Please use this identifier to cite or link to this item: https://doi.org/10.1145/2502081.2502203
Title: Understanding and classifying image tweets
Authors: Chen, T.
Lu, D.
Kan, M.-Y. 
Cui, P.
Keywords: Analysis
Classification
Image tweets
Microblog
Issue Date: 2013
Source: Chen, T., Lu, D., Kan, M.-Y., Cui, P. (2013). Understanding and classifying image tweets. MM 2013 - Proceedings of the 2013 ACM Multimedia Conference : 781-784. ScholarBank@NUS Repository. https://doi.org/10.1145/2502081.2502203
Related Datasets: 10635/137404
Abstract: 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%. Copyright © 2013 ACM.
Source Title: MM 2013 - Proceedings of the 2013 ACM Multimedia Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/78411
ISBN: 9781450324045
DOI: 10.1145/2502081.2502203
Appears in Collections:Staff Publications

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