Modeling and predicting behavioral dynamics on the web

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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “For instance, there are variations on when and for how long users focus on certain topics that may be triggered by current trends, seasonality or periodicity in activities, or even noise (Radinsky et al., 2012).”
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019, background): “The first category of statistics baselines includes: STRX is the structural time series model with exogenous variables (Scott & Varian, 2014; Radinsky et al., 2012).”
News events prediction using Markov logic networks (2018, background): “While some related works focused on predicting news importance (news impact/volume) [6–8] and news interests (news filtering/recommendation) [9–11], other works dealt with predicting dynamic content on the web, often using time-series [12] and state-space models [13].”
Predicting the popularity of web 2.0 items based on user comments (2014, background): “Note that although the first two baselines are heuristic and simple, they do produce reasonable results for short-term popularity prediction, thus forming competitive baselines (see [29]).”