Date: Thursday 22 June, 2023
Time: 2pm – 3pm presentation, followed by 3pm – 3:30pm Q&A
Format:
In-person: Z9-607, Kelvin Grove campus
Online: via Zoom (Meeting ID: 843 1981 879 , Password:776919)
RSVP: Via Outlook Calendar acceptance
Associate Professor Fabio Giglietto is visiting DMRC from Monday 19 to Friday 24 June. Fabio is an associate professor at the university of Urbino, Italy. His research focuses on Harnessing OpenAI Models for Topic Modelling in Social Media Analysis of Italian Elections.
Abstract:
Unveiling Political Discourse: Harnessing OpenAI Models for Topic Modeling in Social Media Analysis of Italian Elections
The advent of OpenAI’s ChatGPT signaled a watershed moment in our research methodologies, particularly in efficiently understanding topics discussed on social media. While similar models, such as BERT, existed, they necessitated a fine-tuning process to adapt to specific languages and domains—a process that is both computationally demanding and time-consuming. Moreover, despite a plethora of domain-oriented pre-trained models, selecting an optimal model often proves challenging and can lead to unsatisfactory results, necessitating a specialized training session for the task at hand.
In contrast, OpenAI’s models are versatile, excelling in summarization and classification tasks across a multitude of domains and languages. Importantly, these models can be customized to meet researchers’ needs by fine-tuning them—a process that is straightforward, leverages server-side computational resources, and is both rapid and cost-effective.
In this presentation, I will explore the three distinct methods we employed using OpenAI models to identify the most salient topics circulated via Facebook links in the run-up to the two most recent Italian general elections. I will elaborate on our techniques for constructing a classifier to detect political links shared on Facebook, performing a cluster analysis on the document embeddings provided by OpenAI’s API embedding endpoint for political links, and autonomously labeling the identified clusters. We also utilized Meta’s URL Shares Dataset to characterize each cluster based on their exposure and interaction patterns.
Although our methods were applied to the context of the Italian elections, they are easily adaptable to other countries and scenarios. While this talk is predominantly methodological, it will also touch on the broader social implications of this approach, including its impact on future election strategies, online influence operations, and public policy decision-making.
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