From Nuisance to News Sense: Abstract and Introduction

20 May 2024

This paper is available on arxiv under CC 4.0 license.


(1) Jeremiah Milbauer, Carnegie Mellon University, Pittsburgh PA, USA (email: {jmilbaue | sherryw};

(2) Ziqi Ding, Carnegie Mellon University, Pittsburgh PA, USA (e-mail: {ziqiding | zhijinw}

(3) Tongshuang Wu, Carnegie Mellon University, Pittsburgh PA, USA.


Reading and understanding the stories in the news is increasingly difficult. Reporting on stories evolves rapidly, politicized news venues offer different perspectives (and sometimes different facts), and misinformation is rampant. However, Existing solutions merely aggregate an overwhelming amount of information from heterogenous sources, such as different news outlets, social media, and news bias rating agencies. We present NEWSSENSE, a novel sensemaking tool and reading interface designed to collect and integrate information from multiple news articles on a central topic. NEWSSENSE augments a central, grounding article of the user’s choice by linking it to related articles from different sources, providing inline highlights on how specific claims in the chosen article are either supported or contradicted by information from other articles. Using NEWSSENSE, users can seamlessly digest and cross-check multiple information sources without disturbing their natural reading flow. Our pilot study shows that NewsSense has the potential to help users identify key information, verify the credibility of news articles, and explore different perspectives. We opensource NewsSense at, and a demo video is hosted at

1 Introduction

Why is it so hard, and so exhausting, to read the news? In the quest for knowledge, news readers today must contend with a rapidly evolving 24-hour news cycle, multiple news venues competing for attention and clicks, and the challenge of integrating fact-based reporting, opinion pieces, and social media commentary (Lazer et al., 2018; Benkler et al., 2018; Farkas and Schou, 2019). With news becoming increasingly politicized (Faris et al., 2017) readers also face the challenge of identifying and avoiding misinformation, disinformation, and hyperbolic “clickbait" as they try to remain informed about the world around them.

Figure 1: A screenshot from NEWSSENSE browser extension running in Chrome. The extension provides highlights indicated supported and controversial information. When the user clicks on a highlighted sentence, NEWSENSE adds an scrollable overlay box containing snippets of external evidence.

Various solutions have been proposed to assist with users’ news reading. For example, media watchdog companies have created media bias charts to represent political leaning and credibility of news sources [1] [2]. However, these resources force users to rely on potentially untrustworthy third-party designations of media bias, which treat each news source as a whole, without digging into specific articles or topics.

While novel automatic fact checking (Thorne et al., 2018) and fake news detection (Zhou and Zafarani, 2020; Chen et al., 2015) systems can provide verification per-article, these approaches typically rely on a preordained corpus of verified facts which cannot keep up with the always-evolving facts, and may not reflect user preferences or multilateral perspectives. Aggregating articles from heterogeneous sources seem a more promising direction for cross-checking new facts (without predetermined groundtruths) and collecting different perspectives, but existing attempts are still too coarse and overwhelming. For example, both Google News’ “Stories" [3] feature and [4] collect articles about the same events but display them in the form of exhaustive lists – Users are still forced to read and compare each article on its own.

We argue that instead of simply collecting and aggregating news articles, information and claims from multiple sources should be integrated in a way that allows users to identify fine-grained claim-level bias, spin, controversy, or evidence.

We present NEWSSENSE, a novel framework for sensemaking within a cluster of documents, to address the three key problems of news reading – bias, factuality, and article overload – in a single streamlined interface. NEWSSENSE leverages existing modular NLP techniques to identify and link claims made across a cluster of news articles, such that these articles become references for each other. NEWSSENSE also displays the linking information using an interactive reading interface, which allows users to easily explore the cross-document connections without being overwhelmed. In pilot user studies, we see that the NEWSSENSE framework has the potential to help users identify key information, verify the credibility of news articles, and explore different perspectives.

While NEWSSENSE is primarily implemented for news articles, our framework can be easily generalized to other assisted reading and cross-checking scenarios (e.g., compare multiple manuscripts in literature reviews). The key contributions of NEWSSENSE are:

  1. A pipeline for analyzing the connections between a collection of documents.

  2. A two-stage method for efficiently computing cross-document links between claims that support or contradict each other, enabling “reference-free” fact checking.

  3. A framework for visualizing cross-document connections, and integrating claims from multiple documents into a single reading experience.

We conclude by discussing the generality and potential social benefits of NEWSSENSE.