Social media and networking websites, such as Twitter and Facebook, generate large quantities of information and have become mechanisms for real-time content dissipation to users. An important question that arises is:\u00a0 how do we sample such social media information spaces in order to deliver relevant content on a topic to end users? Notice that these large scale information spaces are inherently \u2018diverse\u2019, featuring a wide array of attributes such as location, recency, degree of diffusion effects in the network and so on. Naturally, for the end user, different levels of diversity in social media content can signi\ufb01cantly impact the information consumption experience: low diversity can provide focused content that may be simpler to understand, while high diversity can increase bread thin the exposure to multiple opinions and perspectives. Hence to address our research question, we turn to diversity as a core concept in our proposed sampling methodology. Here we are motivated by ideas in the \u201ccompressive sensing\u201d literature and utilize the notion of sparsity in social media information to represent such large spaces via a small number of basis components. Thereafter we use a greedy iterative clustering technique on this transformed space to construct samples matching a desired level of diversity. Based on Twitter Firehose data, we demonstrate quantitatively that our method is robust, and performs better than other baseline techniques over a variety of trending topics. In a user study, we further show that users \ufb01nd samples generated by our method to be more interesting and subjectively engaging compared to techniques inspired by state-of-the-art systems, with improvements in the range of 15\u201345%.<\/p>\n
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Social media and networking websites, such as Twitter and Facebook, generate large quantities of information and have become mechanisms for real-time content dissipation to users. An important question that arises is:\u00a0 how do we sample such social media information spaces in order to deliver relevant content on a topic to end users? 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