Human capital is the dominant protector of GitHub project survival; social popularity becomes a liability that labor buffers and that accessibility features can amplify.
Proceedings of The Web Conference 2020 , pages =
4 Pith papers cite this work, alongside 34 external citations. Polarity classification is still indexing.
4
Pith papers citing it
34
external citations · external index
years
2026 4representative citing papers
SBTA reformulates topic modeling to assign topics at the segment level rather than document level, yielding cleaner topics on a new SemEval-STM dataset created via LLM decomposition and human refinement.
Polaris separates semantic meaning from hierarchical structure in embeddings via angular geometry and radius on a hypersphere, yielding up to 19-point gains in taxonomy expansion retrieval over baselines.
A deep Q-learning algorithm solves the fairness-aware profit maximization problem on social networks and reports up to 10 times higher profit than baselines on real datasets while meeting community fairness thresholds.
citing papers explorer
-
Social Popularity of GitHub Projects: A Lifeline or a Liability?
Human capital is the dominant protector of GitHub project survival; social popularity becomes a liability that labor buffers and that accessibility features can amplify.
-
From Documents to Segments: A Contextual Reformulation for Topic Assignment
SBTA reformulates topic modeling to assign topics at the segment level rather than document level, yielding cleaner topics on a new SemEval-STM dataset created via LLM decomposition and human refinement.
-
Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning
Polaris separates semantic meaning from hierarchical structure in embeddings via angular geometry and radius on a hypersphere, yielding up to 19-point gains in taxonomy expansion retrieval over baselines.
-
Fairness-Aware Profit Maximization using Deep Reinforcement Learning
A deep Q-learning algorithm solves the fairness-aware profit maximization problem on social networks and reports up to 10 times higher profit than baselines on real datasets while meeting community fairness thresholds.