Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

413
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
413
Improving Translational Accuracy02:07

Improving Translational Accuracy

12.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
12.0K
Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K
Classification of Signals01:30

Classification of Signals

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Deep sequence learning with multi-task supervision for scalable population health monitoring.

Frontiers in public health·2026
Same author

Toxic and radioactive elements in construction marbles: enhanced detection using laser induced breakdown spectroscopy for safe living.

Environmental geochemistry and health·2026
Same author

A framework based on hybrid Suzuki-Abe and convex Hull approach for improved classification of skin lesions.

Scientific reports·2026
Same author

LwHM: lightweight hybrid classifier for SDN-attack detection using recursive feature elimination.

Scientific reports·2026
Same author

XAI-MedNet: A Next-Generation Explainable AI Framework for Contrast-Enhanced Skin Lesion Classification via Entropy-Controlled Optimization.

Bioengineering (Basel, Switzerland)·2026
Same author

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

The American journal of pathology·2026

Related Experiment Video

Updated: Oct 12, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.7K

Deepfake tweets classification using stacked Bi-LSTM and words embedding.

Vaibhav Rupapara1, Furqan Rustam2, Aashir Amaar2

  • 1School of Computing and Information Sciences, Florida International University, Florida, United States of America.

Peerj. Computer Science
|November 22, 2021
PubMed
Summary

This study introduces a deep learning model to detect sentiment in deep fake tweets. The stacked bi-directional long short-term memory (SBi-LSTM) network achieved 92% accuracy, outperforming other models in classifying fake media sentiment.

Keywords:
Deep learningDeepfakeDeepfake sentiment analysisMachine learningStacked Bi-LSTM

Related Experiment Videos

Last Updated: Oct 12, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.7K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • The proliferation of sophisticated digital manipulation tools has led to an increase in altered media, including deep fakes.
  • Deep fake content is prevalent on social media platforms, necessitating methods to analyze user reactions.
  • Understanding public sentiment towards deep fakes is crucial for addressing their societal impact.

Purpose of the Study:

  • To develop and evaluate a deep learning model for predicting the sentiment polarity of tweets containing deep fake content.
  • To compare the performance of the proposed model against traditional machine learning classifiers and other deep learning architectures.

Main Methods:

  • A stacked bi-directional long short-term memory (SBi-LSTM) network was proposed for sentiment classification.
  • Traditional machine learning classifiers (SVM, Logistic Regression, Gaussian Naive Bayes, Extra Tree, AdaBoost) were evaluated using TF-IDF and Bag-of-Words features.
  • Other deep learning models, including LSTM, GRU, Bi-LSTM, and CNN+LSTM, were analyzed for comparative performance.

Main Results:

  • The proposed SBi-LSTM model achieved a high accuracy of 0.92 in classifying the sentiment of deep fake tweets.
  • The SBi-LSTM model demonstrated superior performance compared to all investigated machine learning and other deep learning models.
  • The study validates the effectiveness of advanced deep learning architectures for analyzing sentiment in the context of synthetic media.

Conclusions:

  • The SBi-LSTM model offers a robust and accurate solution for detecting sentiment in deep fake tweets.
  • This research contributes to the ongoing efforts to combat the spread and impact of misinformation through synthetic media.
  • Further research can explore real-time detection and mitigation strategies for deep fake content on social platforms.