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Digital Experimentation Methods
1. Estimating Effects of Long-Term Treatments
Estimating the effects of long-term treatments in A/B testing presents a significant challenge. Such treatments - including updates to product functions, user interface designs, and recommendation algorithms - are intended to remain in the system for a long period after their launches. On the other hand, given the constraints of conducting long-term experiments, practitioners often rely on short-term experimental results to make product launch decisions. It remains an open question how to accurately estimate the effects of long-term treatments using short-term experimental data. To address this question, we introduce a longitudinal surrogate framework. We show that, under standard assumptions, the effects of long-term treatments can be decomposed into a series of functions, which depend on the user attributes, the short-term intermediate metrics, and the treatment assignments. We describe the identification assumptions, the estimation strategies, and the inference technique under this framework. Empirically, we show that our approach outperforms existing solutions by leveraging two real-world experiments, each involving millions of users on WeChat, one of the world's largest social networking platforms.
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Computational Social Science & New Social Media
1. Emotions in Online Content Diffusion
This study examines how emotional expression, specifically discrete emotional expression (i.e., expressions of anxiety, sadness, anger, disgust, love, joy, surprise, and anticipation), lead to differential diffusion of online content in social media networks. We conducted an analysis on a random sample of 387,486 online articles and their corresponding diffusion cascades, involving over six million unique individuals, on a large-scale online social network, WeChat. Discrete emotional expressions in these articles were identified using a newly generated, domain-specific, and up-to-date emotion lexicon. Our investigation focused on the structural properties of diffusion cascades (such as size, depth, maximum breadth, and structural virality), as well as individual characteristics (including age, gender, and network degree) and social ties (both strong and weak) involved in the cascading process. We employed various econometric model specifications to robustly demonstrate the relationships between discrete emotional expressions and the diffusion of online articles. Our findings reveal that articles expressing more anxiety, love, and surprise reach a larger number of individuals and diffuse more deeply, broadly, and virally. In contrast, expressions of anger, sadness, and joy exhibit the opposite effect. Expressions of disgust are associated with greater viral diffusion, while expressions of anticipation are associated with reduced levels of depth and virality. Additionally, we find that articles with different emotional expressions tend to spread differently based on individual characteristics and social ties. These findings provide valuable insights into the diffusion and regulation of online content from the perspectives of emotional expressions and social networks.
2. Algorithmic vs. Friend-based Recommendations in Shaping Novel Content Engagement
This study identifies the differential impact of algorithmic and friend-based recommendations—the two predominant mechanisms of online content recommendation—on users' engagement with novel information, characterized as diverse and non-redundant. Our analysis focuses on the influence of different content recommended by algorithms versus friends and the role of social influence, specifically the impact of social cues inherent in friend-based recommendations. We designed and conducted a large-scale field experiment on WeChat, involving 2.1 million users. Participants were randomly assigned to one of three groups: a control group that received content recommended by algorithms, a treatment group that viewed content shared by friends with visible social cues (e.g., friends' ``likes''), and another treatment group that was exposed to friend-shared content with the social cues hidden. The findings reveal a general preference for less novel content across all groups. However, the presence of social cues significantly mitigated this trend, indicating that social influence can encourage engagement with more novel information. Despite algorithms tending to recommend content of lower novelty, users engage more with novel content when recommended by algorithms than by friends with and without social cues. The study also discovered significant variations in engagement with novel content among users of different genders, ages, and city tiers. These results carry important implications for the design of content recommendation systems and inform policymaking regarding the dissemination of information online.
3. The Strength of Weak Ties Differs in Broadcasting versus Broadcasting
Our work contributes to the contemporary weak tie theories by examining a crucial yet under-explored moderator: viral channels through which information is disseminated. We examine how users are exposed to novel information through social ties on social media networks, emphasizing the differential impact of two primary viral channels: direct messaging and broadcasting. These two channels are not only prevalent but also integral in both offline contexts and on social media platforms. Despite their significant roles, the specific outcomes and mechanisms that these channels serve in transmitting novel information through social networks are not yet fully understood. Direct messaging typically occurs through stronger social ties and represents a more intimate form of communication, often leading to more personalized and targeted information exchange. In contrast, broadcasting passively disseminates information to a user's entire network, indiscriminately reaching both strong and weak ties. The distinction between direct messaging and broadcasting can affect individuals' social transmission and interaction with novel information
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Social networks and Product Virality
1. Social Advertising Effectiveness: Evidence from A Large-scale Field Experiment
Study 1:
Most of the empirical evidence on social advertising effectiveness focuses on a single product at a time. As a result, little is known about how the effectiveness of social advertising varies across product categories or product characteristics. We, therefore, collaborated with a large online social network to conduct a randomized field experiment measuring social ads effectiveness across 71 products in 25 categories among more than 37 million users. We found some product categories, like clothing, cars and food exhibited significantly stronger social advertising effectiveness than other categories like financial services, electrical appliances, and mobile games. More generally, we found that status goods, which rely on status-driven consumption, displayed strong social advertising effectiveness. Meanwhile, social ads for experience goods, which rely on informational social influence, did not perform any better or worse than their theoretical counterpart search goods. Social advertising effectiveness also significantly varied across the relative characteristics of ad viewers and their friends shown in ads. Understanding the heterogeneous effects of social advertising across products can help marketers differentiate their social advertising strategies and lead researchers to more nuanced theories of social influence in product evaluation.
Study 2:
One unique feature of social advertising is the coexistence of public and private consumer responses. Social media allows users’ certain responses (e.g., likes) to be publicly revealed to one's social network. More importantly, public responses can also influence private responses (e.g., clicks). We, therefore, use data from a large-scale field experiment with a major social media platform (WeChat Moments) to investigate how the display of social cues (friends' likes) affects users' public (likes) and private responses (clicks) to social ads. We find that, on average, displaying the first social cue significantly enhances the liking rate and the clickthrough rate. Nevertheless, although showing additional social cues can further increase users' tendency to like an ad, it does not further increase the clickthrough rate. This empirical pattern is consistent with the interplay between informational and normative social influence in social advertising. Overall, we find that the coexistence of the two forces can enhance the conformity effect on the public liking response. However, when normative social influence dominates, a crowding-out effect on the private clicking response may occur. Our results have rich implications for advertisers and social media platforms in regard to the design of social advertising policies and social networks.