arXiv subjects

Mahyar Habibi

Publications and source records attributed to Mahyar Habibi.

4 recordsLinked to original sources

arXiv2025

Two-Sided Prioritized Ranking: A Coherency-Preserving Design for Marketplace Experiments

Online marketplaces frequently run pricing experiments in environments where users choose from a list of items. In these settings, items compete for users' limited attention and demand, creating interference among items within a list: Changing prices for any item can affect the demand for others, biasing estimates from item-level A/B tests. Besides, a key consideration in pricing experiments is preserving platform coherency across prices and item availability. This requirement rules out experimental designs such as user-level A/B tests as they violate platform coherency. We propose Two-Sided Prioritized Ranking (TSPR) to estimate the total average treatment effect of price changes in such settings. TSPR exploits position bias in ranked search results to create variation in treatment exposure without compromising coherency. TSPR randomizes both users and items and reorders ranked lists, prioritizing treated items for one group of users and untreated items for the other. All users see the same items at consistent prices, but differ in exposure to treatment as they pay disproportionate attention across ranks. In semi-synthetic simulations based on Expedia hotel search data, TSPR outperforms baseline coherency-preserving experiment designs by reducing estimation bias and providing sufficient statistical power.

econ.EM↗

arXiv2025

Open Sourcing GPTs: Economics of Open Sourcing Advanced AI Models

This paper explores the economic underpinnings of open sourcing advanced large language models (LLMs) by for-profit companies. Empirical analysis reveals that: (1) LLMs are compatible with R&D portfolios of numerous technologically differentiated firms; (2) open-sourcing likelihood decreases with an LLM's performance edge over rivals, but increases for models from large tech companies; and (3) open-sourcing an advanced LLM led to an increase in research-related activities. Motivated by these findings, a theoretical framework is developed to examine factors influencing a profit-maximizing firm's open-sourcing decision. The analysis frames this decision as a trade-off between accelerating technology growth and securing immediate financial returns. A key prediction from the theoretical analysis is an inverted-U-shaped relationship between the owner's size, measured by its share of LLM-compatible applications, and its propensity to open source the LLM. This finding suggests that moderate market concentration may be beneficial to the open source ecosystems of multi-purpose software technologies.

econ.GN↗

arXiv2024

The Content Moderator's Dilemma: Removal of Toxic Content and Distortions to Online Discourse

There is an ongoing debate about how to moderate toxic speech on social media and the impact of content moderation on online discourse. This paper proposes and validates a methodology for measuring the content-moderation-induced distortions in online discourse using text embeddings from computational linguistics. Applying the method to a representative sample of 5 million US political Tweets, we find that removing toxic Tweets alters the semantic composition of content. This finding is consistent across different embedding models, toxicity metrics, and samples. Importantly, we demonstrate that these effects are not solely driven by toxic language but by the removal of topics often expressed in toxic form. We propose an alternative approach to content moderation that uses generative Large Language Models to rephrase toxic Tweets, preserving their salvageable content rather than removing them entirely. We show that this rephrasing strategy reduces toxicity while minimizing distortions in online content.

cs.SI↗

arXiv2024

DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods

Social scientists increasingly use demographically stratified social media data to study the attitudes, beliefs, and behavior of the general public. To facilitate such analyses, we construct, validate, and release publicly the representative DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures. We enrich the user data with high-quality labels for gender, age, and location. DADIT enables us to train and compare the performance of various state-of-the-art models for the prediction of the gender and age of social media users. In particular, we investigate if tweets contain valuable information for the task, since popular classifiers like M3 don't leverage them. Our best XLM-based classifier improves upon the commonly used competitor M3 by up to 53% F1. Especially for age prediction, classifiers profit from including tweets as features. We also confirm these findings on a German test set.

cs.CL↗