The Journal of Machine Learning Research
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Analyzing social media via event facets
Proceedings of the 20th ACM international conference on Multimedia
Towards Automatic Image Understanding and Mining via Social Curation
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Hi-index | 0.00 |
Social media platforms now allow users to share images alongside their textual posts. These image tweets make up a fast-growing percentage of tweets, but have not been studied in depth unlike their text-only counterparts. We study a large corpus of image tweets in order to uncover what people post about and the correlation between the tweet's image and its text. We show that an important functional distinction is between visually-relevant and visually-irrelevant tweets, and that we can successfully build an automated classifier utilizing text, image and social context features to distinguish these two classes, obtaining a macro F1 of 70.5%.