Multi-input BiLSTM deep learning model for social bot detection-Reference-Cited by-同舟云学术

Multi-input BiLSTM deep learning model for social bot detection

Published:2023-03-06 Issue: Volume: Page:
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Container-title:2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)
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Author:

Messai Aya¹,Hamida Zineb Ferhat¹,Drif Ahlem¹,Giordano Silvia²

Affiliation:

1. University of Ferhat Abbas Sétif 1,Networks and Distributed System Laboratory,Sétif,Algeria

2. University of Applied Sciences of Southern Switzerland,Department of Innovative Technologies,SUPSI,Lugano,Switzerland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10104518/10104524/10104646.pdf?arnumber=10104646

Reference23 articles.

1. A Social Bots Detection Model Based on Deep Learning Algorithm

2. Contrast Pattern-Based Classification for Bot Detection on Twitter

3. DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data

4. Fake News Detection Methods: A Survey and New Perspectives;ferhat hamida;International Conference on Advanced Intelligent Systems for Sustainable Development,2020

5. Detection of Bots in Social Media: A Systematic Review

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1. Effective Bot Detection in Twitter using Deep Boltzmann Machine;2024 10th International Conference on Web Research (ICWR);2024-04-24

2. A Hybrid Deep Learning Architecture for Social Media Bots Detection Based on BiGRU-LSTM and GloVe Word Embedding;IEEE Access;2024

3. A Systematic Review of Recent Research on Chatbots;2023 International Conference on Electrical, Communication and Computer Engineering (ICECCE);2023-12-30