Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Grouping Content From Various Social Media Platforms Into Clusters

Authors
Reddi Prasadu1, Aadri Ganesh Kumar1, Barla Gayatri Devi1, *, Suragani Komali1, Barla Jai Chandu1
1Department of Information Technology, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India
*Corresponding author. Email: gayatridevibarla@gmail.com
Corresponding Author
Barla Gayatri Devi
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_217How to use a DOI?
Keywords
Social Media Clustering; Content Categorization; Deep Learning; Natural Language Processing (NLP); Machine Learning; Topic Modeling; CNN; MobileNet; Multi-platform Content Analysis; Feature Extraction; Automated Content Grouping; Artificial Intelligence in Social Media; Text and Image Fusion; Misinformation Detection; Cross-platform Data Mining; Unsupervised Learning
Abstract

Social media content is rapidly growing and varies across platforms, making it challenging to process and analyze manually. This study explores the effectiveness of two deep learning models—CNN and MobileNet—for clustering content from different social media sources into five distinct categories: news, personal updates, promotional content, health-related posts, and misinformation. By using labeled image representations of social media posts, the models learn visual and contextual patterns. Evaluation metrics include accuracy, precision, recall, and F1-score. Results show MobileNet achieves better efficiency and speed, while CNN captures more intricate content structures, offering a balanced approach to multi-platform content clustering.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_217How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Reddi Prasadu
AU  - Aadri Ganesh Kumar
AU  - Barla Gayatri Devi
AU  - Suragani Komali
AU  - Barla Jai Chandu
PY  - 2025
DA  - 2025/11/04
TI  - Grouping Content From Various Social Media Platforms Into Clusters
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2619
EP  - 2627
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_217
DO  - 10.2991/978-94-6463-858-5_217
ID  - Prasadu2025
ER  -