  Due to the increasing trend of performing spamming activities (e.g., Web
spam, deceptive reviews, fake followers, etc.) on various online platforms to
gain undeserved benefits, spam detection has emerged as a hot research issue.
Previous attempts to combat spam mainly employ features related to metadata,
user behaviors, or relational ties. These works have made considerable progress
in understanding and filtering spamming campaigns. However, this problem
remains far from fully solved. Almost all the proposed features focus on a
limited number of observed attributes or explainable phenomena, making it
difficult for existing methods to achieve further improvement. To broaden the
vision about solving the spam problem and address long-standing challenges
(class imbalance and graph incompleteness) in the spam detection area, we
propose a new attempt of utilizing signed latent factors to filter fraudulent
activities. The spam-contaminated relational datasets of multiple online
applications in this scenario are interpreted by the unified signed network.
Two competitive and highly dissimilar algorithms of latent factors mining (LFM)
models are designed based on multi-relational likelihoods estimation (LFM-MRLE)
and signed pairwise ranking (LFM-SPR), respectively. We then explore how to
apply the mined latent factors to spam detection tasks. Experiments on
real-world datasets of different kinds of Web applications (social media and
Web forum) indicate that LFM models outperform state-of-the-art baselines in
detecting spamming activities. By specifically manipulating experimental data,
the effectiveness of our methods in dealing with incomplete and imbalanced
challenges is valida
