Modeling and Predicting Retweeting Dynamics on Microblogging Platforms

Gao, Shuai; Ma, Jun; Chen, Zhumin

doi:10.1145/2684822.2685303

Cited by 120 publications

(81 citation statements)

References 26 publications

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“…Evaluation Metrics. We evaluate the various models by (1) selecting a test post u, (2) using each model to simulate a full cascade u * , and (3) comparing the similarity of the actual cascade of u and the u * generated by each model. Since u and u * are both trees, there exist several methods by which we can evaluate their topological distance.…”

Section: Methodsmentioning

confidence: 99%

Modelling Online Comment Threads from their Start

Krohn

Weninger

2019

2019 IEEE International Conference on Big Data (Big Data)

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The social Web is a widely used platform for online discussion. Across social media, users can start discussions by posting a topical image, url, or message. Upon seeing this initial post, other users may add their own comments to the post, or to another user's comment. The resulting online discourse produces a comment thread, which constitutes an enormous portion of modern online communication. Comment threads are often viewed as trees: nodes represent the post and its comments, while directed edges represent reply-to relationships. The goal of the present work is to predict the size and shape of these comment threads. Existing models do this by observing the first several comments and then fitting a predictive model. However, most comment threads are relatively small, and waiting for data to materialize runs counter to the goal of the prediction task. We therefore introduce the Comment Thread Prediction Model (CTPM) that accurately predicts the size and shape of a comment thread using only the text of the initial post, allowing for the prediction of new posts without observable comments. We find that the CTPM significantly outperforms existing models and competitive baselines on thousands of Reddit discussions from nine varied subreddits, particularly for new posts.

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Section: Methodsmentioning

confidence: 99%

Modelling Online Comment Threads from their Start

Krohn

Weninger

2019

2019 IEEE International Conference on Big Data (Big Data)

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“…The signature rate λ t is expressed as λ t = cf γ (t)r α (R t ), where c represents the attractiveness, f γ (t) ∝ t −γ (γ > 0) describes the aging, and the reinforcement function r α (R t )(α > 0) models the "rich gets richer" phenomenon. The parameters c, γ, α are determined by maximizing the likelihood function [11,20]. For each timestamp t s (x-axis) a predictor was trained using {s p (i)}, i < t s for all petitions p in the data set.…”

Section: Reinforced Poisson Process (Rpp)mentioning

confidence: 99%

Predicting the Success of Online Petitions Leveraging Multidimensional Time-Series

Proskurnia

Grabowicz

Kobayashi

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

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Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolution of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site-one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.

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“…Several works focused on analyzing and mining users behavior on social networks [21,4,8]. We are not aware of work specifically dedicated to the engagement is social network challenges; we therefore describe social engagement in other contexts.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Analyzing Engagement in Social Network Challenges

Brambilla

Ceri

Leonardi

et al. 2016

Web Information Systems Engineering – WISE 2016

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Abstract. Participation to challenges within social networks is a very effective instrument for promoting a brand or event. In this paper, we take the challenge organizer's perspective, and we study how to raise the engagement of players in challenges where the players are stimulated to create and evaluate content, thereby indirectly raising the awareness about the brand or event itself. We illustrate a comprehensive model of the actions and strategies that can be exploited for progressively boosting the social engagement during the challenge evolution. The model studies the organizer-driven management of interactions among players, and evaluates the effectiveness of each action in light of several other factors (time, repetition, third party actions, interplay between different social networks, and so on). We evaluate the model through a set of experiment upon a real case, the YourExpo2015 challenge. Overall, our experiments lasted 9 weeks and mobilized hundreds of thousands of users on two different social platforms; our quantitative analysis assesses the validity of the model.

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Modeling and Predicting Retweeting Dynamics on Microblogging Platforms

Cited by 120 publications

References 26 publications

Modelling Online Comment Threads from their Start

Modelling Online Comment Threads from their Start

Predicting the Success of Online Petitions Leveraging Multidimensional Time-Series

Modeling and Analyzing Engagement in Social Network Challenges

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