Preprint
Towards Designing a Question-Answering Chatbot for Online News: Understanding Questions and Perspectives
arXiv.org
Cornell University
12/17/2023
DOI: 10.48550/arxiv.2312.10650
Abstract
Large Language Models (LLMs) have created opportunities for designing chatbots that can support complex question-answering (QA) scenarios and improve news audience engagement. However, we still lack an understanding of what roles journalists and readers deem fit for such a chatbot in newsrooms. To address this gap, we first interviewed six journalists to understand how they answer questions from readers currently and how they want to use a QA chatbot for this purpose. To understand how readers want to interact with a QA chatbot, we then conducted an online experiment (N=124) where we asked each participant to read three news articles and ask questions to either the author(s) of the articles or a chatbot. By combining results from the studies, we present alignments and discrepancies between how journalists and readers want to use QA chatbots and propose a framework for designing effective QA chatbots in newsrooms.
Details
- Title: Subtitle
- Towards Designing a Question-Answering Chatbot for Online News: Understanding Questions and Perspectives
- Creators
- Md Naimul HoqueAyman MahfuzMayukha KindiNaeemul Hassan
- Resource Type
- Preprint
- Publication Details
- arXiv.org
- Publisher
- Cornell University
- DOI
- 10.48550/arxiv.2312.10650
- eISSN
- 2331-8422
- Language
- English
- Date posted
- 12/17/2023
- Academic Unit
- Computer Science
- Record Identifier
- 9984787450102771
Metrics
1 Record Views