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: how do we sample

such social media information spaces in order to deliver relevant

content on a topic to end users? Notice that these largescale

information spaces are inherently ‘diverse’, 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 significantly impact the information consumption experience:

low diversity can provide focused content that may

be simpler to understand, while high diversity can increase

breadth in 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 “compressive sensing”

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 find samples generated by

our method to be more interesting and subjectively engaging

compared to techniques inspired by state-of

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Publisher  Int'l AAAI Conference on Weblogs and Social Media

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