arXiv subjects

Yakov Bart

Publications and source records attributed to Yakov Bart.

6 recordsLinked to original sources

arXiv2026

AI in Search Reduces Publisher Referrals Without Improving User Experience: Experimental Evidence

The integration of generative AI into web search delivers synthesized answers to user queries, changing how people navigate and assess information, while raising concerns about the downstream impacts on publishers who supply the underlying content. We conduct a preregistered field experiment (N=1,100) on Google Search, the dominant online search platform, to estimate the causal effects of AI Overviews and AI Mode on user behavior, perceptions, and publisher traffic. We show that removing AI Overviews and AI Mode increases click-through rates to publishers, while an AI Mode-only experience reduces click-through rates and erodes user experience and trust in information found on Google. These findings show that integrating generative AI into web search reshapes online attention, with economic consequences for the online publishers that sustain both search platforms and the overall information ecosystem.

cs.IR↗

arXiv2026

AI-Mediated Hiring and the Job Search of Blind and Low-Vision Individuals

Blind and low-vision (BLV) individuals face high unemployment rates. The job search is becoming harder as more employers use AI-driven systems to screen resumes before a human ever sees them. Such AI systems could inadvertently further disadvantage BLV job seekers, introducing additional barriers to an already difficult process. We lack understanding of BLV job seekers' experiences in today's AI-driven hiring ecosystem. Without such understanding, we risk designing technologies that create new systemic barriers for BLV job seekers rather than providing support. To this end, we conducted interviews with 17 BLV job seekers and analyzed their experiences with AI-powered hiring systems. We found that AI hiring systems misrepresented their professional identities and created dehumanizing interactions. To level the playing field, BLV job seekers used strategic counter-navigation: they deployed their own tools to bypass algorithmic screening and built peer networks to share AI literacy. They also practiced 'strategic refusal', choosing to avoid certain AI systems to regain their agency. Unlike prior work that frames job search as an individualistic activity, or one focused on being compliant with employer needs, we use the interdependence framework to argue that for BLV people, job search is an interdependent process. We offer design recommendations for AI-mediated tools that center disability perspectives and support interdependencies in job search.

cs.HC↗

arXiv2025

AdSum: Two-stream Audio-visual Summarization for Automated Video Advertisement Clipping

Advertisers commonly need multiple versions of the same advertisement (ad) at varying durations for a single campaign. The traditional approach involves manually selecting and re-editing shots from longer video ads to create shorter versions, which is labor-intensive and time-consuming. In this paper, we introduce a framework for automated video ad clipping using video summarization techniques. We are the first to frame video clipping as a shot selection problem, tailored specifically for advertising. Unlike existing general video summarization methods that primarily focus on visual content, our approach emphasizes the critical role of audio in advertising. To achieve this, we develop a two-stream audio-visual fusion model that predicts the importance of video frames, where importance is defined as the likelihood of a frame being selected in the firm-produced short ad. To address the lack of ad-specific datasets, we present AdSum204, a novel dataset comprising 102 pairs of 30-second and 15-second ads from real advertising campaigns. Extensive experiments demonstrate that our model outperforms state-of-the-art methods across various metrics, including Average Precision, Area Under Curve, Spearman, and Kendall. The dataset and code are available at https://github.com/ostadabbas/AdSum204.

cs.CV↗

arXiv2025

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation

Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating ``believable'' human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPERA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. OPERA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPERA, we establish the first benchmark to evaluate how well current LLMs can predict a specific user's next action and rationale with a given persona and history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human.

cs.CL↗

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↗

arXiv2024

COVID-19 Demand Shocks Revisited: Did Advertising Technology Help Mitigate Adverse Consequences for Small and Midsize Businesses?

Research has investigated the impact of the COVID-19 pandemic on business performance and survival, indicating particularly adverse effects for small and midsize businesses (SMBs). Yet only limited work has examined whether and how online advertising technology may have helped shape these outcomes, particularly for SMBs. The aim of this study is to address this gap. By constructing and analyzing a novel data set of more than 60,000 businesses in 49 countries, we examine the impact of government lockdowns on business survival. Using discrete-time survival models with instrumental variables and staggered difference-in-differences estimators, we find that government lockdowns increased the likelihood of SMB closure around the world but that use of online advertising technology attenuates this adverse effect. The findings show heterogeneity in country, industry, and business size, which we discuss and is consistent with theoretical expectations.

econ.GN↗