How do major events cause seismic shifts in public opinion? This talk explains a data science workflow built in Python to model how immigration attitudes in Ireland are reshaped by critical events rather than evolving gradually as classic sociological theories predict.

In this talk, I will walk through the process of using Python to trace these shifts, drawing from years of Irish news stories, tweets, and national surveys also explaining the NLP tooling (Gen AI, transformers), dynamic network analysis, and agent-based social simulations to uncover short-lived, emotionally driven spikes in discourse after high-profile events (eg: Cologne incident in Germany). Alongside computational techniques, attendees will see how temporal network analysis isolates the “signal” of event-driven changes using custom exponential decay kernels, and how attitude-network maps reveal symbolic connections in media framing.

Key technical components include:
- Data engineering pipelines for scraping, cleaning, and normalizing multi-source text corpora
- Advanced NLP: topic modelling, sentiment trajectory tracking, gendered discourse with transformer models
- Temporal graph modelling: visualizing the spread and clustering of attitudes post-event with ResIN network model, and custom weighting functions
- Agent-based simulations for hypothesis testing: comparing linear (modernization) vs. reactive (event-driven) models

The PyCon community will benefit from this talk, which explains how Python empowers interdisciplinary research, bridging social science theory, real-world AI, and robust engineering. It also gives an understanding to viewers to scale up NLP pipelines or build temporal network models, and leaves with patterns and architectures applicable to a range of challenges, and a fresh perspective on using Python to interrogate complex societal phenomena.

Why is this relevant?
With current political and social debate often hinging on short bursts of public concern, understanding how attitudes are formed and how to model them with Python has broad relevance for data scientists, AI practitioners, policy-makers, and anyone working on real-world text or network problems. The session prioritises transparent code, reproducibility, and technical detail, making it suitable for both academic and industry audiences.