# A Social Data-Driven System for Identifying Estate-related Events and Topics

arXiv: https://arxiv.org/abs/2508.03711 · HTML: https://mlsanity.com/paper/2508.03711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [A Transformer-based Framework for POI-level Social Post Geolocation](https://mlsanity.com/paper/2211.01336/) (2022)
- [DISCO: Comprehensive and Explainable Disinformation Detection](https://mlsanity.com/paper/2203.04928/) (2022)
- [Protecting Geolocation Privacy of Photo Collections](https://mlsanity.com/paper/1912.02085/) (2019)
- [Label-Free Topic-Focused Summarization Using Query Augmentation](https://mlsanity.com/paper/2404.16411/) (2024)

## Abstract (arXiv)

Social media platforms such as Twitter and Facebook have become deeply embedded in our everyday life, offering a dynamic stream of localized news and personal experiences. The ubiquity of these platforms position them as valuable resources for identifying estate-related issues, especially in the context of growing urban populations. In this work, we present a language model-based system for the detection and classification of estate-related events from social media content. Our system employs a hierarchical classification framework to first filter relevant posts and then categorize them into actionable estate-related topics. Additionally, for posts lacking explicit geotags, we apply a transformer-based geolocation module to infer posting locations at the point-of-interest level. This integrated approach supports timely, data-driven insights for urban management, operational response and situational awareness.