Social World Knowledge: Modeling and Applications
Nir Lotan, Einat Minkov

TL;DR
This paper introduces SocialVec, a framework for creating social entity embeddings from social network data, capturing social aspects of world knowledge to improve tasks like bias detection and user trait prediction.
Contribution
The work presents SocialVec, a novel social entity embedding method derived from social contexts, filling a gap in existing fact-based knowledge representations.
Findings
Embeddings of 200K entities from Twitter improve bias detection.
Social embeddings outperform fact-based schemes in trait prediction.
Existing fact-based embeddings fail to capture social aspects.
Abstract
Social world knowledge is a key ingredient in effective communication and information processing by humans and machines alike. As of today, there exist many knowledge bases that represent factual world knowledge. Yet, there is no resource that is designed to capture social aspects of world knowledge. We believe that this work makes an important step towards the formulation and construction of such a resource. We introduce SocialVec, a general framework for eliciting low-dimensional entity embeddings from the social contexts in which they occur in social networks. In this framework, entities correspond to highly popular accounts which invoke general interest. We assume that entities that individual users tend to co-follow are socially related, and use this definition of social context to learn the entity embeddings. Similar to word embeddings which facilitate tasks that involve text…
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Taxonomy
TopicsTopic Modeling · Misinformation and Its Impacts · Opinion Dynamics and Social Influence
Methodsfail
