# Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter Trends arXiv: https://arxiv.org/abs/2503.00599 ยท HTML: https://mlsanity.com/paper/2503.00599/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 73 references that are in the map. - First posted on arXiv 1 Mar 2025. ## Builds on (most cited first, 26 of 26) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017) - [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018) - [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020) - [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018) - [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020) - [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018) - [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020) - [Understanding over-squashing and bottlenecks on graphs via curvature](https://mlsanity.com/paper/2111.14522/) (2021) - [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019) - [User Preference-aware Fake News Detection](https://mlsanity.com/paper/2104.12259/) (2021) - [A Large-Scale Database for Graph Representation Learning](https://mlsanity.com/paper/2011.07682/) (2020) - [NetLSD: Hearing the Shape of a Graph](https://mlsanity.com/paper/1805.10712/) (2018) - [Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling](https://mlsanity.com/paper/1910.11436/) (2019) - [Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web](https://mlsanity.com/paper/1801.09288/) (2018) - [Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications](https://mlsanity.com/paper/1805.11769/) (2018) - [Trend Alert: How a Cross-Platform Organization Manipulated Twitter Trends in the Indian General Election](https://mlsanity.com/paper/2104.13259/) (2021) - [Characterizing Retweet Bots: The Case of Black Market Accounts](https://mlsanity.com/paper/2112.02366/) (2021) - [RIP Twitter API: A eulogy to its vast research contributions](https://mlsanity.com/paper/2404.07340/) (2024) - [Analyzing Activity and Suspension Patterns of Twitter Bots Attacking Turkish Twitter Trends by a Longitudinal Dataset](https://mlsanity.com/paper/2304.07907/) (2023) - [Early Detection of Promoted Campaigns on Social Media](https://mlsanity.com/paper/1703.07518/) (2017) - [Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election](https://mlsanity.com/paper/2303.04620/) (2023) - [Towards Understanding Trends Manipulation in Pakistan Twitter](https://mlsanity.com/paper/2109.14872/) (2021) - [Information Operations in Turkey: Manufacturing Resilience with Free Twitter Accounts](https://mlsanity.com/paper/2110.08976/) (2021) - [#TeamFollowBack: Detection & Analysis of Follow Back Accounts on Social Media](https://mlsanity.com/paper/2403.15856/) (2024) ## Built on by (most cited first, 1 of 1) - [Density-aware Walks for Coordinated Campaign Detection](https://mlsanity.com/paper/2506.13912/) (2025) ## Abstract (arXiv) Social media users and inauthentic accounts, such as bots, may coordinate in promoting their topics. Such topics may give the impression that they are organically popular among the public, even though they are astroturfing campaigns that are centrally managed. It is challenging to predict if a topic is organic or a coordinated campaign due to the lack of reliable ground truth. In this paper, we create such ground truth by detecting the campaigns promoted by ephemeral astroturfing attacks. These attacks push any topic to Twitter's (X) trends list by employing bots that tweet in a coordinated manner in a short period and then immediately delete their tweets. We manually curate a dataset of organic Twitter trends. We then create engagement networks out of these datasets which can serve as a challenging testbed for graph classification task to distinguish between campaigns and organic trends. Engagement networks consist of users as nodes and engagements as edges (retweets, replies, and quotes) between users. We release the engagement networks for 179 campaigns and 135 non-campaigns, and also provide finer-grain labels to characterize the type of the campaigns and non-campaigns. Our dataset, LEN (Large Engagement Networks), is available in the URL below. In comparison to traditional graph classification datasets, which are small with tens of nodes and hundreds of edges at most, graphs in LEN are larger. The average graph in LEN has ~11K nodes and ~23K edges. We show that state-of-the-art GNN methods give only mediocre results for campaign vs. non-campaign and campaign type classification on LEN. LEN offers a unique and challenging playfield for the graph classification problem. We believe that LEN will help advance the frontiers of graph classification techniques on large networks and also provide an interesting use case in terms of distinguishing coordinated campaigns and organic trends.