Improving Tweet Representations using Temporal and User Context

TL;DR

This paper introduces a new tweet representation model that leverages temporal context and user information to improve semantic understanding and profile attribute prediction.

Contribution

The model uniquely combines chronological tweet context and user-specific data to enhance tweet embeddings beyond existing methods.

Findings

Outperforms state-of-the-art in user profile attribute prediction

Achieves 19.66% improvement in spouse attribute prediction

Demonstrates effectiveness of temporal and user-aware modeling

Abstract

In this work we propose a novel representation learning model which computes semantic representations for tweets accurately. Our model systematically exploits the chronologically adjacent tweets ('context') from users' Twitter timelines for this task. Further, we make our model user-aware so that it can do well in modeling the target tweet by exploiting the rich knowledge about the user such as the way the user writes the post and also summarizing the topics on which the user writes. We empirically demonstrate that the proposed models outperform the state-of-the-art models in predicting the user profile attributes like spouse, education and job by 19.66%, 2.27% and 2.22% respectively.