Social Network Polluting Contents Detection through Deep Learning Techniques

Martinelli, Fabio; Mercaldo, Francesco; Santone, Antonella

doi:10.1109/ijcnn.2019.8852080

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…Martinelli et al [32] developed a simplified deep learning method for determining if a single tweet was produced by a spammer or not. In the tests, the authors developed many MLP classifiers with a range of zero to four hidden layers.…”

Section: Fake Account Detection and Classificationmentioning

confidence: 99%

Real-Time Twitter Data Analysis: A Survey

Rasul

Jumaa

2022

UHD J SCI TECH

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Internet users are used to a steady stream of facts in the contemporary world. Numerous social media platforms, including Twitter, Facebook, and Quora, are plagued with spam accounts, posing a significant problem. These accounts are created to trick unwary real users into clicking on dangerous links or to continue publishing repetitious messages using automated software. This may significantly affect the user experiences on these websites. Effective methods for detecting certain types of spam have been intensively researched and developed. Effectively resolving this issue might be aided by doing sentiment analysis on these postings. Hence, this research provides a background study on Twitter data analysis, and surveys existing papers on Twitter sentiment analysis and fake account detection and classification. The investigation is restricted to the identification of social bots on the Twitter social media network. It examines the methodologies, classifiers, and detection accuracies of the several detection strategies now in use.

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Section: Fake Account Detection and Classificationmentioning

confidence: 99%