News-oriented multimedia search over multiple social networks

Abstract

The paper explores the problem of focused multimedia search over multiple social media sharing platforms such as Twitter and Facebook. A multi-step multimedia retrieval framework is presented that collects relevant and diverse multimedia content from multiple social media sources given an input news story or event of interest. The framework utilizes a novel query formulation method in combination with relevance prediction. The query formulation method relies on the construction of a graph of keywords for generating refined queries about the event/news story of interest based on the results of a firststep high precision query. Relevance prediction is based on supervised learning using 12 features computed from the content (text, visual) and social context (popularity, publication time) of posted items. A study is carried out on 20 real-world events and breaking news stories, using six social sources as input, and demonstrating the effectiveness of the proposed framework to collect and aggregate relevant high-quality media content from multiple social sources.