In the wake of the global COVID-19 pandemic, the hospitality industry has experienced unprecedented upheaval, prompting extensive scrutiny into how guest behavior and preferences have shifted in short and long-term contexts. A recent study spearheaded by Ding, Bao, Li, and colleagues offers a compelling exploration into the lasting impact of the pandemic on Airbnb users, employing advanced structural topic modeling (STM) to analyze a vast array of guest reviews from 2020 through mid-2024. This research illuminates evolving consumer priorities, underscoring how crises catalyze enduring transformations in travel and accommodation patterns.
At the heart of this investigation is the utilization of Airbnb reviews sourced via InsideAirbnb.com, an open data platform aggregating user feedback across major urban centers. While such a dataset provides a rich tapestry of guest experiences, it also presents intrinsic challenges. Selection bias looms large, as the voluntary nature of review submissions perpetuates a skewed landscape—those with particularly positive or negative encounters are more inclined to share their impressions, whereas moderate or neutral experiences might remain undocumented. This imbalance complicates efforts to draw fully representative conclusions about broader consumer sentiment.
Moreover, the emotional valence expressed within reviews can be inherently distorted due to Airbnb’s reciprocal review mechanism. Guests often modulate their language to maintain amicable relations with hosts or to mitigate repercussions, potentially inflating positivity or suppressing genuine dissatisfaction. Such sentiment bias compounds the difficulty in parsing authentic guest feelings, especially when feedback is captured immediately after check-out, potentially reflecting transient impressions rather than long-term evaluations of their stay experience.
Addressing these limitations, the researchers advocate for a multifaceted methodological approach in future studies. Integrating direct guest surveys and structured interviews can enrich qualitative insight, enabling triangulation of data sources that transcend the constraints of platform-specific review systems. Cross-referencing Airbnb feedback with alternative review platforms such as TripAdvisor could uncover discrepancies in sentiments that signal deeper complexities in customer perception dynamics.
The core analytical tool, structural topic modeling, enables the distillation of prevalent themes embedded within thousands of reviews, revealing focal areas of guest attention during a period marked by intense uncertainty and evolving expectations. However, STM inherently lacks sensitivity to the emotional intensity underlying textual content. For example, multiple mentions of “cleanliness” may carry vastly different emotive weights, ranging from mild approval to acute dissatisfaction. To surmount this, future research is encouraged to integrate sophisticated sentiment analysis frameworks—such as VADER or deep neural network approaches—which can quantify emotional tone with higher fidelity, contributing to a more nuanced understanding of user experience.
Temporal considerations also merit critical attention. The dataset traverses the pandemic timeline, encompassing periods of stringent restrictions, gradual reopening, and a tentative recovery phase. While this range captures significant behavioral fluctuations influenced by health concerns and policy shifts, the persistence of COVID-era preferences remains uncertain as economic conditions stabilize and global mobility increases. Extending longitudinal analyses beyond mid-2024 will be vital to discern whether heightened demands for hygiene protocols and flexible booking options are enduring features of post-pandemic tourism or transient adaptations.
Geographical and cultural diversity represent another axis warranting deeper exploration. The study primarily draws upon reviews from four major Western cities, offering a window into these markets but potentially overlooking critical regional variations. Cultural norms and socio-economic contexts heavily shape traveler expectations—preferences regarding affordability, hospitality nuances, and community engagement may diverge markedly across continents. Incorporating data from underrepresented regions such as Asia, South America, and Africa in subsequent research endeavors would enrich the global applicability of the findings and illuminate unique market dynamics.
Beyond individual household behavior, macroeconomic factors increasingly influence travel decisions in the evolving hospitality ecosystem. Inflationary pressures, labor market fluctuations, and surging operational costs are reshaping host strategies and pricing models. As Airbnb listings adjust prices accordingly, affordability may ascend as a primary determinant of guest choice, potentially superseding previously dominant considerations like enhanced sanitation and flexible cancellation policies. A comprehensive understanding of guest decision-making, therefore, necessitates integrating economic indicators and market conditions into analytical frameworks.
The interplay between data source selection, analytical technique, and contextual framing embodies both the promise and complexity of studying consumer behavior during disruptive events. This research exemplifies how computational text analysis can surface latent themes from large, unstructured datasets, yielding insights unattainable through traditional surveys alone. At the same time, it highlights the imperative to complement such methods with sentiment and socio-economic analyses to capture a fuller spectrum of user experience.
From a practical standpoint, these findings provide valuable guidance for hosts, platform designers, and policymakers aiming to adapt to shifting trends in user expectations. Understanding the nuanced evolution in guest priorities—especially those influenced by the pandemic—can inform targeted improvements in service quality, communication strategies, and platform features that enhance trust and satisfaction.
Furthermore, the study underscores the need for ongoing monitoring of user feedback as travel behaviors continue to evolve in response to emerging global health considerations, regulatory environments, and economic realities. Continuous, iterative data collection and analysis will ensure that stakeholders remain attuned to dynamic consumer landscapes, enabling agile responses in a rapidly transforming sector.
In essence, the COVID-19 crisis has served as a catalyst for fundamental change in the sharing economy, particularly in peer-to-peer lodging platforms like Airbnb. This research not only chronicles a period of transition but also beckons further inquiry into the durability of these changes. It challenges scholars and industry practitioners alike to embrace complexity and interdisciplinarity in untangling the intertwined threads of health anxieties, economic challenges, and cultural shifts shaping modern travel.
As the global community navigates the uncertain path to a post-pandemic future, studies such as this illuminate critical avenues for inquiry and innovation. By marrying robust computational tools with deep contextual understanding, researchers can chart more accurate maps of consumer sentiment, facilitating a more resilient and responsive hospitality landscape in an era defined by change.
The future of accommodation preferences will likely rest on a delicate balance among competing demands: safety, affordability, flexibility, and authentic cultural experiences. Deciphering the relative weight and interplay of these factors demands concerted efforts in data collection, methodological refinement, and culturally sensitive analysis, ensuring that the industry’s evolution aligns with guest needs in a complex, interconnected world.
Ultimately, this investigation represents a vital step toward closing the gap between raw data and meaningful insight, empowering stakeholders to harness the transformative potential inherent in crisis-driven change. The lessons learned from pandemic-era behavioral shifts will resonate far beyond Airbnb, offering paradigmatic guidance for innovation across the broader tourism and hospitality sectors.
Subject of Research:
The lasting influence of COVID-19 on Airbnb users’ preferences and behaviors, analyzed through structural topic modeling of guest reviews during the pandemic and recovery period.
Article Title:
From crisis to change: exploring the lasting influence of COVID-19 on Airbnb users through structural topic modeling.
Article References:
Ding, K., Bao, Y., Li, L. et al. From crisis to change: exploring the lasting influence of COVID-19 on Airbnb users through structural topic modeling. Humanit Soc Sci Commun 12, 781 (2025). https://doi.org/10.1057/s41599-025-05153-8
Image Credits: AI Generated
