A survey on detecting spam accounts on Twitter network

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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“…( 2019 ) Spam Confused Yes Low Not sure Çıtlak et al. ( 2019 ) Disinformation Mislead/deceive Yes Medium False Guo et al. ( 2019 ) …”

Section: Introductionmentioning

confidence: 99%

“…It is difficult to distinguish spam from real messages, as spammers hack users’ information (Çıtlak et al. 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

167

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“…To sum up, spam is a real issue that affects the user experience in social media and there are multiple research papers [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] aimed to fight the existence of spam. Many of them focus on social media as a broad category and since Twitter is considered a microblogging service with different user requirements, this broad category of research does not always fit to Twitter.…”

Section: Related Workmentioning

confidence: 99%

“…This indicates that there is a problem with the currently used spam detection framework. Hence, many researchers are concerned with investigating and solving the problem of detecting/preventing spam and phishing on Twitter platform [16,24,[29][30][31][32][33][34][35][36][37][38][39][40]. This paper introduces a new approach for detecting spam on microblogging services using domain popularity and Machine Learning (ML) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Detecting Spam in Twitter Microblogging Services: A Novel Machine Learning Approach based on Domain Popularity

Binsaeed¹,

Stringhini²,

Youssef³

2020

IJACSA

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Detecting Internet malicious activities has been and continues to be a critical issue that needs to be addressed effectively. This is essential to protect our personal information, computing resources, and financial capitals from unsolicited actions, such as, credential information theft, downloading and installing malware, extortion, etc. The introduction of the social media such as Twitter has given malicious users a new and a promising platform to perform their activities, ranging from a simple spam message to taking a full control over the victim's machine. Twitter revealed that its algorithms for detecting spam are not very effective; most of the trending hashtags include unrelated spam and advertising tweets which indicates that there is a problem with the currently used spam detection framework. This paper proposes a new approach for detecting spam in Twitter microblogging using Machine Learning (ML) techniques and domain popularity services. The proposed approach comprises two main stages: 1) Tweets are collected periodically and filtered by selecting the ones that appear more frequently than a decided threshold in the specified period (i.e. common tweets). Then, an inspection is conducted on the common tweets by checking the associated URL domain with Alexa's top one million globally viewed websites. If a tweet is common on Twitter but does not appear on the top one million globally viewed websites, it is flagged as a potential spam. 2) The second stage kicks in by running ML algorithms on the flagged tweets to extract features that help detect the cluster of spam and prevent it in real-time. The performance of the proposed approach has been evaluated using three most popular classification models (random forest, J48, and Naïve Bayes). For all classifiers, results showed the effectiveness of the proposed method in terms of different performance metrics (e.g. precision, sensitivity, F1score, accuracy) and using different test scenarios.

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“…Users are usually unaware of any deception when they enter data into forms connected to clickjacking. Clicking on such a page, which seems innocuous, may result in the installation of a harmful program or disclosure of confidential information in a computer or URL redirection mechanisms that attacker can automatically redirect a visitor to a captured web site [3].…”

Section: Introductionmentioning

confidence: 99%

Analysis of spammers’ behavior on a live streaming chat

Yousukkee

Wisitpongphan

2021

IJ-AI

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<span id="docs-internal-guid-f908fd2e-7fff-1849-4fda-c2cf9baed97e"><span>Live streaming is becoming a popular channel for advertising and marketing. An advertising company can use this feature to broadcast and reach a large number of customers. YouTube is one of the streaming media with an extreme growth rate and a large number of viewers. Thus, it has become a primary target of spammers and attackers. Understanding the behavior of users on live chat may reduce the moderator’s time in identifying and preventing spammers from disturbing other users. In this paper, we analyzed YouTube live streaming comments in order to understand spammers’ behavior. Seven user’s behavior features and message characteristic features were comprehensively analyzed. According to our findings, features that performed best in terms of run time and classification efficiency is the relevant score together with the time spent in live chat and the number of messages per user. The accuracy is as high as 66.22 percent. In addition, the most suitable technique for real-time classification is a decision tree.</span></span>

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A survey on detecting spam accounts on Twitter network

Cited by 22 publications

References 31 publications

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Detecting Spam in Twitter Microblogging Services: A Novel Machine Learning Approach based on Domain Popularity

Analysis of spammers’ behavior on a live streaming chat

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