Narrative Tracking: Detecting Heroes, Villains, and Victims in Newspapers

Extracting triplets to monitor and analyze narrative shifts in news reports.

Background

Using LLMs for role labeling and embedding extraction, we adopt a three-stage processing pipeline.

Three-Stage Processing Pipeline for Role Labeling and Embedding Extraction: (1) Extract hero, villain, and victims from news articles as a question answering task with Vicuna. (2) Normalize entities for spelling variations and disambiguate using GENRE with the article as context. (3) Extract embeddings with RoBERTa for clustering and further analysis.

Installation

Python Setup

First, create a python environment. We will use pyenv, but other options will likely work to.

Use th following commands to (1) install a specific python version, (2) create a new virtual environment, (3) activate that environment and (4) install python dependencies.

pyenv install -v 3.10.8
pyenv virtualenv 3.10.8 hvv
pyenv activate hvv
pip install -r requirements.txt

Configure Database Connection

Duplicate .env-template to .env and populate it with the appropriate database credentials.

CONNECTION_STRING="mongodb://localhost:27017/"
DATABASE_NAME="articlesDB"