452 research outputs found
NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities
Although a lot of research has been done on utilising Online Social Media
during disasters, there exists no system for a specific task that is critical
in a post-disaster scenario -- identifying resource-needs and
resource-availabilities in the disaster-affected region, coupled with their
subsequent matching. To this end, we present NARMADA, a semi-automated platform
which leverages the crowd-sourced information from social media posts for
assisting post-disaster relief coordination efforts. The system employs Natural
Language Processing and Information Retrieval techniques for identifying
resource-needs and resource-availabilities from microblogs, extracting
resources from the posts, and also matching the needs to suitable
availabilities. The system is thus capable of facilitating the judicious
management of resources during post-disaster relief operations.Comment: ACL 2020 Workshop on Natural Language Processing for Social Media
(SocialNLP
Characterizing Information Seeking Events in Health-Related Social Discourse
Social media sites have become a popular platform for individuals to seek and
share health information. Despite the progress in natural language processing
for social media mining, a gap remains in analyzing health-related texts on
social discourse in the context of events. Event-driven analysis can offer
insights into different facets of healthcare at an individual and collective
level, including treatment options, misconceptions, knowledge gaps, etc. This
paper presents a paradigm to characterize health-related information-seeking in
social discourse through the lens of events. Events here are board categories
defined with domain experts that capture the trajectory of the
treatment/medication. To illustrate the value of this approach, we analyze
Reddit posts regarding medications for Opioid Use Disorder (OUD), a critical
global health concern. To the best of our knowledge, this is the first attempt
to define event categories for characterizing information-seeking in OUD social
discourse. Guided by domain experts, we develop TREAT-ISE, a novel multilabel
treatment information-seeking event dataset to analyze online discourse on an
event-based framework. This dataset contains Reddit posts on
information-seeking events related to recovery from OUD, where each post is
annotated based on the type of events. We also establish a strong performance
benchmark (77.4% F1 score) for the task by employing several machine learning
and deep learning classifiers. Finally, we thoroughly investigate the
performance and errors of ChatGPT on this task, providing valuable insights
into the LLM's capabilities and ongoing characterization efforts.Comment: Under review AAAI-2024. 10 pages, 6 tables, 2 figue
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
In multilingual societies like the Indian subcontinent, use of code-switched
languages is much popular and convenient for the users. In this paper, we study
offense and abuse detection in the code-switched pair of Hindi and English
(i.e. Hinglish), the pair that is the most spoken. The task is made difficult
due to non-fixed grammar, vocabulary, semantics and spellings of Hinglish
language. We apply transfer learning and make a LSTM based model for hate
speech classification. This model surpasses the performance shown by the
current best models to establish itself as the state-of-the-art in the
unexplored domain of Hinglish offensive text classification.We also release our
model and the embeddings trained for research purpose
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