Ning Yu


基于义原表示学习的词向量表示方法(Word Representation based on Sememe Representation Learning)
Ning Yu (于宁) | Jiangping Wang (王江萍) | Yu Shi (石宇) | Jianyi Liu (刘建毅)
Proceedings of the 20th Chinese National Conference on Computational Linguistics



Corpus Development for Studying Online Disinformation Campaign: A Narrative + Stance Approach
Mack Blackburn | Ning Yu | John Berrie | Brian Gordon | David Longfellow | William Tirrell | Mark Williams
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management

Disinformation on social media is impacting our personal life and society. The outbreak of the new coronavirus is the most recent example for which a wealth of disinformation provoked fear, hate, and even social panic. While there are emerging interests in studying how disinformation campaigns form, spread, and influence target audiences, developing disinformation campaign corpora is challenging given the high volume, fast evolution, and wide variation of messages associated with each campaign. Disinformation cannot always be captured by simple factchecking, which makes it even more challenging to validate and create ground truth. This paper presents our approach to develop a corpus for studying disinformation campaigns targeting the White Helmets of Syria. We bypass directly classifying a piece of information as disinformation or not. Instead, we label the narrative and stance of tweets and YouTube comments about White Helmets. Narratives is defined as a recurring statement that is used to express a point of view. Stance is a high-level point of view on a topic. We demonstrate that narrative and stance together can provide a dynamic method for real world users, e.g., intelligence analysts, to quickly identify and counter disinformation campaigns based on their knowledge at the time.


pdf bib
Feature Selection for Highly Skewed Sentiment Analysis Tasks
Can Liu | Sandra Kübler | Ning Yu
Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

“My Curiosity was Satisfied, but not in a Good Way”: Predicting User Ratings for Online Recipes
Can Liu | Chun Guo | Daniel Dakota | Sridhar Rajagopalan | Wen Li | Sandra Kübler | Ning Yu
Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)


Filling the Gap: Semi-Supervised Learning for Opinion Detection Across Domains
Ning Yu | Sandra Kübler
Proceedings of the Fifteenth Conference on Computational Natural Language Learning