Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers

Yang, Chao; Harkreader, Robert Chandler; Gu, Guofei

doi:10.1109/tifs.2013.2267732

Cited by 294 publications

(231 citation statements)

References 20 publications

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“…Spammers can be detected by analyzing their tweets. This is necessary to filter spam tweets from legitimate ones and provide users a spam-free environment which is the aim of Twitter [60]. Each tweet contains the information listed in Table 2.…”

Section: B Tweet-based Featuresmentioning

confidence: 99%

A Survey of Spam Detection Methods on Twitter

Kabakuş¹,

Kara²

2017

ijacsa

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Section: B Tweet-based Featuresmentioning

confidence: 99%

A Survey of Spam Detection Methods on Twitter

Kabakuş¹,

Kara²

2017

ijacsa

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“…The Chao Yang focuses on the empirical study and new design for twitter spammer's fighter. With the help of machine learning detection techniques features and the goal is to provide the first empirical analysis of the evasion tactics and in-depth analysis of those evasion tactics [3]. Make a comprehensive and empirical analysis of the evasion tactics utilized by Twitter spammers.…”

Section: Related Workmentioning

confidence: 99%

“…The online social networking sites such as twitter and Facebook are now part of many people's daily routine and hence it is updated. Spammers have utilized Twitter as a new platform to achieve their malicious goals such as sending spam messages, spreading malware, hosting botnet and control (C&C) channels and performing other illicit activities [3]. The named entity recognition (NER) used in twitter stream for the monitoring and response to the stream.…”

Section: Related Workmentioning

confidence: 99%

“…Many social communities thought there data must be spam free means that errors free. The error can be grammatical also and the spam data can be affected your system and hence that malicious data harmful to the system and that's why it is detected properly and preserving that such type of spam and hence system must be error free [3]. The targeted twitter proposed system of tweet segmentation helps to removing the errors and protected from the illegal messages.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tweet Segmentation and Enhancement of Tweets

2015

IJSR

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Twitter is a biggest connecting site that includes various types of users. Many users share their data and it is updated sites so data should be maintained properly and accessing in proper way. Hence mining algorithm helps to managing data. Many application such as Information Retrieval and Natural Language Processing contains some errors and short nature of tweets, hence to recover of such type of tweets tweet classification is used. Data mining algorithm used in the classification of tweets hence it is easily access and easy to understand.

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“…Most studies [11] [14] conclude that RF gives a higher classification performance than the other supervised machine learning algorithms. However, in these studies specific details related to which algorithm parameters or feature selection methods were used are often not provided.…”

Section: Introductionmentioning

confidence: 99%

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

Al-Janabi

Quincey

András

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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Abstract-The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious content in online social networks (ONSs). Multisource features have been used to detect social network posts that contain malicious Uniform Resource Locators (URLs). These URLs could direct users to websites that contain malicious content, drive-by download attacks, phishing, spam, and scams. For the data collection stage, the Twitter streaming application programming interface (API) was used and VirusTotal was used for labelling the dataset. A random forest classification model was used with a combination of features derived from a range of sources. The random forest model without any tuning and feature selection produced a recall value of 0.89. After further investigation and applying parameter tuning and feature selection methods, however, we were able to improve the classifier performance to 0.92 in recall.

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Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers

Cited by 294 publications

References 20 publications

A Survey of Spam Detection Methods on Twitter

A Survey of Spam Detection Methods on Twitter

Tweet Segmentation and Enhancement of Tweets

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

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