Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Calcium signaling mediates proliferation of the precursor cells that give rise to the ciliated left-right organizer in the zebrafish embryo.

Frontiers in molecular biosciences·2023

Same author

An Ultrathin Composite Polymer Electrolyte Dual-Reinforced by a Polymer of Intrinsic Microporosity (PIM-1) and Poly(tetrafluoroethylene) (PTFE) Porous Membrane.

Small (Weinheim an der Bergstrasse, Germany)·2023

Same author

YDD-SLAM: Indoor Dynamic Visual SLAM Fusing YOLOv5 with Depth Information.

Sensors (Basel, Switzerland)·2023

Same author

Association between transcutaneous oxygen saturation within 24 h of admission and mortality in critically ill patients with non-traumatic subarachnoid hemorrhage: a retrospective analysis of the MIMIC-IV database.

Frontiers in neurology·2023

Same author

Small-cell neuroendocrine carcinoma of the ovary with unusual uterine and rectal metastases.

Asian journal of surgery·2023

Same author

Sphingolipids: drivers of cardiac fibrosis and atrial fibrillation.

Journal of molecular medicine (Berlin, Germany)·2023

Search research articles

Related Experiment Video

Updated: May 17, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Rumor detection on social networks based on Temporal Tree Transformer.

Sirong Wu^1,2,3, Yuhui Deng^1,2,3, Junjie Liu⁴

¹Guangdong Provincial Key Laboratoryof Interdisciplinary Research and Application for Data Science, Beijing NormalUniversity-Hong Kong Baptist University United International College, Zhuhai,Guangdong Province, China.

|April 7, 2025

Summary

Detecting social media rumors is crucial. Our Temporal Tree Transformer model analyzes text, structure, and temporal changes for accurate rumor detection, outperforming existing methods.

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Related Experiment Videos

Last Updated: May 17, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Area of Science:

Social Computing
Artificial Intelligence
Information Science

Background:

Social media facilitates rapid rumor spread, causing societal issues.
Automated rumor detection is essential but current methods lack dynamic temporal analysis.
Existing approaches often ignore the evolving structure of rumor propagation.

Purpose of the Study:

To propose a novel model, Temporal Tree Transformer, for automated rumor detection.
To incorporate textual, structural, and temporal dynamics of rumor propagation.
To improve the generalization and accuracy of rumor detection systems.

Main Methods:

Utilized Gated Recurrent Unit (GRU) to encode temporal propagation tree structures.
Analyzed the growth of propagation trees across different time windows.
Employed Leave-One-Event-Out (LOEO) cross-validation for realistic evaluation.

Main Results:

Achieved state-of-the-art accuracy of 75.84% on the PHEME dataset.
Obtained a Macro F1 score of 71.98%.
Demonstrated improved model generalization by extracting temporal features.

Conclusions:

The Temporal Tree Transformer effectively captures dynamic features of rumor propagation.
Integrating temporal information significantly enhances rumor detection performance.
The proposed method offers a more robust approach for real-world rumor detection scenarios.