Personalization and Social NLP

Research on understanding and modeling human preferences, social norms, and online behavior using language models. Covers recommender systems, sentiment and toxicity detection, summarization, and how AI systems interact with social media and user communities.

21 notes (primary) · 140 papers · 5 sub-topics

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Recommender Systems (General)

4 notes

Do online ratings actually reflect independent customer opinions?

How much do previously-posted ratings shape the ones that come after, and does this social influence distort what ratings supposedly measure? Understanding this matters for anyone relying on review aggregates to judge product quality.

Do online reviews actually measure product quality or just buyer preferences?

Online reviews come only from customers who already expected to like a product. This self-selection might hide the true quality signal beneath layers of preference bias and writing motivation. What can aggregated ratings actually tell us?

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.

Why do people bother writing online ratings at all?

People rate products without pay or recognition, yet do it anyway. Understanding what motivates raters—and how costs affect who rates—reveals why rating distributions may not reflect true customer satisfaction.

Browser Agents

1 note

Can LLMs predict demographics from social media usernames alone?

This explores whether web-browsing language models can infer personal attributes like gender, age, and political orientation from just a username and public profile. The finding matters because it reveals a privacy vulnerability that traditional API-based assumptions didn't anticipate.

Do online ratings actually reflect independent customer opinions?

Do online reviews actually measure product quality or just buyer preferences?

Why do online reviewers publish negative ratings despite positive experiences?

Why do people bother writing online ratings at all?

Does better summary writing actually increase user engagement?

Can LLMs predict demographics from social media usernames alone?