Rumor Detection in Social Media Using Word Embedding and LSTM-Based Networks

Dhanraj, P; Harisankar, R; Yadav, Aakash Kumar

doi:https://doi.org/10.55041/ijcope.v2i5.036

Published on: May 2026

RUMOR DETECTION IN SOCIAL MEDIA USING WORD EMBEDDING AND LSTM-BASED NETWORKS

P Dhanraj R Harisankar Aakash Kumar Yadav

S. Veena

Department of CSE - Internet of Things & CSBS School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram-600089 India

DOI:https://doi.org/10.55041/ijcope.v2i5.036

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Abstract

Rumor detection means classifying social media posts from Facebook, Twitter or Instagram into True Rumor, False rumor, Non-Rumor, Unverified categories. The traditional machine learning models uses basic handcrafted features for the classification and predictions; it fails to capture the contextual and sequential nature of the news data. Hence these limitations of traditional models are studied in this system and an efficient deep learning approach is proposed which can understand the contextual meaning of the data for efficient rumor classification. In this approach, textual data is first preprocessed through cleaning, tokenization into dense vector representations using Word2Vec word embedding technique. The LSTM network model processes these sequences to capture the contextual dependencies within the text. The final word embedding data is passed through dense layer for multi-class classification. Experimental evaluation showcase that the proposed deep learning LSTM model achieves reliable performance in identifying the fake rumor data, particularly in early stages. This approach provides a scalable and efficient solution for real-time rumor detection without relying on external metadata.

Keywords— Rumor Detection, Word Embeddings, Long Short-Term Memory (LSTM) model, Word2Vec word embedding, Deep Learning, Text Classification, Social Media Analysis, Sequence Modeling, Tokenization.

How to Cite this Paper

Dhanraj, P., Harisankar, R. & Yadav, A. K. (2026). Rumor Detection in Social Media Using Word Embedding and LSTM-Based Networks. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.036

Dhanraj, P, et al.. "Rumor Detection in Social Media Using Word Embedding and LSTM-Based Networks." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.036.

Dhanraj, P,R Harisankar, and Aakash Yadav. "Rumor Detection in Social Media Using Word Embedding and LSTM-Based Networks." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.036.

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References

Shu, X. Tang, J. Xiao and Y. Chen, "Unverified Rumor Detection," in IEEE Access, vol.13, pp.75635-75647, 2025, doi:10.1109/ACCESS.2025.3564638.

FengYing, A. Din, L. HuiChao, M. Babar and S. Ahmad, "Decentralized Consensus in Robotic Swarm for Collective Collision and Avoidance," in IEEE Access, vol. 12, pp. 72143-72154, 2024, doi: 10.1109/ACCESS.2024.3402564.

Zhu, G. Wang, S. Li and X. Huang, "A Novel Rumor Detection Method Based on Non-Consecutive Semantic Features and Comment Stance," in IEEE Access, vol. 11, pp. 58016-58024, 2023, doi: 10.1109/ACCESS.2023.3284308.

Mahbub, E. Pardede and A. S. M. Kayes, "COVID-19 Rumor Detection Using Psycho Linguistic Features," in IEEE Access, vol. 10,117530-117543, 2022, doi: 10.1109/ACCESS.2022.3220369.

Luo, Q. Li and J. Zheng, "Deep Feature Fusion for Rumor Detection on Twitter," in IEEE Access, vol. 9, pp. 126065-126074, 2021, doi: 10.1109/ACCESS.2021.3111790.

Chen and W. Jiang, "SCDVit: Semantic Change Detection Based on Sam-Vit and Semantic Consistency," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 2269-2282, 2025, doi: 10.1109/JSTARS.2024.3514926.

Ning, Y. He, H. Zhang, R. Zhang, D. Chang and M. Hao, "Semantic Information Collaboration Network for Semantic Change Detection in Remote Sensing Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 12893-12909, 2024, doi: 10.1109/JSTARS.2024.3418632.

Wheeler and B. Natarajan, "Engineering Semantic Communication: A Survey," in IEEE Access, vol. 11, pp. 13965-13995, 2023, doi: 10.1109/ACCESS.2023.3243065.

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•Published on: May 04 2026

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