# Discovering collective narratives shifts in online discussions

arXiv: https://arxiv.org/abs/2307.08541 · HTML: https://mlsanity.com/paper/2307.08541/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 83 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election](https://mlsanity.com/paper/1707.00086/) (2017)
- [An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web](https://mlsanity.com/paper/2008.09961/) (2020)
- [Characterizing Partisan Political Narrative Frameworks about COVID-19 on Twitter](https://mlsanity.com/paper/2103.06960/) (2021)

## Built on by (most cited first, 3 of 3)

- [Extracting narrative signals from public discourse: a network-based approach](https://mlsanity.com/paper/2411.00702/) (2024)
- [Detecting Narrative Shifts through Persistent Structures: A Topological Analysis of Media Discourse](https://mlsanity.com/paper/2506.14836/) (2025)
- [Neural Total Variation Distance Estimators for Changepoint Detection in News Data](https://mlsanity.com/paper/2506.18764/) (2025)

## Abstract (arXiv)

Narrative is a foundation of human cognition and decision making. Because narratives play a crucial role in societal discourses and spread of misinformation and because of the pervasive use of social media, the narrative dynamics on social media can have profound societal impact. Yet, systematic and computational understanding of online narratives faces critical challenge of the scale and dynamics; how can we reliably and automatically extract narratives from massive amount of texts? How do narratives emerge, spread, and die? Here, we propose a systematic narrative discovery framework that fill this gap by combining change point detection, semantic role labeling (SRL), and automatic aggregation of narrative fragments into narrative networks. We evaluate our model with synthetic and empirical data two-Twitter corpora about COVID-19 and 2017 French Election. Results demonstrate that our approach can recover major narrative shifts that correspond to the major events.