Tootfinder

Classification of Quality Characteristics in Online User Feedback using Linguistic Analysis, Crowdsourcing and LLMs
Eduard C. Groen, Fabiano Dalpiaz, Martijn van Vliet, Boris Winter, Joerg Doerr, Sjaak Brinkkemper
https://arxiv.org/abs/2506.11722

Classification of Quality Characteristics in Online User Feedback using Linguistic Analysis, Crowdsourcing and LLMs
Software qualities such as usability or reliability are among the strongest determinants of mobile app user satisfaction and constitute a significant portion of online user feedback on software products, making it a valuable source of quality-related feedback to guide the development process. The abundance of online user feedback warrants the automated identification of quality characteristics, but the online user feedback's heterogeneity and the lack of appropriate training corpora limit the a…

Contracting with a Mechanism Designer
Tian Bai, Yiding Feng, Yaohao Liu, Mengfan Ma, Mingyu Xiao
https://arxiv.org/abs/2507.12054 https://

Contracting with a Mechanism Designer
This paper explores the economic interactions within modern crowdsourcing markets. In these markets, employers issue requests for tasks, platforms facilitate the recruitment of crowd workers, and workers complete tasks for monetary rewards. Recognizing that these roles serve distinct functions within the ecosystem, we introduce a three-party model that distinguishes among the principal (the requester), the intermediary (the platform), and the pool of agents (the workers). The principal, unable …

I know it is well-meaning, but extrapolating the total size of 2000 events from 320 reports is bogus unless one models the size distribution and how that influences whether an event has a report. I.e. big events get reported, smaller ones not so much.
Even with that, it would be very much open to debate.

Wisdom of the Crowd, Without the Crowd: A Socratic LLM for Asynchronous Deliberation on Perspectivist Data
Malik Khadar, Daniel Runningen, Julia Tang, Stevie Chancellor, Harmanpreet Kaur
https://arxiv.org/abs/2508.09911

Wisdom of the Crowd, Without the Crowd: A Socratic LLM for Asynchronous Deliberation on Perspectivist Data
Data annotation underpins the success of modern AI, but the aggregation of crowd-collected datasets can harm the preservation of diverse perspectives in data. Difficult and ambiguous tasks cannot easily be collapsed into unitary labels. Prior work has shown that deliberation and discussion improve data quality and preserve diverse perspectives -- however, synchronous deliberation through crowdsourcing platforms is time-intensive and costly. In this work, we create a Socratic dialog system using…

»Waze für Einwanderungsbehörde – ICEBlock-App geht nach Regierungskritik viral:
Eine Crowdsourcing-App gegen Abschiebeeinsätze in den USA wird von der US-Regierung massiv kritisiert. Doch das hat der App erst richtig zum Erfolg verholfen.«
Das die Bevölkerung in der USA auf Trump's Rassismus reagiert war ja klar und nun gibt es sogar ein App dafür – leider nur für auf dem iPhone.
📱

Waze für Einwanderungsbehörde: ICEBlock-App geht nach Regierungskritik viral
Eine Crowdsourcing-App gegen Abschiebeeinsätze in den USA wird von der US-Regierung massiv kritisiert. Doch das hat der App erst richtig zum Erfolg verholfen.

Network Hexagons Under Attack: Secure Crowdsourcing of Geo-Referenced Data
Okemawo Obadofin, Joao Barros
https://arxiv.org/abs/2506.05601 https://

Network Hexagons Under Attack: Secure Crowdsourcing of Geo-Referenced Data
A critical requirement for modern-day Intelligent Transportation Systems (ITS) is the ability to collect geo-referenced data from connected vehicles and mobile devices in a safe, secure and anonymous way. The Nexagon protocol, which builds on the IETF Locator/ID Separation Protocol (LISP) and the Hierarchical Hexagonal Clustering (H3) geo-spatial indexing system, offers a promising framework for dynamic, privacy-preserving data aggregation. Seeking to address the critical security and privacy v…

App gegen US-Einwanderungsbehörde nach Regierungskritik auf Platz 1
Eine Crowdsourcing-App gegen Abschiebeeinsätze in den USA wird von der US-Regierung massiv kritisiert. Doch das hat der App erst richtig zum Erfolg verholfen.

Stochastic Bandits for Crowdsourcing and Multi-Platform Autobidding
Fran\c{c}ois Bachoc, Nicol\`o Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni
https://arxiv.org/abs/2508.05844

Stochastic Bandits for Crowdsourcing and Multi-Platform Autobidding
Motivated by applications in crowdsourcing, where a fixed sum of money is split among $K$ workers, and autobidding, where a fixed budget is used to bid in $K$ simultaneous auctions, we define a stochastic bandit model where arms belong to the $K$-dimensional probability simplex and represent the fraction of budget allocated to each task/auction. The reward in each round is the sum of $K$ stochastic rewards, where each of these rewards is unlocked with a probability that varies with the fraction…

Great to learn about crowdsourcing Latin American digital archives collecting records about the COVID-19 pandemic https://revista.arquivonacional.gov.br/index.php/revistaacervo/article/download/2297/2361?inline=1 by Ian Kisil…

Crowd-SFT: Crowdsourcing for LLM Alignment
Alex Sotiropoulos, Sulyab Thottungal Valapu, Linus Lei, Jared Coleman, Bhaskar Krishnamachari
https://arxiv.org/abs/2506.04063

Crowd-SFT: Crowdsourcing for LLM Alignment
Large Language Models (LLMs) increasingly rely on Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) to align model responses with human preferences. While RLHF employs a reinforcement learning approach with a separate reward model, SFT uses human-curated datasets for supervised learning. Both approaches traditionally depend on small, vetted groups of annotators, making them costly, prone to bias, and limited in scalability. We propose an open, crowd-sourced fine…

Crowdsourcing MUSHRA Tests in the Age of Generative Speech Technologies: A Comparative Analysis of Subjective and Objective Testing Methods
Laura Lechler, Chamran Moradi, Ivana Balic
https://arxiv.org/abs/2506.00950

Crowdsourcing MUSHRA Tests in the Age of Generative Speech Technologies: A Comparative Analysis of Subjective and Objective Testing Methods
The MUSHRA framework is widely used for detecting subtle audio quality differences but traditionally relies on expert listeners in controlled environments, making it costly and impractical for model development. As a result, objective metrics are often used during development, with expert evaluations conducted later. While effective for traditional DSP codecs, these metrics often fail to reliably evaluate generative models. This paper proposes adaptations for conducting MUSHRA tests with non-ex…

Crowdsourcing eHMI Designs: A Participatory Approach to Autonomous Vehicle-Pedestrian Communication
Ronald Cumbal, Didem Gurdur Broo, Ginevra Castellano
https://arxiv.org/abs/2506.18605

Crowdsourcing eHMI Designs: A Participatory Approach to Autonomous Vehicle-Pedestrian Communication
As autonomous vehicles become more integrated into shared human environments, effective communication with road users is essential for ensuring safety. While previous research has focused on developing external Human-Machine Interfaces (eHMIs) to facilitate these interactions, we argue that involving users in the early creative stages can help address key challenges in the development of this technology. To explore this, our study adopts a participatory, crowd-sourced approach to gather user-ge…

Crowdsourcing Ubiquitous Indoor Localization with Non-Cooperative Wi-Fi Ranging
Emerson Sie, Enguang Fan, Federico Cifuentes-Urtubey, Deepak Vasisht
https://arxiv.org/abs/2506.18317

Crowdsourcing Ubiquitous Indoor Localization with Non-Cooperative Wi-Fi Ranging
Indoor localization opens the path to potentially transformative applications. Although many indoor localization methods have been proposed over the years, they remain too impractical for widespread deployment in the real world. In this paper, we introduce PeepLoc, a deployable and scalable Wi-Fi-based solution for indoor localization that relies only on pre-existing devices and infrastructure. Specifically, PeepLoc works on any mobile device with an unmodified Wi-Fi transceiver and in any indo…

This https://arxiv.org/abs/2308.13301 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

On Incentivizing Social Information Sharing Through Routing Games
Crowdsourcing services, such as Waze, leverage a mass of mobile users to learn massive point-of-interest (PoI) information while traveling and share it as a public good. Given that crowdsourced users mind their travel costs and possess various preferences over the PoI information along different paths, we formulate the problem as a novel non-atomic multi-path routing game with positive network externalities among users in social information sharing. In the absence of any incentive design, our p…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Data Quality in Crowdsourcing and Spamming Behavior Detection
Yang Ba, Michelle V. Mancenido, Erin K. Chiou, Rong Pan