Social multimedia is the hybrid of social media and multimedia. Social multimedia mining employs data mining techniques to understand the association among content, user and interaction in social multimedia. It has three basic problems at basis, micro and macro association levels. This paper extends the current interpretation of social multimedia mining as processing multi-modal individual objects, to a generalized definition beyond modality and beyond object. Cross-OSN (Online Social Networking) data mining, an instantiation to general social multimedia mining, is then positioned with main challenges, review of current studies and future directions.
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%.
By using a superluminescent diode as the light source and a depolariser inside the fibre coil, a constant scale factor is achieved without using polarisation control elements. For long-term behaviour an RMS-bias drift of 10 degrees/h is obtained.