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Social network services (SNSs) such as Twitter and Facebook have emerged as a new medium for communication. They offer a unique mechanism of sharing information by allowing users to receive all messages posted by those whom they &#x201C;follow&#x201D;. As information in today's SNSs often spreads in the form of hashtags, detecting rapidly spreading hashtags in SNSs has recently attracted much attention. In this paper, we propose realistic epidemic models to describe the probabilistic process of hashtag propagation. Our models take into account the way how users communicate in SNSs; moreover the models consider the influence of external media and separate it from internal diffusion within networks. Based on the proposed models, we develop efficient inference algorithms that measure the propagation rates of hashtags in social networks. With real-life social network data including hashtags and synthetic data obtained by simulating information diffusion, we show that the proposed algorithms find fast-spreading hashtags more accurately than existing algorithms. Moreover, our in-depth case study demonstrates that our algorithms correctly find internal diffusion rates of hashtags as well as external media influences.

Detection of Rapidly Spreading Hashtags via Social Networks