An integrated approach for malicious tweets detection using NLP

Gharge, Sagar; Chavan, Manik K.

doi:10.1109/icicct.2017.7975235

Cited by 24 publications

(4 citation statements)

References 11 publications

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“…Gharge et al [3] initiate a method, which is classified on the basis of two new aspects. The first one is the recognition of spam tweets without any prior information about the users and the second one is the exploration of language for spam detection on Twitter trending topic at that time.…”

Section: Detecting Spam In Trending Topicmentioning

confidence: 99%

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Spammer Detection and Fake User Identification on Social Networks

et al. 2019

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Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform.

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Section: Detecting Spam In Trending Topicmentioning

confidence: 99%

“…In addition, it also decreases the repute of the OSN platforms. Therefore, it is essential to design a scheme to spot spammers so that corrective efforts can be taken to counter their malicious activities [3].…”

Section: Introductionmentioning

confidence: 99%

Spammer Detection and Fake User Identification on Social Networks

et al. 2019

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“…2 Literature review Mohawesh et. al [3] offered a thorough analysis of the most significant efforts on machine learning-based fake review identification to date. They have first reviewed the feature extraction strategies employed by numerous researchers.…”

Section: Social Feasibilitymentioning

confidence: 99%

Detection Of Fake Online Reviews Using Semi-Supervised And Supervised Learning

Firdous,

Priyadarshini,

Sree

et al. 2023

jrtdd

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In the era of booming social media and e-commerce, the authenticity of online reviews has become a critical factor influencing consumer decision-making. This study presents an innovative approach to combating the proliferation of fake online reviews, combining semi-supervised and supervised learning techniques. Our method involves extracting pertinent features from review text and employing them to train a supervised classifier, augmented by a semi-supervised algorithm designed to identify and eliminate potentially fraudulent reviews. We leverage a comprehensive dataset comprising genuine and fake reviews, and our results demonstrate the superior performance of our proposed method compared to existing techniques. It achieves a high accuracy rate in identifying fake reviews while effectively minimizing false positives. in parallel, the increasing importance of social media monitoring highlights the significance of analyzing social data to gain insights into consumer behaviour. To this end, we conduct sentiment analysis on Twitter tweets, focusing on user reviews of movies. This paper introduces a novel combined dictionary that incorporates social media keywords and online review data. Additionally, we uncover hidden relationship patterns among these keywords, shedding light on the intricate dynamics of online sentiment expression. As online shopping continues to gain popularity, online reviews have emerged as pivotal factors in e-commerce decisionmaking. A substantial portion of consumers routinely consult product and store reviews before making purchasing decisions. Unfortunately, this heightened reliance on reviews has given rise to a surge in fake, spam, or poorly constructed reviews that can mislead consumers. Therefore, the need to distinguish genuine from fraudulent reviews is more critical than ever. Our study addresses this issue by leveraging machine learning techniques, including supervised and semi-supervised learning, to accurately detect fake reviews. Recognizing that obtaining labelled data can be challenging, we emphasize developing models requiring minimal labelled data. Our approach ensures efficiency, providing rapid results while maintaining high accuracy. In this paper, we explore various classification algorithms, including support vector machines (SVM), random forests (RF), and deep neural networks, to discern the authenticity of online reviews. By combining the power of these algorithms and the use of unlabeled data through semi-supervised learning, we strive to enhance the credibility of online reviews and promote informed decision-making in the realms of e-commerce and social media.

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“…What's more, it likewise diminishes the notoriety of the OSN stages. Consequently, it is fundamental to plan a plan to spot spammers with the goal that remedial endeavors can be taken to counter their vindictive exercises [3] . A few research works have been completed in the area of Twitter spam identification.…”

Section: Introductionmentioning

confidence: 99%

Identifying spammers and fake users identification in online social networking sites

Mobina

2020

Int. J. Commun. Inf. Technol.

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Online social networking sites are new platforms for spreading spammers and fake news for attackers. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features.

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An integrated approach for malicious tweets detection using NLP

Cited by 24 publications

References 11 publications

Spammer Detection and Fake User Identification on Social Networks

Spammer Detection and Fake User Identification on Social Networks

Detection Of Fake Online Reviews Using Semi-Supervised And Supervised Learning

Identifying spammers and fake users identification in online social networking sites

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