• Corpus ID: 10325563

Twitter Spammer Profile Detection

@inproceedings{Gee2010TwitterSP,
  title={Twitter Spammer Profile Detection},
  author={Grace Gee and Hakson Teh gracehg},
  year={2010}
}
Twitter Spammer Profile Detection Grace Gee, Hakson Teh gracehg@stanford.edu, hakson@cs.stanford.edu December 9, 2010 
cs229.stanford.edu
15 Citations
Highly Influential Citations
4
Background Citations
7
Methods Citations
8
Results Citations
1

15 Citations

Citation Type

Citation Type

A Survey of Spam Detection Methods on Twitter
Twitter is one of the most popular social media platforms that has 313 million monthly active users which post 500 million tweets per day. This popularity attracts the attention of spammers who use…
Techniques to Detect Spammers in Twitter- A Survey
TLDR
Current study provides an overview of the methods, features used, detection rate and their limitations (if any) for detecting spam profiles mainly in Twitter.
Evaluating social spammer detection systems
TLDR
Through analysis, this paper can identify the most effective and efficient social spammer detection features and help develop a faster and more accurate classifier model that has higher true positives and lower false positives.
Detecting Spam Tweets using Character N-gram Features
TLDR
This paper proposes using a low-level character n-grams feature that avoids the use of tokenizers or any language dependent tools, and evaluates the performance of multiple ma-chine learning classifiers with different representations of the proposed feature.
Feature engineering for detecting spammers on Twitter: Modelling and analysis
TLDR
This article reviews the latest research works to determine the most effective features that were investigated for spam detection in the literature and reveals the important role of some features like the reputation of the account, average length of the tweet, average mention per tweet, age of the accounts, and the average time between posts in the process of identifying spammers in the social network.
An Analytical Model for Identifying Suspected Users on Twitter
TLDR
A prototype has been developed to classify Twitter users as suspicious and nonsuspicious on the basis of features which identify user demographics and their tweeting activity using Twitter APIs based upon user and tweet meta-data.
A Framework for Evaluating Anti Spammer Systems for Twitter
TLDR
An evaluation framework is proposed that allows researchers, developers, and practitioners to access existing user-based and content-based features, implement their own features, and evaluate the performance of their systems against other systems, to identify the most effective and efficient spammer detection features.
Detecting Malicious Users in Twitter using Classifiers
TLDR
A framework for the detection of malicious users, non-malicious users and celebrities has been developed by using an attribute set for user classification based on user characteristics to identify those forged users.
A Novel Technique to Characterize Social Network Users: Comparative Study
TLDR
A large dataset of genuine and spam profiles was created and exploited for validation purpose, and a unified framework to classify various types of Twitter users was proposed, showing the effectiveness of the proposed technique based upon promising feature selection.
Interaction-Based Behavioral Analysis of Twitter Social Network Accounts
  • Tuncer
  • Computer Science
    Applied Sciences
  • 2019
TLDR
This article considers methodological approaches to determine and prevent social media manipulation specific to Twitter by using k-nearest neighbor (K-NN), support vector machine (SVM), and artificial neural network (ANN) algorithms.
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References

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LIBLINEAR: A Library for Large Linear Classification
LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library…
LIBSVM: A library for support vector machines
TLDR
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Twitter Study Available: http://www.pearanalytics.com/blog/wp- content/uploads
  • Twitter Study Available: http://www.pearanalytics.com/blog/wp- content/uploads
  • 2009