How do MSN algorithms prioritize news stories for users?
Executive summary
MSN shifted from human editors toward algorithmic and AI-driven curation, creating a personalized feed that aggregates content from many mainstream outlets and has been criticized for promoting low-quality or erroneous items; critics note examples of false or offensive headlines and AI-generated polls, while defenders cite broad sourcing and personalization [1] [2] [3] [4]. Independent audits of source mix say MSN pulls mostly Left-Center and Least Biased sources in one analysis (51% Left-Center, 31% Least Biased), though that assessment ties back to which publishers MSN indexes rather than an explicit documented ranking formula [4].
1. How MSN says it builds a feed — personalization and aggregation
MSN functions as a portal and news aggregator that assembles content from hundreds of partner publishers and presents a “personalized feed” to users rather than a single newsroom’s agenda; Microsoft moved toward algorithmic personalization in 2020 and markets the product as tailored content from thousands of trusted sources [5] [1] [6]. Available sources do not publish a detailed, proprietary ranking formula for which signals (e.g., recency, relevance, click history, publisher reputation) are weighted most heavily in MSN’s live product (not found in current reporting).
2. What independent analysts say about source mix and bias
Media Bias/Fact Check’s analysis of MSN treated it as an aggregator and measured the political leaning of the outlets MSN surfaces: it found roughly 51% of stories came from Left-Center sources and 31% from Least Biased sources, with much smaller shares from Right-Center and Right sources — a conclusion framed as a byproduct of source selection rather than an explicit partisan intent by MSN [4]. That study highlights an important distinction: an aggregator’s tilt can reflect the publisher pool it indexes and the wider media ecosystem rather than an explicit algorithmic preference [4].
3. Documented failures: algorithmic curation picking problematic content
Journalists have documented concrete cases where MSN’s automated system elevated false, misleading or cruelly worded items — for example, headlines picked by MSN’s AI that falsely suggested a public figure nodded off during a memorial and an obituary that labeled a player “useless,” which human editors likely would have caught, according to reporting in The Verge summarizing a CNN investigation [2]. Those examples underpin critiques that reliance on algorithmic selection — without adequate human oversight — can prioritize salience or novelty over accuracy and sensitivity [2].
4. Critiques that go beyond misinformation: AI-generated “trash” and poll errors
Beyond headline selection, critics argue Microsoft’s use of AI has produced poorly researched or AI-generated articles, strange automated polls, and other low-quality content that the portal then amplified — Firstpost and other outlets accused Microsoft News of “filling the internet” with AI-generated trash and flagged an inappropriate AI poll beside a Guardian-syndicated article as an example [3]. These critiques suggest algorithmic scale can magnify mistakes: when the system automates production or curation, errors propagate quickly across the portal’s large audience [3].
5. User-facing monitoring and moderation complaints
Users and community forums show friction with automated moderation and comment-monitoring systems; Microsoft support threads and Q&A posts include complaints that algorithms block or flag user comments incorrectly and that there’s limited recourse for restoring content, indicating moderation and content policing are also algorithmically driven and contentious [7] [8]. Those user reports demonstrate that algorithmic governance affects not just front-page selection but also downstream engagement and perceived fairness [7] [8].
6. Competing perspectives and limitations in the reporting
Microsoft’s stated intent — to scale personalized news delivery using AI and to aggregate “trusted” publishers — competes with journalistic investigations that focus on specific failures and quality problems; PCMag noted the cost-cutting rationale for replacing human curators with AI, while critics point to concrete missteps and quality issues [1] [2]. Crucially, none of the provided sources disclose MSN’s exact ranking algorithm, weights, or internal quality-control thresholds, so technical claims about precise ranking mechanics are not verifiable from current reporting (not found in current reporting).
7. What to watch next — transparency, audits and human oversight
The debate centers on whether Microsoft will increase transparency, restore more human editorial review, or refine its AI signals to reduce false or offensive pickups; reporting to date recommends stronger human oversight after documented errors and independent analyses of source mixes that reveal potential systematic effects [2] [4]. Available sources do not report any recent public audit or a full technical disclosure of MSN’s ranking model, so monitoring Microsoft statements and follow-up investigative reporting is the best path to learn if the balance shifts (not found in current reporting).