ENWalk: Learning Network Features for Spam Detection in Twitter

  title={ENWalk: Learning Network Features for Spam Detection in Twitter},
  author={K. C. Santosh and Suman Kalyan Maity and Arjun Mukherjee},
Social medias are increasing their influence with the vast public information leading to their active use for marketing by the companies and organizations. Such marketing promotions are difficult to identify unlike the traditional medias like TV and newspaper. So, it is very much important to identify the promoters in the social media. Although, there are active ongoing researches, existing approaches are far from solving the problem. To identify such imposters, it is very much important to… 

Spam2Vec: Learning Biased Embeddings for Spam Detection in Twitter

A semi-supervised framework Spam2Vec to identify spammers in Twitter that learns the spam representations of the node in the network by leveraging biased random walks.

Review Spam Detection Based on Multi-dimensional Features

A new review spam detection method based on multi-dimensional features is proposed that combines the text and user behavioral features as the overall features, which are used as the input to the classification module to detect spam reviews.

Product Popularity Modeling Via Time Series Embedding

A new popularity index based on Amazon: Product Popularity based on Sales Review Volume (PPSRV) is introduced and sequential deep learning models are explored and evaluated to obtain time series embedding that can predict the product popularity.

Artificial Intelligence and Mobile Services – AIMS 2020: 9th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, September 18-20, 2020, Proceedings

The novel multistage CNNs method work together with prior knowledge constraints in decision making to overcome the limited data problem in infant sound classification.



Detecting Spam and Promoting Campaigns in the Twitter Social Network

A scalable framework to detect both spam and promoting campaigns in Twitter is proposed, which can extract the actual campaigns with high precision and recall and distinguish the majority of the candidate campaigns correctly.

Social Spammer Detection in Microblogging

An optimization formulation is presented that models the social network and content information in a unified framework that can effectively utilize both kinds of information for social spammer detection in microblogging.

Detecting Spammers on Twitter

This paper uses tweets related to three famous trending topics from 2009 to construct a large labeled collection of users, manually classified into spammers and non-spammers, and identifies a number of characteristics related to tweet content and user social behavior which could potentially be used to detect spammers.

Understanding and combating link farming in the twitter social network

It is shown that a simple user ranking scheme that penalizes users for connecting to spammers can effectively address the link farming problem in Twitter by disincentivizing users from linking with other users simply to gain influence.

Detecting Campaign Promoters on Twitter Using Markov Random Fields

The proposed technique aims to identify user accounts that are involved in promotion on Twitter using typed Markov Random Fields (T-MRF), which is proposed as a generalization of the classic Markov random Fields.

Detecting spammers on social networks

The results show that it is possible to automatically identify the accounts used by spammers, and the analysis was used for take-down efforts in a real-world social network.

Uncovering social spammers: social honeypots + machine learning

It is found that the deployed social honeypots identify social spammers with low false positive rates and that the harvested spam data contains signals that are strongly correlated with observable profile features (e.g., content, friend information, posting patterns, etc.).

What is Twitter, a social network or a news media?

This work is the first quantitative study on the entire Twittersphere and information diffusion on it and finds a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.

TwitterRank: finding topic-sensitive influential twitterers

Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank, which is proposed to measure the influence of users in Twitter.

Suspended accounts in retrospect: an analysis of twitter spam

This study examines the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon and identifies an emerging marketplace of illegitimate programs operated by spammers.