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.

通过两个可视化工具，HearHere 为用户提供定量的政治立场信息，促进多视角的新闻信息消费。通过一项用户研究，我们展示了 HearHere 在支持来自各个角度的信息消费方面的可行性，并强调提供政治立场信息和量化用户的政治地位的重要性以减轻政治极化。

HearHere: 基于人工智能的网页系统减轻新闻消费中的回音室现象

HearHere: Mitigating Echo Chambers in News Consumption through an  AI-based Web System

In the past decade, many scientific news media that report scientific
breakthroughs and discoveries emerged, bringing science and technology closer
to the general public. However, not all scientific news article cites proper
sources, such as original scientific papers. A portion of scientific news
articles contain misinterpreted, exaggerated, or distorted information that
deviates from facts asserted in the original papers. Manually identifying
proper citations is laborious and costly. Therefore, it is necessary to
automatically search for pertinent scientific papers that could be used as
evidence for a given piece of scientific news. We propose a system called SciEv
that searches for scientific evidence papers given a scientific news article.
The system employs a 2-stage query paradigm with the first stage retrieving
candidate papers and the second stage reranking them. The key feature of SciEv
is it uses domain knowledge entities (DKEs) to find candidates in the first
stage, which proved to be more effective than regular keyphrases. In the
reranking stage, we explore different document representations for news
articles and candidate papers. To evaluate our system, we compiled a pilot
dataset consisting of 100 manually curated (news,paper) pairs from ScienceAlert
and similar websites. To our best knowledge, this is the first dataset of this
kind. Our experiments indicate that the transformer model performs the best for
DKE extraction. The system achieves a P@1=50%, P@5=71%, and P@10=74% when it
uses a TFIDF-based text representation. The transformer-based re-ranker
achieves a comparable performance but costs twice as much time. We will collect
more data and test the system for user experience.

SciEv 是一个系统，可以在给定科学新闻文章的情况下搜索科学证据论文，并采用两阶段查询范式以检索候选论文并进行重新排序，它使用领域知识实体来找到候选论文，证明比常规关键短语更有效，并在重排阶段中探索了新闻文章和候选论文的不同文档表示方法。