Predicting the popularity of tweets using internal and external knowledge: an empirical Bayes type approach

Tan, Wai Hong; Chen, Feng

doi:10.1007/s10182-021-00390-z

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Within this group, we can further categorize the approaches into generative or discriminative models. Point process based generative models, such as (Shen et al 2014;Zhao et al 2015;Chen and Tan 2018;Tan and Chen 2021;Zhang, Aravamudan, and Anagnostopoulos 2022), model a cascade's information diffusion process first by specifying intensity functions of the process. Then, the conditional mean or median of the counting process is typically adopted as an estimate for cascade size predictions.…”

Section: Related Workmentioning

confidence: 99%

Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods

Aravamudan

Anagnostopoulos

2023

AAAI

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Predicting user engagement -- whether a user will engage in a given information cascade -- is an important problem in the context of social media, as it is useful to online marketing and misinformation mitigation just to name a couple major applications. Based on split population multi-variate survival processes, we develop a discriminative approach that, unlike prior works, leads to a single model for predicting whether individual users of an information network will engage a given cascade for arbitrary forecast horizons and observation periods. Being probabilistic in nature, this model retains the interpretability of its generative counterpart and renders count prediction intervals in a disciplined manner. Our results indicate that our model is highly competitive, if not superior, to current approaches, when compared over varying observed cascade histories and forecast horizons.

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Section: Related Workmentioning

confidence: 99%

Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods

Aravamudan

Anagnostopoulos

2023

AAAI

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“…The advent of generative methods further solves these problems [9,10]. The techniques usually use Poisson or Hawkes process [11,12] to model the information cascade to enhance interpretability and robustness. However, this method cannot fully utilize the hidden information, so the prediction effect is not ideal.…”

Section: Introductionmentioning

confidence: 99%

Dynamic End-to-End Information Cascade Prediction Based on Neural Networks and Snapshot Capture

Han

Meng

2023

Electronics

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Knowing how to effectively predict the scale of future information cascades based on the historical trajectory of information dissemination has become an important topic. It is significant for public opinion guidance; advertising; and hotspot recommendation. Deep learning technology has become a research hotspot in popularity prediction, but for complex social platform data, existing methods are challenging to utilize cascade information effectively. This paper proposes a novel end-to-end deep learning network CAC-G with cascade attention convolution (CAC). This model can stress the global information when learning node information and reducing errors caused by information loss. Moreover, a novel Dynamic routing-AT aggregation method is investigated and applied to aggregate node information to generate a representation of cascade snapshots. Then, the gated recurrent unit (GRU) is employed to learn temporal information. This study’s validity and generalization ability are verified in the experiments by applying CAC-G on two public datasets where CAC-G is better than the existing baseline methods.

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What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes

Calderon,

Rizoiu

2024

Lecture Notes in Computer Science

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Predicting the popularity of tweets using internal and external knowledge: an empirical Bayes type approach

Cited by 4 publications

References 17 publications

Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods

Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods

Dynamic End-to-End Information Cascade Prediction Based on Neural Networks and Snapshot Capture

What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes

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