TY - JOUR

T1 - A Trustworthy Approach to Classify and Analyze Epidemic-Related Information From Microblogs

AU - Nguyen, Thi Huyen

AU - Fisichella, Marco

AU - Rudra, Koustav

N1 - Publisher Copyright: IEEE

PY - 2024/5/13

Y1 - 2024/5/13

N2 - Social media platforms, such as Twitter, are crucial resources to obtain situational information during disease outbreaks. Due to the sheer volume of user-generated content, providing tools that can automatically classify input texts into various types, such as symptoms, transmission, prevention measures, etc., and generate concise situational updates is necessary. Apart from high classification accuracy, interpretability is an important requirement when designing machine learning models for tasks in medical domain. In this article, we provide annotated epidemic-related datasets with labels of information types and rationales, which are short phrases from the original tweets, to support the assigned labels. Next, we introduce a trustworthy approach for the automatic classification of tweets posted during epidemics. Our classification model is able to extract short explanations/rationales for output decisions on unseen data. Moreover, we propose a simple graph-based ranking method to generate short summaries of tweets. Experiments on two epidemic-related datasets show the following: 1) our classification model obtains an average of 82% Macro-F1 and better interpretability scores in terms of Token-F1 (20% improvement) than baselines; 2) the extracted rationales capture essential disease-related information in the tweets; 3) our graph-based method with rationales is simple, yet efficient for generating concise situational updates.

AB - Social media platforms, such as Twitter, are crucial resources to obtain situational information during disease outbreaks. Due to the sheer volume of user-generated content, providing tools that can automatically classify input texts into various types, such as symptoms, transmission, prevention measures, etc., and generate concise situational updates is necessary. Apart from high classification accuracy, interpretability is an important requirement when designing machine learning models for tasks in medical domain. In this article, we provide annotated epidemic-related datasets with labels of information types and rationales, which are short phrases from the original tweets, to support the assigned labels. Next, we introduce a trustworthy approach for the automatic classification of tweets posted during epidemics. Our classification model is able to extract short explanations/rationales for output decisions on unseen data. Moreover, we propose a simple graph-based ranking method to generate short summaries of tweets. Experiments on two epidemic-related datasets show the following: 1) our classification model obtains an average of 82% Macro-F1 and better interpretability scores in terms of Token-F1 (20% improvement) than baselines; 2) the extracted rationales capture essential disease-related information in the tweets; 3) our graph-based method with rationales is simple, yet efficient for generating concise situational updates.

KW - Blogs

KW - Classification

KW - Computational modeling

KW - Data mining

KW - Diseases

KW - epidemic

KW - Feature extraction

KW - health crisis

KW - microblogs

KW - Social networking (online)

KW - Transformers

KW - trustworthy systems

UR - http://www.scopus.com/inward/record.url?scp=85193299864&partnerID=8YFLogxK

U2 - 10.1109/TCSS.2024.3391395

DO - 10.1109/TCSS.2024.3391395

M3 - Article

AN - SCOPUS:85193299864

SP - 1

EP - 13

JO - IEEE Transactions on Computational Social Systems

JF - IEEE Transactions on Computational Social Systems

ER -