HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System
Youngseung Jeon, Jaehoon Kim, Sohyun Park, Yunyong Ko, Seongeun Ryu, Sang-Wook Kim, Kyungsik Han
TL;DR
HearHere introduces an AI-based web system to mitigate political echo chambers by pairing a News viewer with AI-predicted political stance and extremeness with a My opinion map that positions user thoughts among liberal and conservative opinions. The system uses a graph-based stance predictor with hierarchical attention and dual political knowledge graphs to quantify stance, informing curated news feeds and self-awareness visualizations. A formative study informs design and a 94-participant user study shows HearHere increases awareness of information diversity, though effects vary with political interest and stance, highlighting backfire risks and the need for user initiative. The work demonstrates a feasible path for AI-assisted digital literacy tools to promote exposure to diverse perspectives and balanced information consumption, with clear design implications and avenues for future refinement.
Abstract
Considerable efforts are currently underway to mitigate the negative impacts of echo chambers, such as increased susceptibility to fake news and resistance towards accepting scientific evidence. Prior research has presented the development of computer systems that support the consumption of news information from diverse political perspectives to mitigate the echo chamber effect. However, existing studies still lack the ability to effectively support the key processes of news information consumption and quantitatively identify a political stance towards the information. In this paper, we present HearHere, an AI-based web system designed to help users accommodate information and opinions from diverse perspectives. HearHere facilitates the key processes of news information consumption through two visualizations. Visualization 1 provides political news with quantitative political stance information, derived from our graph-based political classification model, and users can experience diverse perspectives (Hear). Visualization 2 allows users to express their opinions on specific political issues in a comment form and observe the position of their own opinions relative to pro-liberal and pro-conservative comments presented on a map interface (Here). Through a user study with 94 participants, we demonstrate the feasibility of HearHere in supporting the consumption of information from various perspectives. Our findings highlight the importance of providing political stance information and quantifying users' political status as a means to mitigate political polarization. In addition, we propose design implications for system development, including the consideration of demographics such as political interest and providing users with initiatives.