Towards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems (2022.lrec-1)
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| Challenge: | In order to overcome this “self-imposed filter bubble” (SFB), it is crucial to identify influential indicators for the user’s SFB, namely Reflective User Engagement (RUE), Personal Relevance ranking of content-related subtopics as well as False (FK) and True Knowledge (TK). |
| Approach: | They propose to model an SFB by focusing on four indicators for the user's Reflective User Engagement (RUE), their Personal Relevance ranking of content-related subtopics and their False (FK) and True Knowledge (TK) indicators are based on the responses of 202 users of an online argumentative dialogue system BEA. |
| Outcome: | The proposed system aims to break the self-imposed filter bubble (SFB) by identifying indicators for the user's SFB . |
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