AI Researchers Use Social Media Monitoring Tactics to Identify Behavior Toward Vaccination
The AI research team at the University of Warwick has developed an intelligent machine learning-based algorithm as part of social media monitoring and cluster intelligence. This AI-based social media intelligence algorithm can be used to identify and evaluate how people on social media communicate about their opinion, experiences and concerns toward vaccinations. This model is called “The Vaccine Attitude Detection (VADet) Model.” The latest addition to our coverage of AI and machine learning projects exemplify how advanced data science could be used effectively in improving our interaction with machines and internet.
VADeT model is an advanced ML dataset that requires minimal training. It can be trained using a small sample dataset of online tweets before these are used for larger analyses. The new ML model can identify how people’s attitudes vary in the online domain and how different kinds of fact-checking and conspiracy tool kits work in the social media world as far as vaccinations are concerned. AI researchers at the University of Warwick believe their AI model could potentially save healthcare organizations and government agencies save millions of dollars wasted in creating awareness drives about vaccination. By leveraging social media platforms, healthcare institutions can channelize their resources to address vaccine misinformation and negative comments posted on social media platforms such as Facebook, Instagram, LinkedIn, Twitter, and TikTok.
The AI-based model can analyse a social media post and establish its author’s stance towards vaccines, by being ‘trained’ to recognise that stance from a small number of example tweets. – University of Warwick
The UK Research and Innovation (UKRI) funded this research led by Professor Yulan He. It was proposed to be presented at the Annual Conference of the North American Chapter of the Association for Computational Linguistics on 12 July 2022.
What is VADeT?
The COVID-19 pandemic is the most destructive event of our lifetime. Vaccinations have helped save many lives. Yet, online social media platforms are filled with negative sentiments and comments linked to vaccinations– which could emerge from a variety of sources such as fear, anxiety and misinformation. Many organizations collected data from social media platforms such as Twitter to understand how healthcare companies could use Search and Big Data strategy to improve rate of vaccinations.
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VADeT is a powerful AI-based algorithm running on semi-supervised approach for sentiment analysis and tweet clustering on topics related to vaccinations. It is majorly focused at simplifying the process linked to annotated and unlabelled data. It uses a variational auto-encoding architecture to learn from a very small dataset of unlabelled data and then augments the analysis for fine-tuning annotated data of user attitudes and sentiment extracted from social media posts. Currently, it is proposed for use as a tool to combat “infodemic” but in the long run, it could also be used against fake news detection and marketing analysis.
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Currently, it has been proved that VADet is able to learn disentangled stance and aspect topics. It also outperforms existing aspect-based sentiment analysis models on both stance detection and tweet clustering.
AI Machine Learning Projects on Vaccination and How People Generally Talk about it Online
Public sentiment has always been divided about vaccinations, much before social media came into the picture. However, online access to social media micro-blogging sites has certainly played a big role in spreading misinformation about vaccines. But, thanks to recent developments in Natural Language Processing, Deep Learning and Sentiment Analysis, AI researchers are able to exactly identify the extent to which social media comments and conversations influence vaccinations success in recent regions of the world. From vaccine hesitancy to anti-government stance, AI is able to find what really put people off about vaccinations.
The recent AI research by the University of Warwick follows the recent demand for social media intelligence to curb infodemic about vaccinations and their potential benefits and effectiveness, especially in the Post-COVID-19 era. For example, AI and machine learning researchers at the Stevens Institute of Technology were on their way toward developing a scalable solution: an AI tool capable of detecting “fake news” relating to COVID-19. It gathered data from 24000 tweets to develop a stance detection algorithm.
While there are many challenges in the way AI and machine learning are used to detect and analyze vaccination misinformation, there role in creating more awareness about the benefits of vaccination can’t be underestimated.
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