Recommender Systems Language Understanding and Pragmatics

Why do online reviewers publish negative ratings despite positive experiences?

When people post reviews publicly, do they adjust their honest opinions to seem more discerning? Schlosser's experiments test whether audience awareness shifts how people rate products compared to private ratings.

Note · 2026-05-03 · sourced from Recommenders General
How do people build trust with conversational AI? How do recommendation feeds shape what people see and believe?

Reviewing online is communication to a multiple audience — people who liked the product and people who didn't, simultaneously. Schlosser's experiments isolate a self-presentational mechanism that distinguishes posting from private rating. After reading a negative review, posters lower their public rating relative to the no-review and positive-review conditions, even when their personal experience with the product was favorable. Lurkers, who rate privately, show no such effect.

The mechanism: negative evaluators are perceived as more intelligent, competent, and expert than positive evaluators (Amabile 1983). Reading a negative review primes posters to worry their own positive opinion will look indiscriminate or low-standards, so they hedge downward to seem more discerning. The effect is asymmetric — positive reviews don't trigger the equivalent worry because positive reviewers don't carry the intelligence-signaling effect.

Posters also acknowledge multiple sides of the issue more than lurkers do, but they do not integrate the sides — they hold them as parallel parallel claims rather than synthesizing them. Lurkers, freer of social pressure, are more likely to integrate multiple viewpoints into a single coherent judgment.

This contradicts cognitive-tuning research (which predicts polarization toward attitudes), Grice's cooperative-principle maxims (which posters violate by suppressing their genuine positive experience), and the assumption that anticipated social interaction prevents negativity bias. The findings are specifically about multiple-audience public communication. The implication for recommender systems: aggregated ratings in multi-audience platforms systematically understate true average satisfaction for products that received any negative review, because every subsequent positive-experiencer adjusts their rating downward in public.


Source: Recommenders General

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Original note title

posters publish negativity-biased reviews in multiple-audience contexts even when private experience was positive