# Revisiting Information Cascades in Online Social Networks

arXiv: https://arxiv.org/abs/2208.00904 · HTML: https://mlsanity.com/paper/2208.00904/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeepInf: Social Influence Prediction with Deep Learning](https://mlsanity.com/paper/1807.05560/) (2018)
- [SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity](https://mlsanity.com/paper/1506.02594/) (2015)
- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014)
- [Feature Driven and Point Process Approaches for Popularity Prediction](https://mlsanity.com/paper/1608.04862/) (2016)
- [Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models](https://mlsanity.com/paper/1910.05451/) (2019)
- [Do Diffusion Protocols Govern Cascade Growth?](https://mlsanity.com/paper/1805.07368/) (2018)
- [Cascade-LSTM: Predicting Information Cascades using Deep Neural Networks](https://mlsanity.com/paper/2004.12373/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning QoE from Packet-Level Measurements in Encrypted Video Conferencing Traffic](https://mlsanity.com/paper/2601.06862/) (2026)

## Abstract (arXiv)

It's by now folklore that to understand the activity pattern of a user in an online social network (OSN) platform, one needs to look at his friends or the ones he follows. The common perception is that these friends exert influence on the user, effecting his decision whether to re-share content or not. Hinging upon this intuition, a variety of models were developed to predict how information propagates in OSN, similar to the way infection spreads in the population. In this paper, we revisit this world view and arrive at new conclusions. Given a set of users V, we study the task of predicting whether a user u ∈ V will re-share content by some v ∈ V at the following time window given the activity of all the users in V in the previous time window. We design several algorithms for this task, ranging from a simple greedy algorithm that only learns u's conditional probability distribution, ignoring the rest of V, to a convolutional neural network-based algorithm that receives the activity of all of V, but does not receive explicitly the social link structure. We tested our algorithms on four datasets that we collected from Twitter, each revolving around a different popular topic in 2020. The best performance, average F1-score of 0.86 over the four datasets, was achieved by the convolutional neural network. The simple, social-link ignorant, algorithm achieved an average F1-score of 0.78.