The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
The feature quantity: an information theoretic perspective of Tfidf-like measures
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Machine Learning
Data mining tasks and methods: Classification: decision-tree discovery
Handbook of data mining and knowledge discovery
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
An effective defense against email spam laundering
Proceedings of the 13th ACM conference on Computer and communications security
Spamalytics: an empirical analysis of spam marketing conversion
Communications of the ACM - The Status of the P versus NP Problem
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
@spam: the underground on 140 characters or less
Proceedings of the 17th ACM conference on Computer and communications security
Detecting and characterizing social spam campaigns
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Detecting spammers on social networks
Proceedings of the 26th Annual Computer Security Applications Conference
Who is tweeting on Twitter: human, bot, or cyborg?
Proceedings of the 26th Annual Computer Security Applications Conference
Twitter spammer detection using data stream clustering
Information Sciences: an International Journal
| Hi-index | 0.00 |
The popularity of Twitter greatly depends on the quality and integrity of contents contributed by users. Unfortunately, Twitter has attracted spammers to post spam content which pollutes the community. Social spamming is more successful than traditional methods such as email spamming by using social relationship between users. Detecting spam is the first and very critical step in the battle of fighting spam. Conventional detection methods check individual messages or accounts for the existence of spam. Our work takes the collective perspective, and focuses on detecting spam campaigns that manipulate multiple accounts to spread spam on Twitter. Complementary to conventional detection methods, our work brings efficiency and robustness. More specifically, we design an automatic classification system based on machine learning, and apply multiple features for classifying spam campaigns. Our experimental evaluation demonstrates the efficacy of the proposed classification system.