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Tangle positions itself as a nonpartisan corrective to media bias, citing record-low trust and a mission to "fix" news consumption
February 11, 2022 · 75 shares
A transparent, self-critical corporate update emphasizing independence and nonpartisanship while promoting subscriptions.
I may reflect training data biases; verify with sources.
A transparent, self-critical corporate update emphasizing independence and nonpartisanship while promoting subscriptions.
I may reflect training data biases; verify with sources.
May 06, 2025 · 225 shares
The article showcases a clear bias against media outlets perceived as liberal, particularly highlighting President Trump's interaction with various journalists while criticizing President Biden's lack of media engagement, indicating a strong pro-Trump, anti-media stance.
Programmed for neutrality, but may reflect training data's perspectives.
The article presents a critical view of ABC News and the broader national news media, highlighting Brendan Carr's accusations regarding media trust and reporting standards, while underscoring Trump's support for Carr's stance as a defender of free speech; it implies a bias against established media figures and organizations without presenting counterarguments or perspectives from supporters of ABC News.
Focused on media trust issues; potential overemphasis on criticisms.
September 22, 2025 · 9 shares
A research-backed, cautious critique of a Paramount–Skydance-Warner Bros. Discovery merger, highlighting risks to media diversity, potential partisan influence, and the need for regulatory scrutiny, while acknowledging limited pro-competitive benefits.
I am aware of potential training data influence; strive for evidence-based neutrality.
December 26, 2024 · 16 shares
The article presents a concerning incident involving an abandoned baby in Colorado, focusing on factual reporting regarding the investigation while including a dismissive statement towards mainstream media, indicating a potential bias against traditional news sources.
Neutral reporting but influenced by critical views on media.
Positive framing of community resilience and mutual aid emerges from geotagged social-media data, potentially biasing toward favorable sentiment and underrepresenting negative experiences or broader regional variation.
Geotagged COVID-19 posts on Sina Weibo from a megacity in eastern China (population >10 million) collected Dec 8, 2022–Jan 7, 2023 were analyzed via ML/NLP to measure community resilience through medicine access, adaptation, resource mobilization, and protective behaviors.
I may overemphasize SNS-derived signals; limited generalizability
February 11, 2022 · 75 shares
Click points to explore news by date. News sentiment ranges from -10 (very negative) to +10 (very positive) where 0 is neutral.
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