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Found 9 papers, 0 papers with code
Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals
Also, we propose a novel technique to construct news datasets minimizing the latent biases in existing news datasets.
Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data
Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost.
METEOR: Learning Memory and Time Efficient Representations from Multi-modal Data Streams
To address this problem, we present METEOR, a novel MEmory and Time Efficient Online Representation learning technique, which: (1) learns compact representations for multi-modal data by sharing parameters within semantically meaningful groups and preserves the domain-agnostic semantics; (2) can be accelerated using parallel processes to accommodate different stream rates while capturing the temporal changes of the units; and (3) can be easily extended to capture implicit/explicit external knowledge related to multi-modal data streams.
On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation
Moreover, we use the stack model to predict the personal values of a large community of Twitter users using their public tweet content and empirically derive several interesting findings about their online behavior consistent with earlier findings in the social science and social media literature.
OMBA: User-Guided Product Representations for Online Market Basket Analysis
OMBA jointly learns representations for products and users such that they preserve the temporal dynamics of product-to-product and user-to-product associations.
JPLink: On Linking Jobs to Vocational Interest Types
To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions.
USTAR: Online Multimodal Embedding for Modeling User-Guided Spatiotemporal Activity
Building spatiotemporal activity models for people's activities in urban spaces is important for understanding the ever-increasing complexity of urban dynamics.
On Learning Word Embeddings From Linguistically Augmented Text Corpora
Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations.
SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP)
This paper describes the SemEval 2018 shared task on semantic extraction from cybersecurity reports, which is introduced for the first time as a shared task on SemEval.
