arXiv subjects

Lynn Wu

Publications and source records attributed to Lynn Wu.

6 recordsLinked to original sources

arXiv2026

The Double-edged Effect of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange

We investigate how banning generative artificial intelligence-generated content (AIGC) affects knowledge seeking, knowledge contribution, and contribution efficiency in online question-and-answer communities. After the launch of ChatGPT in late November 2022, several Stack Exchange communities implemented official bans on AIGC over concerns such as less reliable and socially engaged content. Leveraging data from the full network of Stack Exchange communities, we employ a difference-in-differences (DID) approach to examine the impacts of these bans. Our results reveal a double-edged impact: while the AIGC ban increases knowledge seeking, as evidenced by a higher volume of posted questions, it simultaneously reduces contribution efficiency, reflected in a lower proportion of questions receiving satisfactory answers within the expected time frame. Notably, these impacts are only evident in non-STEM communities. We take a socio-technical perspective to explore information reliability and social interactivity as two plausible underlying factors driving the observed changes. Our mechanism exploration reveals that the AIGC ban spurs question volume in topics where AIGC is less reliable and where social interaction is highly expected. In contrast, the ban hampers answer efficiency in communities where LLMs are capable of producing reliable answers and where social interactivity is minimal. Additionally, our results indicate the increased human involvement from knowledge seekers and contributors following the ban. They adapt their behavior by posting questions and answers that are more informationally rich and socially engaging. Overall, our findings offer actionable implications for platform managers, community moderators, and policymakers of online Q&A communities.

cs.CY↗

arXiv2026

Large Language Models Polarize Ideologically but Moderate Affectively in Online Political Discourse

The emergence of large language models (LLMs) is reshaping how people engage in political discourse online. We examine how the release of ChatGPT altered ideological and emotional patterns in Reddit's largest political forum. Analysis of millions of comments shows that ChatGPT intensified ideological polarization: liberal-leaning authors posted increasingly liberal comments, while conservative-leaning authors posted increasingly conservative comments. Multiple falsification tests suggest that these findings are unlikely to be driven by contemporaneous events, such as the 2022 U.S. midterm elections, or by broader platform-wide trends in political polarization. Mechanism tests show that this shift does not stem from the creation of more persuasive or ideologically extreme original content using LLM. Instead, it originates from the tendency of LLM-assisted comments to echo and reinforce the original post's viewpoint, a pattern consistent with algorithmic sycophancy. Yet, despite growing ideological divides, affective polarization, measured by hostility and toxicity, declined. These findings reveal that LLMs can simultaneously deepen ideological separation and foster more civil exchanges, challenging the long-standing assumption in literature that extremity and incivility necessarily move together.

econ.GN↗

arXiv2025

Artificial Intelligence, Lean Startup Method, and Product Innovations

Although AI has the potential to drive significant business innovation, many firms struggle to realize its benefits. We examine how the Lean Startup Method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping focused on developing minimum viable products, and controlled experimentation, focused on rigorous testing such as AB testing. We find that LSM complements discovery oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using AB testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high quality product in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct, as different AI capabilities require distinct organizational processes to achieve optimal outcomes.

econ.GN↗

arXiv2025

Social Media Can Reduce Misinformation When Public Scrutiny is High

Misinformation poses a growing global threat to institutional trust, democratic stability, and public decision-making. While prior research has often portrayed social media as a channel for spreading falsehoods, less is known about the conditions under which it may instead constrain misinformation by enhancing transparency and accountability. Here we show this dual potential in the context of local governments' GDP reporting in China, where data falsifications are widespread. Analyzing official reports from 2011 to 2019, we find that local governments have overstated GDP on average. However, after adopting social media for public communications, the extent of misreporting declines significantly but only in regions where the public scrutiny over political matters is high. In such regions, social media increases the cost of misinformation by facilitating greater information disclosure and bottom-up monitoring. In contrast, in regions with low public scrutiny, adopting social media can exacerbate data manipulation. These findings challenge the prevailing view that social media primarily amplifies misinformation and instead highlight the importance of civic engagement as a moderating force. Our findings show a boundary condition for the spread of misinformation and offer insights for platform design and public policy aimed at promoting accuracy and institutional accountability.

econ.GN↗

arXiv2025

Impact of Engagement Allocation Across Social Platform Modalities on E-Commerce Performance

Firms increasingly operate across multiple social media platforms, yet it remains unclear whether diversifying engagement across platforms enhances performance or simply fragments marketing efforts. We examine how the allocation of user engagement across platforms affects e commerce sales performance. Using panel data on approximately 2,000 leading U.S. online retailers from 2012 to 2019, combined with detailed engagement measures across five major social media platforms, we construct firm year indicators of engagement diversification and explore how they relate to sales performance. We find that greater diversification in engagement allocation is associated with significantly higher web sales. Importantly, this effect is not driven by platform adoption breadth or overall engagement volume. Rather than increasing traffic quantity, diversification improves conversion rates and enhances traffic quality. Mechanism analyses reveal that these performance gains stem from cross modality complementarities: engagement distributed across heterogeneous content modalities (image, video, and mixed) generates reinforcing brand exposure, whereas diversification across platforms within the same modality yields limited benefits. Furthermore, the positive effects of diversification arise only when there is sufficient overlap in audiences across platforms, providing additional evidence for a memory-reinforcement mechanism. Taken together, these findings document the importance of engagement allocation structure and highlight the role of cross-modality complementarities in multi platform digital marketing strategies.

econ.GN↗

arXiv2019

PILOT: Password and PIN Information Leakage from Obfuscated Typing Videos

This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work [4]. In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper.

cs.CR↗