Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence

• URL: http://arxiv.org/abs/2301.06774v2
• Date: Thu, 9 May 2024 10:15:28 GMT
• Title: Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence
• Authors: Serena Tardelli, Leonardo Nizzoli, Maurizio Tesconi, Mauro Conti, Preslav Nakov, Giovanni Da San Martino, Stefano Cresci,
• Abstract summary: Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants. Here, we carry out the first dynamic analysis of coordinated behavior. Thanks to our novel approach we find that: (i) coordinated communities feature variable degrees of temporal instability; (ii) dynamic analyses are needed to account for such instability; and results of static analyses can be unreliable and scarcely representative of unstable communities.
• Score: 43.555910858821576
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out the first dynamic analysis of coordinated behavior. To reach our goal we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. Thanks to our novel approach we find that: (i) coordinated communities feature variable degrees of temporal instability; (ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; (iii) some users exhibit distinct archetypal behaviors that have important practical implications; (iv) content and network characteristics contribute to explaining why users leave and join coordinated communities. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of coordinated communities, and on the patterns of online influence.

Related papers

• LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight [1.1119672724275114]
  Emotional coordination is a core property of human interaction that shapes how meaning is constructed in real time.<n>We introduce a probabilistic framework that characterizes emotion not as a static label, but as a continuous latent probability distribution.<n>This work establishes a scalable and deployable pathway for understanding interpersonal dynamics, offering a generalizable solution.
  arXiv  Detail & Related papers  (2026-01-07T06:50:41Z)
• Detecting Coordinated Activities Through Temporal, Multiplex, and Collaborative Analysis [2.7415651415305597]
  coordinated campaigns are better characterized by evidence of similar temporal behavioral patterns.<n>We propose a framework to model complex coordination patterns across multiple online modalities.<n>Our results demonstrate that a multiplex time-aware model excels in the identification of coordinating groups.
  arXiv  Detail & Related papers  (2025-12-22T18:53:43Z)
• DynamiX: Large-Scale Dynamic Social Network Simulator [101.65679342680542]
  DynamiX is a novel large-scale social network simulator dedicated to dynamic social network modeling.<n>For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances.<n>For ordinary users, we construct an inequality-oriented behavior decision-making module.
  arXiv  Detail & Related papers  (2025-07-26T12:13:30Z)
• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• CauSkelNet: Causal Representation Learning for Human Behaviour Analysis [7.139285159330364]
  This study introduces a novel representation learning framework based on causal inference to address these challenges.<n>Our two-stage approach combines the Peter-Clark (PC) algorithm and Kullback-Leibler (KL) divergence to identify and quantify causal relationships between human joints.<n>By capturing joint interactions, the proposed causal Graph Convolutional Network (GCN) produces interpretable and robust representations.
  arXiv  Detail & Related papers  (2024-09-23T21:38:49Z)
• Detection and Characterization of Coordinated Online Behavior: A Survey [1.9651052909588413]
  This survey collects, categorizes, and critically discusses the body of work produced as a result of the growing interest on coordinated online behavior. We reconcile industry and academic definitions, propose a comprehensive framework to study coordinated online behavior, and review and critically discuss the existing detection and characterization methods.
  arXiv  Detail & Related papers  (2024-08-02T13:27:56Z)
• Behavior-Inspired Neural Networks for Relational Inference [3.7219180084857473]
  Recent works learn to categorize relationships between agents based on observations of their physical behavior. We introduce a level of abstraction between the observable behavior of agents and the latent categories that determine their behavior. We integrate the physical proximity of agents and their preferences in a nonlinear opinion dynamics model which provides a mechanism to identify mutually exclusive latent categories, predict an agent's evolution in time, and control an agent's physical behavior.
  arXiv  Detail & Related papers  (2024-06-20T21:36:54Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Parallel Reasoning Network for Human-Object Interaction Detection [53.422076419484945]
  We propose a new transformer-based method named Parallel Reasoning Network(PR-Net) PR-Net constructs two independent predictors for instance-level localization and relation-level understanding. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.
  arXiv  Detail & Related papers  (2023-01-09T17:00:34Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation [102.24108167002252]
  We propose a novel attention network, named self-modulating attention, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-30T03:54:11Z)
• An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments [0.0]
  This framework aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs) It can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment.
  arXiv  Detail & Related papers  (2021-06-29T17:50:54Z)
• Divide and Rule: Recurrent Partitioned Network for Dynamic Processes [25.855428321990328]
  Many dynamic processes are involved with interacting variables, from physical systems to sociological analysis. Our goal is to represent a system with a part-whole hierarchy and discover the implied dependencies among intra-system variables. The proposed architecture consists of (i) a perceptive module that extracts a hierarchical and temporally consistent representation of the observation at multiple levels, (ii) a deductive module for determining the relational connection between neurons at each level, and (iii) a statistical module that can predict the future by conditioning on the temporal distributional estimation.
  arXiv  Detail & Related papers  (2021-06-01T06:45:56Z)
• Learning Reactive and Predictive Differentiable Controllers for Switching Linear Dynamical Models [7.653542219337937]
  We present a framework for learning composite dynamical behaviors from expert demonstrations. We learn a switching linear dynamical model with contacts encoded in switching conditions as a close approximation of our system dynamics. We then use discrete-time LQR as the differentiable policy class for data-efficient learning of control to develop a control strategy.
  arXiv  Detail & Related papers  (2021-03-26T04:40:24Z)
• A General Method to Find Highly Coordinating Communities in Social Media through Inferred Interaction Links [13.264683014487376]
  Political misinformation, astroturfing and organised trolling are online malicious behaviours with significant real-world effects. We propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in various behaviours that, in concert, come to execute different goal-based strategies.
  arXiv  Detail & Related papers  (2021-03-05T00:48:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.