Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training datasets.The majority of such measurement data is embedded in scientific literature and the ability to automatically extract these data is essential to support the development of reliable property prediction methods.In this work, we describe a methodology for an automatic property extraction framework using material solubility as the target property.We create an annotated dataset containing tags for solubility-related entities using a combination of regular expressions and manual tagging.We then compare five entity recognition models leveraging both token-level and span-level architectures on the task of classifying solute names, solubility values, and solubility units.Additionally, we explore a novel pretraining approach that leverages automated chemical name and quantity extraction tools to generate large datasets that do not rely on intensive manual effort.Finally, we perform an analysis to identify the causes of classification errors.

Massive digital disinformation is one of the main risks of modern society.Hundreds of models and linguistic analyses have been done to compare and contrast misleading and credible content online.However, most models do not remove the confounding factor of a topic or narrative when training, so the resulting models learn a clear topical separation for misleading versus credible content.We study the feasibility of using two strategies to disentangle the topic bias from the models to understand and explicitly measure linguistic and stylistic properties of content from misleading versus credible content.First, we develop conditional generative models to create news content that is characteristic of different credibility levels.We perform multi-dimensional evaluation of model performance on mimicking both the style and linguistic differences that distinguish news of different credibility using machine translation metrics and classification models.We show that even though generative models are able to imitate both the style and language of the original content, additional conditioning on both the news category and the topic leads to reduced performance.In a second approach, we perform deception style "transfer" by translating deceptive content into the style of credible content and vice versa.Extending earlier studies, we demonstrate that, when conditioned on a topic, deceptive content is shorter, less readable, more biased, and more subjective than credible content, and transferring the style from deceptive to credible content is more challenging than the opposite direction.

Our contribution is the design and evaluation of an interactive machine learning interface that rapidly provides the user with model feedback after every interaction. To address visual scalability, this interface communicates with the user via a "tip of the iceberg" approach, where the user interacts with a small set of recommended instances for each class. To address computational scalability, we developed an O(n) classification algorithm that incorporates user feedback incrementally, and without consulting the data's underlying representation matrix. Our computational evaluation showed that this algorithm has similar accuracy to several off-the-shelf classification algorithms with small amounts of labeled data. Empirical evaluation revealed that users performed better using our design compared to an equivalent active learning setup.

Cryptocurrencies are a novel and disruptive technology that has prompted a new approach to how currencies work in the modern economy. As such, online discussions related to cryptocurrencies often go beyond posts about the technology and underlying architecture of the various coins, to subjective speculations of price fluctuations and predictions. Furthermore, online discussions, potentially driven by foreign adversaries, criminals or hackers, can have a significant impact on our economy and national security if spread at scale.