mlsanityData to 7 Oct 2026

Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model

arXiv 2104.03189 ↗First posted 7 Apr 2021cs.CL cs.AI cs.CYcited by 4

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Abstract (arXiv)

Multiview representation learning of data can help construct coherent and contextualized users' representations on social media. This paper suggests a joint embedding model, incorporating users' social and textual information to learn contextualized user representations used for understanding their lifestyle choices. We apply our model to tweets related to two lifestyle activities, `Yoga' and `Keto diet' and use it to analyze users' activity type and motivation. We explain the data collection and annotation process in detail and provide an in-depth analysis of users from different classes based on their Twitter content. Our experiments show that our model results in performance improvements in both domains.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 47 references that are in the map. First posted on arXiv 7 Apr 2021.

Builds on

22 of 47 references.

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10 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 2.

4 papers.

  1. 2022Overcoming Language Disparity in Online Content Classification with Multimodal Learningcited by 8
  2. 2021Twitter User Representation Using Weakly Supervised Graph Embeddingcited by 4
  3. 2026Social Knowledge for Cross-Domain User Preference Modelingcited by 0
  4. 2026POLAR:A Per-User Association Test in Embedding Spacecited by 0