SocialVec: Social Entity Embeddings

TL;DR

SocialVec introduces a framework for creating social entity embeddings from Twitter data, capturing socio-demographic similarities among accounts, and demonstrates their usefulness in user trait inference and political bias detection.

Contribution

This paper presents a novel social entity embedding method from Twitter data, improving tasks like user trait inference and bias detection over existing schemes.

Findings

Embeddings for 200,000 accounts learned from 1.3 million users.

Embeddings outperform existing schemes in user trait inference.

Embeddings effectively gauge political bias of news sources.

Abstract

This paper introduces SocialVec, a general framework for eliciting social world knowledge from social networks, and applies this framework to Twitter. SocialVec learns low-dimensional embeddings of popular accounts, which represent entities of general interest, based on their co-occurrences patterns within the accounts followed by individual users, thus modeling entity similarity in socio-demographic terms. Similar to word embeddings, which facilitate tasks that involve text processing, we expect social entity embeddings to benefit tasks of social flavor. We have learned social embeddings for roughly 200,000 popular accounts from a sample of the Twitter network that includes more than 1.3 million users and the accounts that they follow, and evaluate the resulting embeddings on two different tasks. The first task involves the automatic inference of personal traits of users from their social media profiles. In another study, we exploit SocialVec embeddings for gauging the political bias of news sources in Twitter. In both cases, we prove SocialVec embeddings to be advantageous compared with existing entity embedding schemes. We will make the SocialVec entity embeddings publicly available to support further exploration of social world knowledge as reflected in Twitter.