AMUSED: An Annotation Framework of Multi-modal Social Media Data
This work addresses data collection challenges for researchers and practitioners working with social media, but it is incremental as it builds on existing annotation methods with a new framework.
The authors tackled the problem of collecting and annotating multi-modal social media data by developing AMUSED, a semi-automated framework that reduces workload and issues in data annotation, as demonstrated by its application to gathering COVID-19 misinformation data from platforms like Twitter, YouTube, and Reddit.
In this paper, we present a semi-automated framework called AMUSED for gathering multi-modal annotated data from the multiple social media platforms. The framework is designed to mitigate the issues of collecting and annotating social media data by cohesively combining machine and human in the data collection process. From a given list of the articles from professional news media or blog, AMUSED detects links to the social media posts from news articles and then downloads contents of the same post from the respective social media platform to gather details about that specific post. The framework is capable of fetching the annotated data from multiple platforms like Twitter, YouTube, Reddit. The framework aims to reduce the workload and problems behind the data annotation from the social media platforms. AMUSED can be applied in multiple application domains, as a use case, we have implemented the framework for collecting COVID-19 misinformation data from different social media platforms.