social media platform operators

Abstract: Sensitive attributes such as age, income, and occupation can be inferred from user-generated content by aggregating indirect cues across many ordinary posts. LLM-based profilers can perform this aggregation automatically and with high accuracy, which makes large-scale personal attribute inference a major privacy threat. Existing LLM-based profilers, however, offer limited insight into which specific posts, concepts, and relationships made an inference possible, which is key to targeted privacy mitigation, i.e., redacting or rewriting only the few posts that actually leak an attribute, rather than perturbing entire histories. We introduce GraphProfiler, an auditable LLM-based profiler that represents each user's post history as a source-linked personal knowledge graph where nodes and edges trace back to the originating post and resolves attribute predictions to cited graph records and source texts. GraphProfiler reaches 86.7% attack success rate on the eight-attribute SynthPAI benchmark, within two points of strong text-only baselines, and 84.6% on PANDORA, while citing supporting evidence for over 98% of predictions. Our controlled ablation experiments provide evidence that the cited posts contribute to attack success, as removing them reduces the attack success rate substantially more than removing an equal number of random posts.

Abstract: Did people stop asking other people for advice online once generative AI could answer their questions? Prior work on ChatGPT's effect on online help-seeking disagrees in both size and sign, in part because no study has compared affected communities against similar communities that AI cannot easily substitute for, over the same months. In this paper, we track monthly post counts in 26 Reddit informational communities against 90 size-comparable hobby communities over the same six calendar months before and after the launch of ChatGPT. We also repeat the entire analysis at 66 earlier dates, before ChatGPT existed, to see what our method reports when no ChatGPT-effect exists. We find that informational help-seeking did not decline. Our results rule out any decline in posting larger than 3.4%, far smaller than the 8% to 25% declines documented in prior work. Steady post counts could still be misleading if AI-written posts had replaced human ones. We test this possibility by scoring 274,411 posts and 223,775 comments with AI-text detectors, compared in a way that cancels out detector false-positives on human-written text. AI-written posts rose only 2-3 percentage points more in informational communities than in hobby communities, short of the 5.1 points that would be needed to hide even the smallest decline previously reported for Reddit. In addition, the comments people receive show no such rise at all. Why, then, do published studies disagree? Reddit community types were already drifting apart before ChatGPT existed, at rates comparable to every published estimate, and without same-time controls, that drift can look like an effect of generative AI. Our own largest estimate, an 18% fall in posts to low-stakes curiosity communities, matches its pre-existing trend. Humans still ask humans for help, and, as far as detection can tell, humans still answer them.