Abstract
The flourish of Web-based Online Social Networks (OSNs) has led to numerous applications that exploit social relationships to boost the influence of content in the network. However, existing approaches focus on the social ties and ignore how the topic of a post and its structure relate to its popularity. Our work assists in filling this gap. The contribution of this work is two-fold: (i) we develop a scheme that automatically identifies the topic of a post, specifically tweets, in real-time without human participation in the process, and then (ii) based on the topic of the tweet and prior related posts, we recommend appropriate structural properties to increase the popularity of the particular tweet. By exploiting Wikipedia, our model requires no training or expensive feature engineering for the classification of tweets to topics.
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Best People On Twitter: http://goo.gl/AOU0GU.
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This research has been financed by the European Union through the FP7 ERC IDEAS 308019 NGHCS project and the Horizon2020 688380 VaVeL project.
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Litou, I., Kalogeraki, V., Gunopulos, D. (2016). On Topic Aware Recommendation to Increase Popularity in Microblogging Services (Short Paper). In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_40
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