Data and models for stance and premise detection in COVID-19 tweets: insights from the Social Media Mining for Health (SMM4H) 2022 shared task

• URL: http://arxiv.org/abs/2311.08057v1
• Date: Tue, 14 Nov 2023 10:30:49 GMT
• Title: Data and models for stance and premise detection in COVID-19 tweets: insights from the Social Media Mining for Health (SMM4H) 2022 shared task
• Authors: Vera Davydova, Huabin Yang, Elena Tutubalina
• Abstract summary: We organize the Social Media Mining for Health (SMM4H) 2022 Shared Task 2. This competition utilized manually annotated posts on three COVID-19-related topics: school closures, stay-at-home orders, and wearing masks. We present newly collected data on vaccination from Twitter to assess the performance of models on a different topic.
• Score: 7.559611243635055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The COVID-19 pandemic has sparked numerous discussions on social media platforms, with users sharing their views on topics such as mask-wearing and vaccination. To facilitate the evaluation of neural models for stance detection and premise classification, we organized the Social Media Mining for Health (SMM4H) 2022 Shared Task 2. This competition utilized manually annotated posts on three COVID-19-related topics: school closures, stay-at-home orders, and wearing masks. In this paper, we extend the previous work and present newly collected data on vaccination from Twitter to assess the performance of models on a different topic. To enhance the accuracy and effectiveness of our evaluation, we employed various strategies to aggregate tweet texts with claims, including models with feature-level (early) fusion and dual-view architectures from SMM4H 2022 leaderboard. Our primary objective was to create a valuable dataset and perform an extensive experimental evaluation to support future research in argument mining in the health domain.

Related papers

• Capabilities of Gemini Models in Medicine [100.60391771032887]
  We introduce Med-Gemini, a family of highly capable multimodal models specialized in medicine. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them. Our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment.
  arXiv  Detail & Related papers  (2024-04-29T04:11:28Z)
• Explorers at #SMM4H 2023: Enhancing BERT for Health Applications through Knowledge and Model Fusion [3.386401892906348]
  Social media has become a valuable data resource for studying human health. This paper outlines the methods in our participation in the #SMM4H 2023 Shared Tasks.
  arXiv  Detail & Related papers  (2023-12-17T08:52:05Z)
• Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets [1.9988653168573556]
  Social media platforms have become a popular medium for discussions on vaccine-related topics. In this study, we evaluate the usage of Large Language Models, in this case GPT-4, and weak supervision, to identify COVID-19 vaccine-related tweets.
  arXiv  Detail & Related papers  (2023-09-12T18:18:23Z)
• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information. To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)
• Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification [60.49594822215981]
  This paper presents a classification model for detecting COVID-19 vaccination related search queries. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
  arXiv  Detail & Related papers  (2022-12-16T13:57:41Z)
• METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets [13.35986397208115]
  This paper releases METS-CoV, a dataset containing medical entities and targeted sentiments from COVID-19-related tweets. To the best of our knowledge, METS-CoV is the first dataset to collect medical entities and corresponding sentiments of COVID-19-related tweets.
  arXiv  Detail & Related papers  (2022-09-28T01:55:14Z)
• Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies [14.558584240713154]
  We propose an adversarial learning-based stance classifier to automatically identify the public's attitudes toward COVID-19-related health policies. To enhance the model's deeper understanding, we incorporate policy descriptions as external knowledge into the model. We evaluate the performance of a broad range of baselines on the stance detection task for COVID-19-related health policies.
  arXiv  Detail & Related papers  (2022-09-10T10:27:21Z)
• Twitter conversations predict the daily confirmed COVID-19 cases [0.2320417845168326]
  Pandemic-specific discourse has remained on-trend on microblogging platforms such as Twitter and Weibo. We propose a sentiment-involved topic-based methodology for designing multiple time series from publicly available COVID-19 related Twitter conversations. We show that the inclusion of social media variables for modeling introduces 48.83--51.38% improvements on RMSE over the baseline models.
  arXiv  Detail & Related papers  (2022-06-21T15:31:06Z)
• MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation [86.38736781043109]
  We build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG. We propose two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset.
  arXiv  Detail & Related papers  (2020-10-15T03:34:33Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.