Preserving Integrity in Online Social Networks
Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. This paper surveys the state of the art in keeping online platforms and their users safe from such harm, also known as the problem of preserving integrity. This survey comes from the perspective of having to combat a broad spectrum of integrity violations at Facebook. We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community. Instead of discussing the many individual violation types, we identify key aspects of the social-media eco-system, each of which is common to a wide variety violation types. Furthermore, each of these components represents an area for research and development, and the innovations that are found can be applied widely.
Code (0)
등록된 구현이 없습니다.
Tasks
MisinformationSimilar Papers 제목 키워드 기반
Implicit Contextual Integrity in Online Social Networks
Many real incidents demonstrate that users of Online Social Networks need mechanisms that help them manage their interactions by increasing the awareness of the different contexts that coexist in Online Social Networks a…
TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook
Since its inception, Facebook has become an integral part of the online social community. People rely on Facebook to make connections with others and build communities. As a result, it is paramount to protect the integri…
Graph EmbeddingMisinformationSHARE: Social-Humanities AI for Research and Education
This intermediate technical report introduces the SHARE family of base models and the MIRROR user interface. The SHARE models are the first causal language models fully pretrained by and for the social sciences and human…
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework
This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality. LE3D is a generalisable platform for evaluating novel drift detection mechanisms within the Internet of …
Drift DetectionPrivacy PreservingTime SeriesTime Series AnalysisIntegrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Search
Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc. While the approach has demonstrated its efficacy in tasks like semantic matching and context…
RetrievalText Matching