Opportunities and Threats for Brands in the Social Media Ecosystem

In today’s digital environment, social media has become a central channel for understanding consumers’ opinions, preferences, and behaviors. These platforms enable individuals to connect with others who share similar interests, which leads to repeated exposure to brand-related content within their personal networks. This can benefit brands by strengthening engagement and fostering customer loyalty. At the same time, the same mechanisms that support rapid information sharing can also facilitate the spread of misinformation, distort public perception, and increase reputational risk. In parallel, recent advances in generative artificial intelligence offer new tools for anticipating consumer responses, particularly in contexts where direct feedback is limited or delayed. This thesis examines these three distinct but related areas, social media echo chambers, misinformation, and generative AI, through a series of three essays. The first essay, “Brand Echo Chambers,” extends the concept of echo chambers beyond political discourse to explore their relevance in the context of brands. It investigates how consumers who are surrounded by others with similar brand preferences perceive and engage with brands. Drawing on data from over 870,000 users following 179 brands on X (formerly Twitter), I introduce two distinct metrics to assess brand echo chambers. The findings show that consumers in stronger brand echo chambers are more likely to discuss the brand, express positive sentiment, and display emotional attachment, while being less exposed to competing brands. The second essay, “The Cost of Misinformation for Brands: The Case of Bonduelle,” examines misinformation targeting brands in digital environments, the role of automated accounts in amplifying false claims, and the resulting impact on brand performance. Drawing on social media data from a real-world misinformation campaign, I show that bots play a significant role in amplifying false narratives and that this amplification coincides with a measurable decline in the targeted firm’s stock price. This study underscores the strategic risks that misinformation poses to brands and the influence of bots in shaping public perception. The third essay, “Voices from the Future: Generative AI for Forecasting the Success of Artistic Works,” explores whether synthetic content produced by large language models can serve as a proxy for user feedback prior to a product’s release. Focusing on cultural products (books, music, and films) I develop a two-step generation pipeline that first extracts media features and metadata of each product, then produces simulated user reviews. I evaluate the semantic and sentiment similarity between AI-generated and real user feedback and demonstrate that features derived from synthetic reviews can predict post-release user ratings. This essay presents a novel application of generative AI for early-stage market forecasting, especially in situations where traditional feedback is delayed or unavailable. Together, these essays contribute to a broader understanding of how digital environments enable insight into consumer perceptions and behaviors, facilitate the circulation of false information against brands, and support the use of emerging technologies to anticipate customers’ feedback. More broadly, it highlights the evolving challenges and opportunities that arise as firms and consumers interact within increasingly complex and algorithmically mediated systems.