Predicting the popularity of social media content in real time requires approaches that efficiently operate at global scale. Popularity prediction is important for many applications, including detection of harmful viral content to enable timely content moderation. The prediction task is difficult because views result from interactions between user interests, content features, resharing, feed ranking, and network structure. We consider the problem of accurately predicting popularity both at any given prediction time since a content item's creation and for arbitrary time horizons into the future. In order to achieve high accuracy for different prediction time horizons, it is essential for models to use static features (of content and user) as well as observed popularity growth up to prediction time. We propose a feature-based approach based on a self-excited Hawkes point process model, which involves prediction of the content's popularity at one or more reference horizons in tandem with a point predictor of an effective growth parameter that reflects the timescale of popularity growth. This results in a highly scalable method for popularity prediction over arbitrary prediction time horizons that also achieves a high degree of accuracy, compared to several leading baselines, on a dataset of public page content on Facebook over a two-month period, covering billions of content views and hundreds of thousands of distinct content items. The model has shown competitive prediction accuracy against a strong baseline that consists of separately trained models for specific prediction time horizons.

MOTIVATION: Target-specific hybridization depends on oligo-probe characteristics that improve hybridization specificity and minimize genome-wide cross-hybridization. Interplay between specific hybridization and genome-wide cross-hybridization has been insufficiently studied, despite its crucial role in efficient probe design and in data analysis. RESULTS: In this study, we defined hybridization specificity as a ratio between oligo target-specific hybridization and oligo genome-wide cross-hybridization. A microarray database, derived from the Genomic Comparison Hybridization (GCH) experiment and performed using the Affymetrix platform, contains two different types of probes. The first type of oligo-probes does not have a specific target on the genome and their hybridization signals are derived from genome-wide cross-hybridization alone. The second type includes oligonucleotides that have a specific target on the genomic DNA and their signals are derived from specific and cross-hybridization components combined together in a total signal. A comparative analysis of hybridization specificity of oligo-probes, as well as their nucleotide sequences and thermodynamic features was performed on the database. The comparison has revealed that hybridization specificity was negatively affected by low stability of the fully-paired oligo-target duplex, stable probe self-folding, G-rich content, including GGG motifs, low sequence complexity and nucleotide composition symmetry. CONCLUSION: Filtering out the probes with defined 'negative' characteristics significantly increases specific hybridization and dramatically decreasing genome-wide cross-hybridization. Selected oligo-probes have two times higher hybridization specificity on average, compared to the probes that were filtered from the analysis by applying suggested cutoff thresholds to the described parameters. A new approach for efficient oligo-probe design is described in our study. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.