Detection of Increased Time Intervals of Anti-Vaccine Tweets for COVID-19 Vaccine with BERT Model

• URL: http://arxiv.org/abs/2202.00477v1
• Date: Wed, 12 Jan 2022 18:30:23 GMT
• Title: Detection of Increased Time Intervals of Anti-Vaccine Tweets for COVID-19 Vaccine with BERT Model
• Authors: \"Ulk\"u Tuncer K\"u\c{c}\"ukta\c{s}, Fatih Uysal, F{\i}rat Hardala\c{c}, \.Ismail Biri
• Abstract summary: Anti-vaccination increases in social media can help institutions determine the strategy to be used in combating anti-vaccination. Recording and tracking every tweet entered with human labor would be inefficient. A deep learning-based natural language processing (NLP) model was used to determine the intervals in which anti-vaccine tweets are concentrated.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The most effective of the solutions against Covid-19 is the various vaccines developed. Distrust of vaccines can hinder the rapid and effective use of this remedy. One of the means of expressing the thoughts of society is social media. Determining the time intervals during which anti-vaccination increases in social media can help institutions determine the strategy to be used in combating anti-vaccination. Recording and tracking every tweet entered with human labor would be inefficient, so various automation solutions are needed. In this study, The Bidirectional Encoder Representations from Transformers (BERT) model, which is a deep learning-based natural language processing (NLP) model, was used. In a dataset of 1506 tweets divided into four different categories as news, irrelevant, anti-vaccine, and vaccine supporters, the model was trained with a learning rate of 5e-6 for 25 epochs. To determine the intervals in which anti-vaccine tweets are concentrated, the categories to which 652840 tweets belong were determined by using the trained model. The change of the determined categories overtime was visualized and the events that could cause the change were determined. As a result of model training, in the test dataset, the f-score of 0.81 and AUC values for different classes were obtained as 0.99,0.91, 0.92, 0.92, respectively. In this model, unlike the studies in the literature, an auxiliary system is designed that provides data that institutions can use when determining their strategy by measuring and visualizing the frequency of anti-vaccine tweets in a time interval, different from detecting and censoring such tweets.

Related papers

• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning [98.43475653490219]
  Federated learning (FL) is susceptible to poisoning attacks. FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
  arXiv  Detail & Related papers  (2023-12-07T16:56:24Z)
• Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter [3.768191396638854]
  Vaccine hesitancy continues to be a main challenge for public health officials during the COVID-19 pandemic. We present Vax-Culture, a novel Twitter COVID-19 dataset consisting of 6373 vaccine-related tweets. We hope this can lead to effective and targeted public health communication strategies for reaching individuals with anti-vaccine beliefs.
  arXiv  Detail & Related papers  (2023-04-13T23:04:30Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• 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)
• Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection [70.86672569101536]
  Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression. This paper investigates the use of prompt-based fine-tuning of PLMs that consistently uses AD classification errors as the training objective function.
  arXiv  Detail & Related papers  (2022-10-29T09:18:41Z)
• Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers [64.1526243118151]
  We find that doctors are overall more positive toward the COVID-19 vaccines. Doctors are more concerned with the effectiveness of the vaccines over newer variants. Nurses pay more attention to the potential side effects on children.
  arXiv  Detail & Related papers  (2022-09-11T14:22:16Z)
• Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets [3.319350419970857]
  11,211,672 COVID-19 vaccine-related tweets corresponding to 2,203,681 users over two years were analyzed. We finetuned a deep learning classifier using a state-of-the-art model, XLNet, to detect each tweet's sentiment automatically. Users from various demographic groups demonstrated distinct patterns in sentiments towards COVID-19 vaccines.
  arXiv  Detail & Related papers  (2022-07-05T13:53:16Z)
• Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media [40.61499595293957]
  We propose a novel semi-supervised approach for vaccine attitude detection, called VADet. VADet is able to learn disentangled stance and aspect topics, and outperforms existing aspect-based sentiment analysis models on both stance detection and tweet clustering.
  arXiv  Detail & Related papers  (2022-05-06T15:24:33Z)
• A Python Package to Detect Anti-Vaccine Users on Twitter [1.1602089225841632]
  Anti-vaccine hesitancy has been recently driven by the anti-vaccine narratives shared online. We introduce a Python package capable of analyzing Twitter profiles to assess how likely that profile is to spread anti-vaccine sentiment. We leverage the data on such users to understand what are the moral and emotional characteristics of anti-vaccine spreaders.
  arXiv  Detail & Related papers  (2021-10-21T17:59:25Z)
• Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU [9.8215089151757]
  Vaccine hesitancy and refusal can create clusters of low vaccine coverage and reduce the effectiveness of vaccination programs. Social media provides an opportunity to estimate emerging risks to vaccine acceptance by including geographical location and detailing vaccine-related concerns. Methods for classifying social media posts, such as vaccine-related tweets, use language models (LMs) trained on general domain text. We present a novel end-to-end framework consisting of interconnected components that use domain-specific LM trained on vaccine-related tweets and models commonsense knowledge into a bidirectional gated recurrent network (CK-BiGRU) with context-aware attention.
  arXiv  Detail & Related papers  (2021-06-17T15:16:08Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)

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.