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PyChemFlow is a Python library for automated, reproducible data pre-processing that leverages transformer objects built on minimal dependencies. It enables one-line execution with train-validation splits, persistent storage of transformers and metadata, and customizable data manipulation steps, making it ideal for applications requiring strict reproducibility.

This study examines how language in YouTube comments changes after videos are shared on Reddit’s conspiracy forum. Analyzing 859 videos and 180,000+ comments, we found YouTube comments adopt linguistic patterns similar to r/conspiracy after Reddit exposure, evidenced by reduced text distribution distances. While sentiment remains unchanged, vocabulary measurably shifts toward conspiracy forum discourse, demonstrating cross-platform influence on user language.

This study explores using regular equivalence in trust networks to improve Collaborative Filtering recommendations, particularly for cold-start users. While traditional CF suffers from data sparsity when users rate few items, incorporating trust relationships (explicit or implicit) can enhance recommendations. The research applies an iterative regular equivalence calculation to generate similarity matrices for neighbor selection, examining how both strong and weak network ties affect recommendation quality. Evaluations on Epinions data demonstrate that incorporating weak ties alongside strong ties significantly improves recommendation accuracy for cold-start users in trust-based recommender systems.