# Social World Knowledge: Modeling and Applications

arXiv: https://arxiv.org/abs/2306.16299 · HTML: https://mlsanity.com/paper/2306.16299/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 84 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 22 of 22)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks](https://mlsanity.com/paper/1608.04207/) (2016)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://mlsanity.com/paper/1903.12287/) (2019)
- [E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT](https://mlsanity.com/paper/1911.03681/) (2019)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Item2Vec: Neural Item Embedding for Collaborative Filtering](https://mlsanity.com/paper/1603.04259/) (2016)
- [Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms](https://mlsanity.com/paper/1805.09843/) (2018)
- [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://mlsanity.com/paper/1601.01343/) (2016)
- [What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context](https://mlsanity.com/paper/2005.04518/) (2020)
- [An Open-World Extension to Knowledge Graph Completion Models](https://mlsanity.com/paper/1906.08382/) (2019)
- [Towards Hate Speech Detection at Large via Deep Generative Modeling](https://mlsanity.com/paper/2005.06370/) (2020)
- [Predicting Twitter User Socioeconomic Attributes with Network and Language Information](https://mlsanity.com/paper/1804.04095/) (2018)
- [Corpus-level Fine-grained Entity Typing](https://mlsanity.com/paper/1708.02275/) (2017)
- [Evaluating vector-space models of analogy](https://mlsanity.com/paper/1705.04416/) (2017)
- [Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement](https://mlsanity.com/paper/2010.06472/) (2020)
- [Would you Like to Talk about Sports Now? Towards Contextual Topic Suggestion for Open-Domain Conversational Agents](https://mlsanity.com/paper/2005.13803/) (2020)

## Built on by (most cited first, 1 of 1)

- [Generative AI for Hate Speech Detection: Evaluation and Findings](https://mlsanity.com/paper/2311.09993/) (2023)

## Abstract (arXiv)

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 semantics, we expect the learned social entity embeddings to benefit multiple tasks of social flavor. In this work, we elicited the social embeddings of roughly 200K entities from a sample of 1.3M Twitter users and the accounts that they follow. We employ and gauge the resulting embeddings on two tasks of social importance. First, we assess the political bias of news sources in terms of entity similarity in the social embedding space. Second, we predict the personal traits of individual Twitter users based on the social embeddings of entities that they follow. In both cases, we show advantageous or competitive performance using our approach compared with task-specific baselines. We further show that existing entity embedding schemes, which are fact-based, fail to capture social aspects of knowledge. We make the learned social entity embeddings available to the research community to support further exploration of social world knowledge and its applications.