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  • Digital Experimentation Methods

1. Estimating Effects of Long-Term Treatments 

  • Shan Huang*, Chen Wang, Yuan Yuan, Jinglong Zhao & Jingjing Zhang

  • The Twenty-Fourth ACM Conference on Economics and Computation (EC'23) 

  • under revision at Management Science

  • presented in Conference on Digital Experimentation (CODE) 2022, Marketing Innovation Workshop 2023, American Causal Inference Conference (ACIC) 2023, Revenue Management and Pricing (RMP) 2023, Conference on Information Systems and Technology (CIST) 2023, Marketing Science 2024

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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. 

2. Enhancing External Validity of Experiments with Ongoing Sampling 

  • Chen Wang, Shichao Han, & Shan Huang*

  • The Twenty-Fourth ACM Conference on Economics and Computation (EC'24)

  • presented in China India Insights (CIIP) 2024

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Subjects often arrive and participate in experiments sequentially, potentially compromising the external validity of results due to temporal shifts in sample characteristics over time. This issue is especially pronounced in short-duration experiments, such as A/B tests. To mitigate this, we introduce a novel framework that adapts to the dynamic nature of participant engagement to improve external validity. Our method segments the sampling process into three distinct stages, each with different degrees of generalizability.By employing survival analysis, we devise a heuristic function to identify these stages and create stage-specific estimators for the average treatment effect. We validate our approach using synthetic data and 600 real-world experiments conducted on Weixin (WeChat), a leading social media platform.

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  • Computational Social Science & New Social Media

1. Emotions in Online Content Diffusion

  • Yifan Yu, Shan Huang*, Yuchen Liu, & Yong Tan

  • under major revision at Information Systems Research

  • presented in Conference on Digital Experimentation (CODE) 2019, Marketing Science Conference 2020, IC2S2 (International Conference on Computational Social Science) 2020

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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.

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2. Algorithmic vs. Friend-based Recommendations in Shaping Novel Content Engagement

  • Shan Huang* & Yi Ji

  • The Twenty-Fourth ACM Conference on Economics and Computation (EC'24)

  • presented at China India Insights (CIIP) 2024

 

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 a major social media platform. 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 Across Viral Channels

  • Shan Huang*, Yuan Yuan & Yi Ji

  • under review​

 

The diffusion of novel information through social networks is essential for dismantling echo chambers and promoting innovation. Our study examines how two major types of viral channels, specifically Direct Messaging (DM) and Broadcasting (BC), impact the well-known “strength of weak ties” in disseminating novel information across social networks. We conducted a large-scale empirical analysis, examining the sharing behavior of 500,000 users over a two-month period on a major social media platform. Our results suggest a greater capacity for DM to transmit novel information compared to BC, although DM typically involves stronger ties. Furthermore, the “strength of weak ties” is only evident in BC, not in DM where weaker ties do not transmit significantly more novel information. Our mechanism analysis indicates that the content selection by both senders and recipients, contingent on tie strength, contributes to the observed differences between these two channels. These findings expand both our understanding of contemporary weak tie theory and our knowledge of how to disseminate novel information in social networks.

  • Social networks and Product Virality

1. Social Advertising Effectiveness: Evidence from A Large-scale Field Experiment

  • Shan Huang, Sinan Aral, Yu Hu & Erik Brynjolfsson.

  • Marketing Science, 39(6), 1142-1165.

  • presented in INFORMS Annual Meeting 2018, Conference on Digital Experimentation (CODE) 2016, Conference on Digital Experimentation (CODE) 2017

 

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.

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2. Do More "Likes" Lead to More Clicks? Evidence from a Field Experiment on Social Advertising

  • Shan Huang & Song Lin 

  • under revision at Journal of Marketing

  • presented in Conference on Digital Experimentation (CODE) 2017, WISE 2018

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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.

3. Customer-Product Matches in Online Social Referrals

  • Shan Huang* & Yifan Yu,

  • under reject and resubmit at Management Science

  • presented at Conference on Information Systems and Technology (CIST) 2022

 

New social media has been enlarging the impact of social referrals on product diffusion. There is, however, no direct evidence on how effectively referrers match products with referred customers that, in turn, leads to high returns for online social referrals. To answer this question, we analyzed 137,622 referrals of 20,169 instant apps from 11,668 referrers to 84,166 recipients (those referred), and 1,141,363 (100 per referrer) unique contacts randomly selected from referrers' local social networks excluding the recipients. We leveraged a state-of-the-art graph embedding framework to estimate users' latent preferences for the apps based on a massive amount of user-app historical usage sequences. We find that referrers recommend products to contacts with substantially stronger preferences for the referred products than the non-referred contacts have. Referrers' contacts have significantly greater preferences for the referred products than do their non-contacts. These results confirm the effectiveness of active (actively screening and selecting contacts to match products) and passive (homophily-based) matching in online social referrals. We also find that referrers exhibit the strongest preferences for the referred products. The matching outperforms among narrow-appeal products and for more engaged referrers and those with a smaller local social network. Our findings shed light on the mechanisms and management of social referrals.

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