相关论文: Beyond the Crowd: LLM-Augmented Community Notes fo…

Community-based fact-checking is a promising approach to verify social media content and correct misleading posts at scale. Yet, causal evidence regarding its effectiveness in reducing the spread of misinformation on social media is…

Misinformation undermines the credibility of social media and poses significant threats to modern societies. As a countermeasure, Twitter has recently introduced "Birdwatch," a community-driven approach to address misinformation on Twitter.…

As platforms increasingly scale down professional fact-checking, community-based alternatives are promoted as more transparent and democratic. The main substitute being proposed is community-based contextualization, most notably Community…

Several major social media platforms have shifted toward crowdsourced fact-checking systems like Community Notes to combat misinformation at scale. However, these systems face criticism regarding which content is scrutinized and how visible…

Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media content. Conventionally, annotator disagreements are resolved…

Community Notes are emerging as an important option for content moderation. The Community Notes system pioneered by Twitter, now known as X, uses a bridging algorithm to identify user-generated context with upvotes across political divides,…

The growing spread of online misinformation has created an urgent need for scalable, reliable fact-checking solutions. Crowdsourced fact-checking - where non-experts evaluate claim veracity - offers a cost-effective alternative to expert…

Community Notes (formerly known as Birdwatch) is the first large-scale crowdsourced content moderation initiative that was launched by X (formerly known as Twitter) in January 2021. As the Community Notes model gains momentum across other…

Social media platforms are increasingly adopting features that display crowdsourced context alongside posts, a technique pioneered by X's Community Notes. These systems -- which we term Crowdsourced Context Systems (CCS) -- have the…

Social networks scaffold the diffusion of information on social media. Much attention has been given to the spread of true vs. false content on online social platforms, including the structural differences between their diffusion patterns.…

We build a novel database of around 285,000 notes from the Twitter Community Notes program to analyze the causal influence of appending contextual information to potentially misleading posts on their dissemination. Employing a difference in…

Community-based fact-checking is promising to reduce the spread of misleading posts at scale. However, its effectiveness can be undermined by the delays in fact-check delivery. Notably, user-initiated organic comments often contain…

Deploying links to fact-checking websites (so-called "snoping") is a common intervention that can be used by social media users to refute misleading claims. However, its real-world effect may be limited as it suffers from low visibility and…

Community-based fact-checking is a promising approach to address misinformation on social media at scale. However, an understanding of what makes community-created fact-checks helpful to users is still in its infancy. In this paper, we…

Online social platforms increasingly rely on crowd-sourced systems to label misleading content at scale, but these systems must both aggregate users' evaluations and decide whose evaluations to trust. To address the latter, many platforms…

Fact-checking ecosystems on social media depend on the interplay between what users want checked and what contributors are willing to supply. Prior research has largely examined these forces in isolation, yet it remains unclear to what…

Misinformation spreads rapidly on social media, causing serious damage by influencing public opinion, promoting dangerous behavior, or eroding trust in reliable sources. It spreads too fast for traditional fact-checking, stressing the need…

Climate misinformation is a problem that has the potential to be substantially aggravated by the development of Large Language Models (LLMs). In this study we evaluate the potential for LLMs to be part of the solution for mitigating online…

With the rise of social media and peer-to-peer networks, users increasingly rely on crowdsourced responses for information and assistance. However, the mechanisms used to rank and promote responses often prioritize and end up biasing in…

We present an approach for selecting objectively informative and subjectively helpful annotations to social media posts. We draw on data from on an online environment where contributors annotate misinformation and simultaneously rate the…