Spam Detection on Twitter Using Traditional Classifiers

By M. McCord, Mooi Choo Choo Chuah. International Conference on Autonomic and Trusted Computing, 2011

308 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/4fc5defd41d17418a482f8bcca95743b54c6657c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fred Morstatter
2. P. J. García–Nieto
3. J. Martínez Torres
4. Carla Iglesias Comesaña
5. Liang Wu
6. Zachary Miller
7. William Deitrick
8. Brian Dickinson

In Computer Networks and Communications

1. P. J. García–Nieto
2. J. Martínez Torres
3. Carla Iglesias Comesaña
4. Zachary Miller
5. William Deitrick
6. Brian Dickinson
7. Wei-Gang Hu
8. Jing Gao

In Information Systems

1. Fred Morstatter
2. Stefano Cresci
3. Huan Liu
4. P. J. García–Nieto
5. J. Martínez Torres
6. Carla Iglesias Comesaña
7. Zachary Miller
8. William Deitrick

Citations in the corpus

• Misinformation in Social Media: Definition, Manipulation, and Detection (2019, background): “Early research in this field regards social media users as a special type of documents [3; 26].”
• Fame for sale: Efficient detection of fake Twitter followers (2015, background): “There are other works for spam detection, not detailed here, like [1, 28, 29, 30, 31, 32], which base their results on subsets, or on slightly modified versions, of criteria considered by the selected set of related work.”
• LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data (2014, background): “Knowledge discovery on linked data is the process of leveraging both node attributes and link structures for the learning on the corresponding systems, and it is of great importance to understanding the characteristics and interaction patterns in these systems.”
• Review: machine learning techniques applied to cybersecurity (2019, methodology): “The spread of social networks in recent years has made them a target for spammers, being Twitter the most attacked one due to its fast growth [101].”
• Twitter spammer detection using data stream clustering (2014, background): “One of the most recent studies [13] used a collection of learning algorithms including Random Forest, Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes.”
• Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies (2016, methodology): “Quite a few spam filtering techniques were investigated to remove such garbage data [15, 24 – 29].”