Search Results for author:
Found 17 papers, 4 papers with code
Detecting Japanese Patients with Alzheimer's Disease based on Word Category Frequencies
This study investigates features of the words that AD patients use in their spoken language.
Forecasting Word Model: Twitter-based Influenza Surveillance and Prediction
Because of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events.
Density Estimation for Geolocation via Convolutional Mixture Density Network
Nowadays, geographic information related to Twitter is crucially important for fine-grained applications.
Multivariate Linear Regression of Symptoms-related Tweets for Infectious Gastroenteritis Scale Estimation
Based on these results, we can infer that social sensors can reliably detect unseasonal and local disease events under certain conditions, just as they can for seasonal or global events.
NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset
This has affected the social life of people owing to enforcements, such as "social distancing" and "stay at home."
Syndromic surveillance using search query logs and user location information from smartphones against COVID-19 clusters in Japan
Second, we extracted the location of the WSSCI via the smartphone application.
Offensive Language Detection on Video Live Streaming Chat
To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i. e., transfer learning).
KART: Parameterization of Privacy Leakage Scenarios from Pre-trained Language Models
One problem is that previous studies have assessed the risk for different real-world privacy leakage scenarios and attack methods, which reduces the portability of the findings.
End-to-end Biomedical Entity Linking with Span-based Dictionary Matching
Disease name recognition and normalization, which is generally called biomedical entity linking, is a fundamental process in biomedical text mining.
Single Model for Influenza Forecasting of Multiple Countries by Multi-task Learning
The accurate forecasting of infectious epidemic diseases such as influenza is a crucial task undertaken by medical institutions.
Mitigation of Diachronic Bias in Fake News Detection Dataset
Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducted.
Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset
Using the annotation scheme, we construct and publish the first Japanese fake news dataset.
Arukikata Travelogue Dataset
We have constructed Arukikata Travelogue Dataset and released it free of charge for academic research.
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
Geoparsing is a fundamental technique for analyzing geo-entity information in text.
Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations
Afterwards, a correlation analysis between the extracted emotions and a set of vaccination measures in Japan was conducted. The results revealed that surprise and fear were the most intense emotions predicted by the model for writers and readers, respectively, on the vaccine-related Tweet dataset.
Annotation-Scheme Reconstruction for “Fake News” and Japanese Fake News Dataset
Using the annotation scheme, we construct and publish the first Japanese fake news dataset.
