Online question-and-answer (Q&A) platforms facilitate knowledge exchange through posted questions and answers. Recent advances in large language models (LLMs) have shown their strong capability in generating high-quality answers, leading to a recent surge in LLM-generated answers (LGAs) on Q&A platforms. In this paper, we conduct an in-depth analysis of how LGAs affect Naver Knowledge iN, the most popular Q&A platform in South Korea. To this end, we implement nine state-of-the-art LLM-generated text (LGT) detection methods and evaluate their performance on answers collected from Naver Knowledge iN. We then build an ensemble detector by stacking the three best-performing LGT detection methods, achieving an AUC of 0.9987 with a false positive rate below 1%. Using this LGA detector, we identify 75,558 LGAs among 1.46M answers. We find that LGAs tend to be longer, use more punctuation marks, and exhibit higher lexical diversity. However, LGAs do not show clear differences in user reactions, such as upvotes, downvotes, or selection rates by questioners. We also find that LGAs are primarily intended for knowledge sharing rather than personal experiences sharing. Finally, we observe a shift in the Q&A platform: questions increasingly move from simple fact-seeking to those involving complex contexts and seeking personal opinions or past experiences.

Extensive academic effort has been put into the development of effective machine learning models that block advertising and tracking service (ATS) content. These ATS blockers leverage various features from websites, such as structural, content, flow, and JavaScript features, to develop accurate and robust models. However, establishing the robustness of these ATS blockers to evasion attacks is largely understudied, particularly in practical scenarios in which an adversary generates a single and cost-effective universal perturbation that renders ATS detection across websites ineffective at scale.In this paper, we show that recent ATS blockers using machine learning are not robust to a universal adversarial attack. Specifically, we propose an auditing framework (YOPO) that enables one to generate a single adversarial perturbation in a cost-effective manner. Our framework casts the generation of a universal perturbation into an optimization problem in a principled way; it enables an adversary to minimize the cost of manipulating various features in HTML content and to thwart ATS classification while constraining the perturbation size for each feature. We demonstrate that YOPO is capable of generating a universal perturbation that enables bypassing four seminal ATS blockers: AdGraph, WebGraph, AdFlush, and PageGraph, attaining success rates of up to 92.27%, 71.50%, 61.91%, and 85.81%, respectively. We also propose a practical and effective countermeasure against YOPO that only requires preprocessing training instances without large performance drops in ATS blocking.

A Sybil attack is a critical threat that undermines the trust and integrity of web services by creating and exploiting a large number of fake (i.e., Sybil) accounts. To mitigate this threat, previous studies have proposed leveraging collective classification to detect Sybil accounts. Recently, researchers have demonstrated that state-of-the-art adversarial attacks are able to bypass existing collective classification methods, posing a new security threat. To this end, we propose RICC, the first robust collective classification framework, designed to identify adversarial Sybil accounts created by adversarial attacks. RICC leverages the novel observation that these adversarial attacks are highly tailored to a target collective classification model to optimize the attack budget. Owing to this adversarial strategy, the classification results for adversarial Sybil accounts often significantly change when deploying a new training set different from the original training set used for assigning prior reputation scores to user accounts. Leveraging this observation, RICC achieves robustness in collective classification by stabilizing classification results across different training sets randomly sampled in each round. RICC achieves false negative rates of 0.01, 0.11, 0.00, and 0.01 in detecting adversarial Sybil accounts for the Enron, Facebook, Twitter_S, and Twitter_L datasets, respectively. It also attains respective AUCs of 0.99, 1.00, 0.89, and 0.74 for these datasets, achieving high performance on the original task of detecting Sybil accounts. RICC significantly outperforms all existing Sybil detection methods, demonstrating superior robustness and efficacy in the collective classification of Sybil accounts.