Towards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems

Annalena Aicher, Wolfgang Minker, Stefan Ultes


Abstract
To build a well-founded opinion it is natural for humans to gather and exchange new arguments. Especially when being confronted with an overwhelming amount of information, people tend to focus on only the part of the available information that fits into their current beliefs or convenient opinions. To overcome this “self-imposed filter bubble” (SFB) in the information seeking process, it is crucial to identify influential indicators for the former. Within this paper we propose and investigate indicators for the the user’s SFB, mainly their Reflective User Engagement (RUE), their Personal Relevance (PR) ranking of content-related subtopics as well as their False (FK) and True Knowledge (TK) on the topic. Therefore, we analysed the answers of 202 participants of an online conducted user study, who interacted with our argumentative dialogue system BEA (“Building Engaging Argumentation”). Moreover, also the influence of different input/output modalities (speech/speech and drop-down menu/text) on the interaction with regard to the suggested indicators was investigated.
Anthology ID:
2022.lrec-1.438
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4126–4134
Language:
URL:
https://aclanthology.org/2022.lrec-1.438
DOI:
Bibkey:
Cite (ACL):
Annalena Aicher, Wolfgang Minker, and Stefan Ultes. 2022. Towards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4126–4134, Marseille, France. European Language Resources Association.
Cite (Informal):
Towards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems (Aicher et al., LREC 2022)
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PDF:
https://preview.aclanthology.org/nodalida-main-page/2022.lrec-1.438.pdf