Cross-Platform Posting in Academic Libraries: A Comparative Dataset of Facebook and Instagram Interactions

This dataset provides a detailed evaluation of cross-posting strategies implemented by academic libraries worldwide. By pairing social media posts across Facebook and Instagram, the framework enables direct, comparative measurement of user engagement. Employing a computational methodology, the study started with an initial dataset of 8,364 Instagram and 9,524 Facebook observations. Cross-posted content was rigorously identified via the Levenshtein distance algorithm (Yujian & Bo, 2007), yielding 3,379 distinct cross-platform post pairs. The core aim is to explain the impact of posting latency on engagement differentials between platforms, hence advancing research in social media optimization (SMO) for academic libraries. The analytical workflow and data management were executed within the Google Colaboratory (Colab) cloud-based environment, utilising open-source Python libraries. Data curation was handled using the Pandas library for CSV manipulation, error handling, and initial diagnostic statistics. The core analysis focuses on two derived metrics: the Row-Index-Difference, which serves as a proxy for the sequential posting lag, and interaction_diff, which represents the performance gap calculated by subtracting the Facebook interaction rate from the Instagram interaction rate. Consequently, positive values indicate high performance on Instagram, while negative values denote higher engagement on Facebook.