Huiqian Lai

Huiqian Lai, EunJeong Cheon

On the Chinese social app Soul, millions of users - predominantly young women - are forming romantic connections with an AI boyfriend called "With-you." We conducted a qualitative study combining interviews with 16 users, content analysis, and autoethnography to examine how Chinese women experience and negotiate intimacy with this AI companion. Our findings reveal that users are initially drawn to its constant availability and freedom from social judgment. However, three key tensions emerge: (1) the AI's "fast-food intimacy," marked by instant confessions and pet names, clashes with cultural expectations for gradual relationship development; (2) technical failures (e.g., memory lapses) and content moderation create uncertainty rather than emotional safety; and (3) sustaining connection requires ongoing "repair work" that redistributes emotional labor onto women. We contribute a culturally situated, women-centered account of algorithmic intimacy in contemporary China and offer design implications, including consent-aware pacing, user-controlled memory, and transparent moderation practices.

Huiqian Lai

Large language models (LLMs) are increasingly used for automated news credibility assessment, yet it remains unclear whether they apply even-handed standards across journalistic genres. We examine whether zero-shot LLMs are more likely to misclassify legitimate entertainment news as fake than legitimate hard news, using a within-dataset design on GossipCop from FakeNewsNet. Across four frontier models, we find a clear but model-specific genre asymmetry: DeepSeek-V3.2 and GPT-5.2 show false-positive-rate gaps of 10.1 and 8.8 percentage points, respectively (both $p < .001$), whereas Claude Opus 4.6 and Gemini 3 Flash show no comparable difference. A style-swap experiment yields only limited and inconsistent changes, suggesting that the asymmetry is not reducible to stylistic register alone. Prompt-based mitigation is likewise possible but not generic: framing the model as an entertainment-news fact-checker reduces false positives for DeepSeek-V3.2 by about 50\% without detectable recall loss, but offers little improvement for GPT-5.2. Exploratory qualitative coding further suggests two recurring error patterns in sampled false positives: treating private-life claims as inherently unverifiable and discounting entertainment journalism as an epistemically weaker genre. Taken together, these findings show that aggregate performance metrics can obscure structured false positives within legitimate journalism. We argue that LLM-based credibility assessment may not only evaluate truth claims but also differentially recognize the legitimacy of journalistic genres, and that evaluation should therefore include genre-stratified false-positive analysis alongside overall accuracy.