The Banking Royal Commission: An Algorithmic Analysis of News Media Data
Title: The Banking Royal Commission: An Algorithmic Analysis of News Media Data
Speakers: Ashish Chopra, Hannah Harris & Sharon Li
The Banking Royal Commission (BRC) involved a formal inquiry into misconduct in the Banking, Superannuation and Financial Services industry. It generated significant news coverage and public debate, resulting in legal changes with implications for businesses, consumers, investors and the wider public. Between January 2016 and March 2019, an average of 11 news articles a day were published on the topic of the Banking Royal Commission, with peaks during public hearings and the interim and final reports.
Headlines during this period suggest that media framing of the BRC was focused on the negative conduct being revealed and the various failures and flaws in the financial and regulatory system that allowed such conduct to proliferate. As news media framing is likely to impact broader understandings and perspectives on the topics reported, critical analysis is essential. However, with more than 13,000 articles on the BRC over a three-year period, traditional desk-based analysis would be time-consuming and error prone. In an attempt to overcome these limitations, our project uses an algorithmic approach to analysing news media data on the BRC.
This project was conducted as part of the CLMR internship program. It uses Natural Language Processing (NLP) algorithms to analyse news media on the Banking Royal Commission (BRC); in doing so, the research contributes to understandings of the BRC news coverage, and the strengths and limitations of using NLP on news media data.