (Indian Institute of Science Bangalore), Vinod Ganapathy (Indian Institute of Science Bangalore)

Read More

AdvCAPTCHA: Creating Usable and Secure Audio CAPTCHA with Adversarial...

Hao-Ping (Hank) Lee (Carnegie Mellon University), Wei-Lun Kao (National Taiwan University), Hung-Jui Wang (National Taiwan University), Ruei-Che Chang (University of Michigan), Yi-Hao Peng (Carnegie Mellon University), Fu-Yin Cherng (National Chung Cheng University), Shang-Tse Chen (National Taiwan University)

Read More