Social movements drive social changes through raising awareness of important issues. As one of the largest social movements, “Black Lives Matter” (BLM) movement highlighted race-related issues and aims to dismantle systemic racism and police brutality that disproportionately impacts minority communities. While BLM protests attract attention, the interplay between social movements and digital platforms warrants further exploration. Undoubtedly, movements spill into online spheres as digital platforms evolve into vital conduits for information sharing and connection. Simultaneously, social media enable organizers to mobilize backing and rally participants by leveraging digital resources. A reciprocal relationship sees online actions stimulate meaningful discussions with real-world reverberations. Together, offline activism and virtual platforms can accelerate positive change by amplifying important yet underrepresented narratives. Examining this bidirectional dynamic enhances understanding of how social progress materializes in the digital age.

The first study examines the influence of offline BLM protests on the sharing platform Airbnb. We find that occurrences of BLM demonstrations improve Airbnb listing occupancy rates and revenues, particularly for minority hosts. Using a difference-in-differences analysis with spatial matching, the results show that BLM protests positively impact Airbnb listing occupancy rates by 1.8% and revenues by 5.43% in the subsequent six-month period. Furthermore, we discover that African American hosts, identified through a deep learning algorithm analyzing host profiles, benefit from an even greater revenue increase beyond the average effect of BLM on host performance. However, incidents of police brutality reduce the positive impact of BLM on performance, highlighting how institutional violence can dampen the transformative potential of social movements. Our research contributes to understanding how large-scale movements can bring about economic performance changes in related markets. This challenges the prevailing perspective that racial bias is persistent and self-reinforcing on platform-based sharing markets.

The second study examines the online resources mobilized by social movement organizations on the social media platform X (previously known as Twitter). Building upon the classical theory of Resource Mobilization, we addressed how the number of BLM social movement posts and sentiment in social media posts functions as critical digital resources that facilitate further mobilization. We used the Zero-Inflated Negative Binomial (ZINB) model and natural language processing techniques to analyze the effect of the number of tweets and sentiments expressed in tweets from BLM social movement organizations (SMOs) X accounts. In sentiment analysis, we used both the traditional lexicon-based model VADER and the state-of-the-art deep learning model GPT to measure the sentiment perceived in the specific domain of tweets from SMOs. Findings show more posts from the SMOs correlated with increased protests and participation in that region and time period. Furthermore, we find that both positive and negative sentiments conveyed in the tweets correlate with protests and participants, defying the stereotypes and portrayals that social movement participants act solely because of anger and grievance. Analysis of these factors enhances understanding of digital resources’ importance and offers strategic insights for maximizing movement success.

The third study examines the effect of police brutality cases on the sharing economy which constitutes an important part of the modern economy. Specifically, this study investigates how such cases influence home-sharing platform Airbnb listing performance. We constructed a comprehensive and novel data set of more than ten million listing-month records using location-matching of previous and following six-month of a police brutality case across 1,026 cities among 50 States and the District of Columbia from October 2014 to December 2019. Using this data set, we obtained a unique setting to study the local influence of police brutality cases and how the impact has changed over time in influenced areas. We found that the occurrences of police brutality cases significantly and positively influenced the occupancy rate and revenue of Airbnb listings within 0.5 miles of the case location over the following six months. Across a range of heterogeneities, the performance of Airbnb listings increases as case number decreases, number of African American victim increases, and number of African American host increases. This relationship between police brutality cases and Airbnb listing performance is exacerbated in non-Black neighborhoods. We discuss our results and implications for future research and policy.