  Multiple modalities represent different aspects by which information is
conveyed by a data source. Modern day social media platforms are one of the
primary sources of multimodal data, where users use different modes of
expression by posting textual as well as multimedia content such as images and
videos for sharing information. Multimodal information embedded in such posts
could be useful in predicting their popularity. To the best of our knowledge,
no such multimodal dataset exists for the prediction of social media photos. In
this work, we propose a multimodal dataset consisiting of content, context, and
social information for popularity prediction. Specifically, we augment the
SMPT1 dataset for social media prediction in ACM Multimedia grand challenge
2017 with image content, titles, descriptions, and tags. Next, in this paper,
we propose a multimodal approach which exploits visual features (i.e., content
information), textual features (i.e., contextual information), and social
features (e.g., average views and group counts) to predict popularity of social
media photos in terms of view counts. Experimental results confirm that despite
our multimodal approach uses the half of the training dataset from SMP-T1, it
achieves comparable performance with that of state-of-the-art.
