Behind the Screens: Using AI to Dissect Social Media Persuasion
AI's role in comprehending the complexities of human communication on social media presents unparalleled prospects for behavioral research. Our investigation focuses on how Large Language Models (LLMs) can transform our ability to recognize and analyze the persuasive strategies used by financial influencers across several platforms.
This approach to AI-driven insights demonstrates not only the integration of technology and human behavior but also the specific methodology we used to extract and classify massive volumes of content. By concentrating on the particular use of LLMs in social media listening, we highlight its crucial significance in providing a new viewpoint on how digital communication influences consumer perceptions.
The Objective
We had the challenge of understanding the impact social media has on retail investors. A part of our research was aimed at analyzing the types of persuasive techniques influencers employ to encourage their audience to make a particular financial decision. The study focused on influencers across social media platforms including YouTube, X, TikTok, Instagram, and Reddit. This type of work usually follows two main steps:
- Extract the available social media content and clean the data to be analyzed.
- Use traditional natural language processing techniques and algorithms to classify the data and extract insights.
Large Language Models (LLMs), such as ChatGPT and LLaMA, are algorithms that excel in classifying and analyzing text—which, in this case, are the millions of words we gathered from social media platforms. LLMs can aid in both of these steps: cleaning and classifying the data.
References
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17).
- Balaji, M. S., Jiang, Y., & Jha, S. (2021). Nanoinfluencer marketing: How message features affect credibility and behavioral intentions. Journal of Business Research, 136, 293-304.
About the Author
Jerónimo Kanahuati
Jero is a Consultant at The Decision Lab with a passion for artificial intelligence and behavioral science. Prior to joining The Decision Lab he founded a startup in Mexico to develop apps for kids to encourage education, and developing web scraping bots. He also worked at Google as an account manager and technical specialist focused on ad placement across Google's products. Jero has a bachelor's degree in engineering and a postgraduate specialty degree in operations from Universidad Panamericana in Mexico City.
About us
We are the leading applied research & innovation consultancy
Our insights are leveraged by the most ambitious organizations
“
I was blown away with their application and translation of behavioral science into practice. They took a very complex ecosystem and created a series of interventions using an innovative mix of the latest research and creative client co-creation. I was so impressed at the final product they created, which was hugely comprehensive despite the large scope of the client being of the world's most far-reaching and best known consumer brands. I'm excited to see what we can create together in the future.
Heather McKee
BEHAVIORAL SCIENTIST
GLOBAL COFFEEHOUSE CHAIN PROJECT
OUR CLIENT SUCCESS
$0M
Annual Revenue Increase
By launching a behavioral science practice at the core of the organization, we helped one of the largest insurers in North America realize $30M increase in annual revenue.
0%
Increase in Monthly Users
By redesigning North America's first national digital platform for mental health, we achieved a 52% lift in monthly users and an 83% improvement on clinical assessment.
0%
Reduction In Design Time
By designing a new process and getting buy-in from the C-Suite team, we helped one of the largest smartphone manufacturers in the world reduce software design time by 75%.
0%
Reduction in Client Drop-Off
By implementing targeted nudges based on proactive interventions, we reduced drop-off rates for 450,000 clients belonging to USA's oldest debt consolidation organizations by 46%
