  The work of this paper presents a dataset that contains the data of 4011
videos about the ongoing outbreak of measles published on 264 websites on the
internet between January 1, 2024, and May 31, 2024. The dataset is available at
https://dx.doi.org/10.21227/40s8-xf63. These websites primarily include YouTube
and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The
remainder of the websites include Instagram and Facebook as well as the
websites of various global and local news organizations. For each of these
videos, the URL of the video, title of the post, description of the post, and
the date of publication of the video are presented as separate attributes in
the dataset. After developing this dataset, sentiment analysis (using VADER),
subjectivity analysis (using TextBlob), and fine-grain sentiment analysis
(using DistilRoBERTa-base) of the video titles and video descriptions were
performed. This included classifying each video title and video description
into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii)
one of the subjectivity classes i.e. highly opinionated, neutral opinionated,
or least opinionated, and (iii) one of the fine-grain sentiment classes i.e.
fear, surprise, joy, sadness, anger, disgust, or neutral. These results are
presented as separate attributes in the dataset for the training and testing of
machine learning algorithms for performing sentiment analysis or subjectivity
analysis in this field as well as for other applications. Finally, this paper
also presents a list of open research questions that may be investigated using
this dataset.
