A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation

The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets. The study makes comparisons alongside four key aspects: 1) the distribution of topics, 2) the live status of tweets, 3) language analysis and 4) the spreading power over time. An added contribution of this study is the creation of a COVID-19 misinformation classification dataset. Finally, we demonstrate that this new dataset helps improve misinformation classification by more than 9% based on average F1 measure

On the Coherence of Fake News Articles

The generation and spread of fake news within new and online media sources is emerging as a phenomenon of high societal significance. Combating them using data-driven analytics has been attracting much recent scholarly interest. In this study, we analyze the textual coherence of fake news articles vis-a-vis legitimate ones. We develop three computational formulations of textual coherence drawing upon the state-of-the-art methods in natural language processing and data science. Two real-world datasets from widely different domains which have fake/legitimate article labellings are then analyzed with respect to textual coherence. We observe apparent differences in textual coherence across fake and legitimate news articles, with fake news articles consistently scoring lower on coherence as compared to legitimate news ones. While the relative coherence shortfall of fake news articles as compared to legitimate ones form the main observation from our study, we analyze several aspects of the differences and outline potential avenues of further inquiry.Comment: 8th International Workshop on News Recommendation and Analytics (INRA 2020) held in conjunction with ECML PKDD 2020 Conferenc

WikiTalkEdit: A Dataset for modeling Editors’ behaviors on Wikipedia

10.18653/v1/2021.naacl-main.177Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologie

Real-Time Area Angle Monitoring Using Synchrophasors: A Practical Framework and Utility Deployment

This article develops a practical framework of Area Angle Monitoring (AAM) to monitor in real time the stress of bulk power transfer across an area of a power transmission system. Area angle is calculated from synchrophasor measurements in real time to provide alert to system operators if the area angle exceeds pre-defined thresholds. This article proposes a general method to identify the warning threshold of area angle and a simplified method to quickly update area angle thresholds under significant topology change. A mitigation strategy to relieve the area stress is also proposed. In order to handle the limited coverage of synchrophasor measurements, this article proposes a method to estimate phase angles for boundary buses without synchrophasor measurements, which extends the application scenario of AAM. AAM is verified for a power transmission area in the Western Electricity Coordinating Council system with both simulated data and synchrophasor measurements recorded from real events. A utility deployment for real-time application of AAM with livestream and recorded synchrophasor data is described.This is a manuscript of an article published as Ju, Wenyun, Ian Dobson, Kenneth Martin, Kai Sun, Neeraj Nayak, Iknoor Singh, Horacio Silva-Saravia, Anthony Faris, Lin Zhang, and Yajun Wang. "Real-Time Area Angle Monitoring Using Synchrophasors: A Practical Framework and Utility Deployment." IEEE Transactions on Smart Grid 12, no. 1 (2021): 859-870. DOI: 10.1109/TSG.2020.3020790. Posted with permission.</p

Real-Time Monitoring of Area Angles with Synchrophasor Measurements

This paper develops a comprehensive framework of Area Angle Monitoring (AAM) to monitor the stress of bulk power transfer across an area of a power transmission system in real-time. Area angle is calculated from synchrophasor measurements to provide alert to system operators if the area angle exceeds pre-defined thresholds. This paper proposes general methods to identify these warning and emergency thresholds, and tests a mitigation strategy to relieve the area stress when the area angle exceeds the threshold. In order to handle the limited coverage of synchrophasor measurements, this paper proposes methods to estimate phase angles for boundary buses without synchrophasor measurements, which extends the application of AAM. AAM is verified for a power transmission area in the Western Electricity Coordinating Council system with both simulated data and synchrophasor measurements recorded from real events. A utility deployment to test the framework for monitoring area angle with live-stream and recorded synchrophasor data is described.This is a pre-print of the article Ju, Wenyun, Ian Dobson, Kenneth Martin, Kai Sun, Neeraj Nayak, Iknoor Singh, Horacio Silva-Saravia, Anthony Faris, Lin Zhang, and Yajun Wang. "Real-Time Monitoring of Area Angles with Synchrophasor Measurements." arXiv preprint arXiv:2003.06476 (2020). Posted with permission.</p