Real Time Detection of Drifted Twitter Spam Based on Statistical Features

Kamble, Sayali; Sangve, Sunil

doi:10.1109/icicet.2018.8533767

Cited by 6 publications

(1 citation statement)

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“…Ms. Sayali Kamble et al [5] has exhibited the plan of ongoing vector space denotation of words. Evaluation of a novel AI-based way to deal with specialization Social spam detection.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Comparison for Spam Detection in Social Media Using Deep Learning Algorithms

Deshmukh¹

2021

TURCOMAT

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Social media applications like Twitter, Instagram, Facebook have helped people to connect to each other. This has been eased due to high-speed internet. However, this has invited various spam messages through tweets or Facebook. The sole purpose of such messages is aggregation or exploitation of personal data in terms of finances or medical records, political benefit’s or community violence. This makes spam detection an extreme value-added service. We tend to recommend a 1D CNN algorithmic technique and compare results with variants of CNN and with boosting algorithms. The model is braced with linguistics data in the illustration of the words with the assistance of knowledge-bases such as Word2vec and fast ext. This improves the end to end performance, by providing higher linguistics vector illustration of input testing words. Projected Experimental results show the efficiency of the projected approach from the point of view of accuracy, F1-score and response time.

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“…Ms. Sayali Kamble et al [5] has exhibited the plan of ongoing vector space denotation of words. Evaluation of a novel AI-based way to deal with specialization Social spam detection.…”

Section: Literature Surveymentioning

confidence: 99%