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Found 14 papers, 2 papers with code
Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts
Tasks such as semantic search and clustering on crisis-related social media texts enhance our comprehension of crisis discourse, aiding decision-making and targeted interventions.
Urban Region Representation Learning with Attentive Fusion
Our model is powered by a dual-feature attentive fusion module named DAFusion, which fuses embeddings from different region features to learn higher-order correlations between the regions as well as between the different types of region features.
CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts
Additionally, we investigate the impact of model initialization on convergence and evaluate the significance of domain-specific models in generating semantically meaningful sentence embeddings.
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.
Demystifying the COVID-19 vaccine discourse on Twitter
While pro-vax tweets (37 million) far outnumbered anti-vax tweets (10 million), a majority of tweets from both stances (63% anti-vax and 53% pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax tweets during the observation period.
Real-time Spatio-temporal Event Detection on Geotagged Social Media
First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data.
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.
Graph Neural Networks with Continual Learning for Fake News Detection from Social Media
(2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection.
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.
Image Analysis Enhanced Event Detection from Geo-tagged Tweet Streams
Events detected from social media streams often include early signs of accidents, crimes or disasters.
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.
Geometry of Interest (GOI): Spatio-Temporal Destination Extraction and Partitioning in GPS Trajectory Data
To improve the quality of the extracted SVLs, instead of using NNQ, we label the visited locations as the IDs of the POIs which geometrically intersect with the GPS observations.
