arXiv subjects

Daniel Barkoczi

Publications and source records attributed to Daniel Barkoczi.

3 recordsLinked to original sources

arXiv2026

Celebrity messages reduce online hate and limit its spread

Online hate spreads rapidly, yet little is known about whether preventive and scalable strategies can curb it. We conducted the largest randomized controlled trial of hate speech prevention to date: a 20-week messaging campaign on X in Nigeria targeting ethnic hate. 73,136 users who had previously engaged with hate speech were randomly assigned to receive prosocial video messages from Nigerian celebrities. The campaign reduced hate content by 2.5% to 5.5% during treatment, with about 75% of the reduction persisting over the following four months. Reaching a larger share of a user's audience reduced amplification of that user's hate posts among both treated and untreated users, cutting hate reposts by over 50% for the most exposed accounts. Scalable messaging can limit online hate without removing content.

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arXiv2017

A preference elicitation interface for collecting dense recommender datasets with rich user information

We present an interface that can be leveraged to quickly and effortlessly elicit people's preferences for visual stimuli, such as photographs, visual art and screensavers, along with rich side-information about its users. We plan to employ the new interface to collect dense recommender datasets that will complement existing sparse industry-scale datasets. The new interface and the collected datasets are intended to foster integration of research in recommender systems with research in social and behavioral sciences. For instance, we will use the datasets to assess the diversity of human preferences in different domains of visual experience. Further, using the datasets we will be able to measure crucial psychological effects, such as preference consistency, scale acuity and anchoring biases. Last, we the datasets will facilitate evaluation in counterfactual learning experiments.

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arXiv2016

Social learning strategies modify the effect of network structure on group performance

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.

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