no code implementations • 17 Mar 2024 • Xinyi Zhou, ASHISH SHARMA, Amy X. Zhang, Tim Althoff
By retrieving evidence as refutations or contexts, MUSE identifies and explains (in)accuracies in a piece of content--not presupposed to be misinformation--with references.
no code implementations • 25 Oct 2023 • Boda Lin, Xinyi Zhou, Binghao Tang, Xiaocheng Gong, Si Li
Pre-trained language models have been widely used in dependency parsing task and have achieved significant improvements in parser performance.
1 code implementation • 15 Aug 2023 • Haolin Zhou, Junwei Pan, Xinyi Zhou, Xihua Chen, Jie Jiang, Xiaofeng Gao, Guihai Chen
To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target.
1 code implementation • 7 Jan 2023 • Xinyi Zhou, Jiayu Li, Qinzhou Li, Reza Zafarani
We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal.
no code implementations • 20 Nov 2022 • Xinyi Zhou, Reza Zafarani, Emilio Ferrara
The COVID-19 pandemic has gained worldwide attention and allowed fake news, such as ``COVID-19 is the flu,'' to spread quickly and widely on social media.
no code implementations • 9 Feb 2022 • Xinyi Zhou, Kai Shu, Vir V. Phoha, Huan Liu, Reza Zafarani
To distinguish between intentional versus unintentional spreading, we study the psychological explanations of unintentional spreading.
1 code implementation • 18 Oct 2020 • Chen Yang, Xinyi Zhou, Reza Zafarani
Also, misinformation on COVID-19 is frequently spread on social media.
1 code implementation • 9 Jun 2020 • Xinyi Zhou, Apurva Mulay, Emilio Ferrara, Reza Zafarani
Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies.
1 code implementation • 19 Feb 2020 • Xinyi Zhou, Jindi Wu, Reza Zafarani
Current studies have made significant contributions to predicting fake news with less focus on exploiting the relationship (similarity) between the textual and visual information in news articles.
no code implementations • 2 Nov 2019 • Niraj Sitaula, Chilukuri K. Mohan, Jennifer Grygiel, Xinyi Zhou, Reza Zafarani
By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility.
no code implementations • 30 Apr 2019 • Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, Huan Liu
In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors on social media and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news.
no code implementations • 26 Apr 2019 • Xinyi Zhou, Atishay Jain, Vir V. Phoha, Reza Zafarani
Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.
no code implementations • 2 Dec 2018 • Xinyi Zhou, Reza Zafarani
The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news analysis, detection and intervention.
no code implementations • 23 Jul 2016 • Xinyi Zhou, Yong Hu, Yong Deng, Felix T. S. Chan, Alessio Ishizak
However, in many cases, the pairwise comparison matrix is difficult to complete, which obstructs the subsequent operations of the clas- sical AHP.