Human Feedback Types
missingNone explicit
No explicit feedback protocol extracted.
"The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors."
HFEPX · Eval paper review
Sarmad Chandio, Rishab Nithyanand
Published
Mar 9, 2026
Citations
0
Trust level
Low
Usefulness score
0/100 (Low)
Extraction confidence
15% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Mar 9, 2026
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this as background context only. Do not make protocol decisions from this page alone.
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
Best use
Background context only
Use if you need
Background context only.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This paper looks adjacent to evaluation work, but not like a strong protocol reference.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors. While prior work has largely focused on user behavior and algorithmic recommendations, the interplay between what is produced and what gets consumed, and its role in ideological shifts remains understudied. In this paper, we present a longitudinal, mixed-methods analysis combining one year of YouTube watch history with two waves of ideological surveys from 1,100 U.S. participants. We identify users who exhibited significant shifts toward more extreme ideologies and compare their content consumption and the production patterns of YouTube channels they engaged with to ideologically stable users. Our findings show that users who became more extreme consumed have different consumption habits from those who do not. This gets amplified by the fact that channels favored by users with extreme ideologies also have a higher affinity to produce content with a higher anger, grievance and other such markers. Lastly, using time series analysis, we examine whether content producers are the primary drivers of consumption behavior or merely responding to user demand.
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
None explicit
No explicit feedback protocol extracted.
"The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors."
None explicit
Validate eval design from full paper text.
"The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors."
Not reported
No explicit QC controls found.
"The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors."
Not extracted
No benchmark anchors detected.
"The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors."
Not extracted
No metric anchors detected.
"The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
No explicit human feedback protocol detected.
Evaluation mode is explicit
No clear evaluation mode extracted.
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
Metric reporting is present
No metric terms extracted.