3 research outputs found
Multimodal Emotion Classification
Most NLP and Computer Vision tasks are limited to scarcity of labelled data.
In social media emotion classification and other related tasks, hashtags have
been used as indicators to label data. With the rapid increase in emoji usage
of social media, emojis are used as an additional feature for major social NLP
tasks. However, this is less explored in case of multimedia posts on social
media where posts are composed of both image and text. At the same time, w.e
have seen a surge in the interest to incorporate domain knowledge to improve
machine understanding of text. In this paper, we investigate whether domain
knowledge for emoji can improve the accuracy of emotion classification task. We
exploit the importance of different modalities from social media post for
emotion classification task using state-of-the-art deep learning architectures.
Our experiments demonstrate that the three modalities (text, emoji and images)
encode different information to express emotion and therefore can complement
each other. Our results also demonstrate that emoji sense depends on the
textual context, and emoji combined with text encodes better information than
considered separately. The highest accuracy of 71.98\% is achieved with a
training data of 550k posts.Comment: Accepted at the 2nd Emoji Workshop co-located with The Web Conference
201
ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
The convenience of social media has also enabled its misuse, potentially
resulting in toxic behavior. Nearly 66% of internet users have observed online
harassment, and 41% claim personal experience, with 18% facing severe forms of
online harassment. This toxic communication has a significant impact on the
well-being of young individuals, affecting mental health and, in some cases,
resulting in suicide. These communications exhibit complex linguistic and
contextual characteristics, making recognition of such narratives challenging.
In this paper, we provide a multimodal dataset of toxic social media
interactions between confirmed high school students, called ALONE (AdoLescents
ON twittEr), along with descriptive explanation. Each instance of interaction
includes tweets, images, emoji and related metadata. Our observations show that
individual tweets do not provide sufficient evidence for toxic behavior, and
meaningful use of context in interactions can enable highlighting or
exonerating tweets with purported toxicity.Comment: Accepted: Social Informatics 202