1 research outputs found
AMUSED: An Annotation Framework of Multi-modal Social Media Data
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.Comment: 10 pages, 5 figures, 3 table