Novel Sentiment Majority Voting Classifier and Transfer Learning-Based Feature Engineering for Sentiment Analysis of Deepfake Tweets

Deepfake text known as synthetic text, involves using artificial intelligence (AI)-generated text to create fabricated information or imitate actual individuals. Twitter tweets related to deepfake can be used for many malicious intents, including impersonation, creating fake news, and spreading misi...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.67117-67129
Main Authors: Khalid, Madiha, Raza, Ali, Younas, Faizan, Rustam, Furqan, Villar, Monica Gracia, Ashraf, Imran, Akhtar, Adnan
Format: Article
Language:English
Subjects:
Artificial intelligence
Blogs
Classification
Classifiers
Data mining
Deception
Decision trees
Deep learning
Deepfake
deepfake tweets
Deepfakes
False information
feature engineering
Learning
Long short term memory
Machine learning
Natural language processing
Performance assessment
Reviews
Sentiment analysis
Social networking (online)
Transfer learning
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Deepfake text known as synthetic text, involves using artificial intelligence (AI)-generated text to create fabricated information or imitate actual individuals. Twitter tweets related to deepfake can be used for many malicious intents, including impersonation, creating fake news, and spreading misinformation. The main goal of this investigation is to detect people's sentiments related to deepfake technology with an advanced technique. A novel sentiment majority voting classifier (SMVC) is proposed for the sentiment labeling of collected tweets. The proposed SMVC selects the final sentiment from three lexicon-based models TextBlob, valence-aware dictionary and sentiment reasoner (VADER), and AFINN using a majority voting mechanism. For sentiment classification, we propose a novel transfer feature where embedding features are fed to a long short-term memory (LSTM), and decision tree (DT) models and outputs are combined into a single feature set. Extensive experiments show that transfer learning-based feature engineering results in the highest performance. The logistic regression outperforms with the highest accuracy of 98.9% with minimum computational complexity. The sentiment classification performance of each applied model is validated using the k-fold cross-validations. Moreover, performance assessment with existing state-of-the-art models is also carried out to show the robustness of the proposed technique.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3398582