Exploring Author Profiling for Fake News Detection

Rathod, Shloak

doi:10.1109/compsac54236.2022.00256

Cited by 8 publications

(2 citation statements)

References 10 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LR achieved the highest accuracy in classifying fake news. Rathod et al [25] proposed a model for detecting fake news in online resources and social media platforms. The authors used Natural Language Processing (NLP) and ML techniques to classify news articles as fake and real news based on source authenticity.…”

Section: Algorithmsmentioning

confidence: 99%

Deep Dive into Fake News Detection: Feature-Centric Classification with Ensemble and Deep Learning Methods

Alarfaj,

Khan

2023

Algorithms

View full text Add to dashboard Cite

The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known “Fake News” dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naïve bayes (MNB), gaussian naïve bayes (GNB), Bernoulli naïve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem.

show abstract

Section: Algorithmsmentioning

confidence: 99%

Deep Dive into Fake News Detection: Feature-Centric Classification with Ensemble and Deep Learning Methods

Alarfaj,

Khan

2023

Algorithms

View full text Add to dashboard Cite

show abstract

“…All of these responsibilities are carried out by fictitious individuals. People, robots, and even cybernetic creatures are capable of creating false identities that can be identified via Artificial Neural Networks (ANNs) [4,5,6]. Computers are now in charge of maintaining "cyborg" accounts, despite the fact that humans were the ones who first established them.…”

Section: Introductionmentioning

confidence: 99%

TSMFPMSM: Design of a Temporal Sentiment analysis Model for identification of Fake Profiles from Multimodal Social Media interfaces

BHRUGUMALLA¹,

Mohanty²

2023

Preprint

View full text Add to dashboard Cite

The growing number of fake profiles on social media has made it necessary to develop intelligent models for identifying and removing them. This study proposes a novel approach for identifying fake profiles using temporal sentiment analysis. The model collects large datasets from various social media platforms, which are then processed using an ensemble sentiment analysis engine. This produces temporal sentiment patterns, which are used to train a 1D Convolutional Neural Network (1D CNN) for the efficient identification of fake profiles. The model is based on the theory that the data communicated by fake users always converges, and was observed to improve fake-profile detection accuracy by 5.9% with higher precision and recall compared to standard methods. This proposed model can be applied to multimodal social media interfaces.

show abstract

F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets

Aditya

Gnanajeyaraman

Hole

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Exploring Author Profiling for Fake News Detection

Cited by 8 publications

References 10 publications

Deep Dive into Fake News Detection: Feature-Centric Classification with Ensemble and Deep Learning Methods

Deep Dive into Fake News Detection: Feature-Centric Classification with Ensemble and Deep Learning Methods

TSMFPMSM: Design of a Temporal Sentiment analysis Model for identification of Fake Profiles from Multimodal Social Media interfaces

F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets

Contact Info

Product

Resources

About