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Spam detection in social media using convolutional and long short term memory neural network

Authors:

Manisha Sharma,

Basant AgarwalAuthors Info & Claims

Annals of Mathematics and Artificial Intelligence, Volume 85, Issue 1

Pages 21 - 44

https://doi.org/10.1007/s10472-018-9612-z

Published: 01 January 2019 Publication History

Abstract

As the use of the Internet is increasing, people are connected virtually using social media platforms such as text messages, Facebook, Twitter, etc. This has led to increase in the spread of unsolicited messages known as spam which is used for marketing, collecting personal information, or just to offend the people. Therefore, it is crucial to have a strong spam detection architecture that could prevent these types of messages. Spam detection in noisy platform such as Twitter is still a problem due to short text and high variability in the language used in social media. In this paper, we propose a novel deep learning architecture based on Convolutional Neural Network (CNN) and Long Short Term Neural Network (LSTM). The model is supported by introducing the semantic information in representation of the words with the help of knowledge-bases such as WordNet and ConceptNet. Use of these knowledge-bases improves the performance by providing better semantic vector representation of testing words which earlier were having random value due to not seen in the training. Proposed Experimental results on two benchmark datasets show the effectiveness of the proposed approach with respect to the accuracy and F1-score.

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Cited By

Chrismanto AWinarko ESuyanto Y(2024)EiAP-BC: A Novel Emoji Aware Inter-Attention Pair Model for Contextual Spam Comment Detection Based on Posting TextACM Transactions on Asian and Low-Resource Language Information Processing10.1145/369666323:12(1-30)Online publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1145/3696663
Mishra MJain SM SMani SBoragule ASahni NNair RGanesan DLane NShi W(2024)Multimodal Strategy To Defend Mobile Devices Against Vishing AttacksProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690683(1134-1146)Online publication date: 4-Dec-2024
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Manasa PMalik ABatra I(2024)Detection of Twitter Spam Using GLoVe Vocabulary Features, Bidirectional LSTM and Convolution Neural NetworkSN Computer Science10.1007/s42979-023-02518-15:2Online publication date: 13-Jan-2024
https://dl.acm.org/doi/10.1007/s42979-023-02518-1
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Spam detection in social media using convolutional and long short term memory neural network
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cover image Annals of Mathematics and Artificial Intelligence

Annals of Mathematics and Artificial Intelligence Volume 85, Issue 1

January 2019

69 pages

ISSN:1012-2443

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Copyright © Copyright © 2019 Springer Nature Switzerland AG.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2019

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Cited By

Chrismanto AWinarko ESuyanto Y(2024)EiAP-BC: A Novel Emoji Aware Inter-Attention Pair Model for Contextual Spam Comment Detection Based on Posting TextACM Transactions on Asian and Low-Resource Language Information Processing10.1145/369666323:12(1-30)Online publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1145/3696663
Mishra MJain SM SMani SBoragule ASahni NNair RGanesan DLane NShi W(2024)Multimodal Strategy To Defend Mobile Devices Against Vishing AttacksProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690683(1134-1146)Online publication date: 4-Dec-2024
https://dl.acm.org/doi/10.1145/3636534.3690683
Manasa PMalik ABatra I(2024)Detection of Twitter Spam Using GLoVe Vocabulary Features, Bidirectional LSTM and Convolution Neural NetworkSN Computer Science10.1007/s42979-023-02518-15:2Online publication date: 13-Jan-2024
https://dl.acm.org/doi/10.1007/s42979-023-02518-1
Das LAhuja LPandey A(2024)A novel deep learning model-based optimization algorithm for text message spam detectionThe Journal of Supercomputing10.1007/s11227-024-06148-z80:12(17823-17848)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s11227-024-06148-z
Sessa PLaforgue PCesa-Bianchi NKrause AOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Multitask learning with no regretProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666419(6770-6781)Online publication date: 10-Dec-2023
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Iqbal FJaved AJhaveri RAlmadhor AFarooq U(2023)Transfer Learning-based Forensic Analysis and Classification of E-Mail ContentACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3604592Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1145/3604592
Wu CZhang RGuo JDe Rijke MFan YCheng X(2023)PRADA: Practical Black-box Adversarial Attacks against Neural Ranking ModelsACM Transactions on Information Systems10.1145/357692341:4(1-27)Online publication date: 8-Apr-2023
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Guo ZYu KJolfaei ALi GDing FBeheshti A(2023)Mixed Graph Neural Network-Based Fake News Detection for Sustainable Vehicular Social NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.318501324:12(15486-15498)Online publication date: 1-Dec-2023
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Rao SVerma ABhatia T(2023)Hybrid ensemble framework with self-attention mechanism for social spam detection on imbalanced dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119594217:COnline publication date: 1-May-2023
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Ghanem RErbay HBakour K(2023)Contents-Based Spam Detection on Social Networks Using RoBERTa Embedding and Stacked BLSTMSN Computer Science10.1007/s42979-023-01798-x4:4Online publication date: 6-May-2023
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