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@article{145974,
        author = {B.sairam and R.K.magesh and T.vallarasan and S.P.mani},
        title = {temporal model for topic prediction in online social networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {11},
        pages = {1368-1371},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145974},
        abstract = {It is really popular to detect hot topics, which can benefit many tasks including topic recommendations, the guidance of public opinions, and so on. However, in some cases, people may want to know when to re-hot a topic, i.e., make the topic popular again. In this paper, we address this issue by introducing a temporal User Topic Participation (UTP) model which models users’ behaviours of posting messages. The UTP model takes into account users’ interests, friend-circles, and unexpected events in online social networks. Also, it considers the continuous temporal modeling of topics, since topics are changing continuously over time. Furthermore, a weighting scheme is proposed to smooth the fluctuations in topic re-hotting prediction. Finally, experimental results conducted on real-world data sets demonstrate the effectiveness of our proposed models and topic re-hotting prediction methods.
},
        keywords = {},
        month = {},
        }