RETRACTED ARTICLE: Graph based CNN Algorithm to Detect Spammer Activity Over Social Media

Tandon, Aditya; Guha, Shouvik Kumar; Rashid, Junaid; Kim, Jung-Eun; Gahlan, Mamta; Shabaz, Mohammad; Anjum, Nasreen

doi:10.1080/03772063.2022.2061610

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…e accuracy and scope of information extraction can be improved using an entity-relationship-based framework [42][43][44][45][46][47]. Few research works employed the term-frequency methodology for ranking the webpages [48][49][50][51][52][53][54]. us, there is a demand for a practical ontological framework for managing documents and retrieving information based on the user query.…”

Section: Literature Reviewmentioning

confidence: 99%

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Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

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Section: Literature Reviewmentioning

confidence: 99%

“…erefore, developing an ontological framework for document management can support organizations in satisfying their stakeholders. In addition, the role of natural language processing (NLP) in the ontological framework enables individuals to interact with the system in their natural language [52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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Suspicious behaviour detection in multilayer social networks using PF-KMA and SS-GAE techniques

Sandeep,

Siddesh,

Naresh

2024

Soc. Netw. Anal. Min.

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Multilayer Social networks are an important part of human life to interact on different networks at the same time. Due to the openness of such networks, they become a platform for spammers to spread malicious behaviors. Hence, there is an urgent need for effective detection of malicious behaviors; thereby, enabling the networks to take mitigation actions to decrease the possibility to reward such activities. Detection of suspicious behaviors in previous works is challenging due to the problems of community detection, the large amount of feature corruption, and memory requirements. Thus, to deal with such problems, in this paper, an efficient clustering-based detection of malicious users in multilayer social networks is proposed. Initially, the input dataset is pre-processed and used for Exponential Distribution based Erdős–Rényi based graph construction. From the graph structure, two types of data, such as user representations and graph features are extracted for graph encoding using the Soft Sign activated Graph Auto Encoder model. Then, the decoding is done to predict the information diffusion level, thereby, ranking the users using the Laplace Regularization technique. Then, the ranked users are clustered into different groups using the Pareto Front based K-Means Clustering Algorithm technique. Finally, the experimental results were analyzed to demonstrate the efficacy of the proposed model to detect malicious users in multilayer social networks.

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Spam community detection & influence minimization using NRIM algorithm

Srivastava

Agrahari

Singh

2023

Computers in Human Behavior

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RETRACTED ARTICLE: Graph based CNN Algorithm to Detect Spammer Activity Over Social Media

Cited by 4 publications

References 29 publications

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Suspicious behaviour detection in multilayer social networks using PF-KMA and SS-GAE techniques

Spam community detection & influence minimization using NRIM algorithm

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