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Sexual Orientation-Based Victimization and Internalized Homonegativity among Latinx Sexual Minority Youth: Exploring the Moderating Effect of Social Support and School Level
Latinx sexual minority youth (SMY) experience sexual orientation-based victimization at school and may internalize these homophobic experiences. At the same time, the minority stress model posits sexual minority individuals may benefit from social support. Thus, the current study explored associations between sexual orientation-based victimization and internalized homonegativity across social contexts (family, peer, and school adult) and across development (high school versus college) among 233 Latinx SMY. Results showed that sexual orientation-based victimization was positively associated with internalized homonegativity among Latinx SMY. Such associations were moderated by perceived family support and the school level in the family context such that in high school, perceived family support exacerbated associations between sexual orientation-based victimization and internalized homonegativity, but in college, it mitigated that association. Similarly, findings also showed perceived peer support exacerbated the associations between sexual orientation-based victimization and internalized homonegativity. There was no moderating effect of perceived school adult support in the associations between sexual orientation-based victimization and internalized homonegativity. These findings contribute empirical evidence regarding the minority stress model among Latinx SMY and highlighted the potential complex effect of social support across social contexts and development
SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection
Sybil attacks are a fundamental threat to the security of distributed
systems. Recently, there has been a growing interest in leveraging social
networks to mitigate Sybil attacks. However, the existing approaches suffer
from one or more drawbacks, including bootstrapping from either only known
benign or known Sybil nodes, failing to tolerate noise in their prior knowledge
about known benign or Sybil nodes, and being not scalable.
In this work, we aim to overcome these drawbacks. Towards this goal, we
introduce SybilBelief, a semi-supervised learning framework, to detect Sybil
nodes. SybilBelief takes a social network of the nodes in the system, a small
set of known benign nodes, and, optionally, a small set of known Sybils as
input. Then SybilBelief propagates the label information from the known benign
and/or Sybil nodes to the remaining nodes in the system.
We evaluate SybilBelief using both synthetic and real world social network
topologies. We show that SybilBelief is able to accurately identify Sybil nodes
with low false positive rates and low false negative rates. SybilBelief is
resilient to noise in our prior knowledge about known benign and Sybil nodes.
Moreover, SybilBelief performs orders of magnitudes better than existing Sybil
classification mechanisms and significantly better than existing Sybil ranking
mechanisms.Comment: 12 page
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