Abstract
The online social networks have embraced huge success from the crowds in the last two decades. Now, more and more people get used to chat with friends online via instant messaging applications on personal computers or mobile devices. Since these conversations are sequentially organized, which fails to show the logical relations between messages, they are called asynchronous conversations in previous studies. Unfortunately, the sequential layouts of messages are usually not intuitive to see how the conversation evolves as time elapses. In this paper, we propose to learn the structures of online asynchronous conversations by predicting the “reply-to” relation between messages based on text similarity and latent semantic transferability. A heuristic method is also brought forward to predict the relation, and then recover the conversation structure. We demonstrate the effectiveness of the proposed method through experiments on a real-world web forum comment data set.
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see the Python library: http://scikit-learn.org/.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (No. 61373023, No. 61133002, No. 61502116), the China National Arts Fund (No. 20164129), and the National Science Foundation (NSF) under grant No. CNS-1252292.
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Chen, J., Wang, C., Lin, H., Wang, W., Cai, Z., Wang, J. (2017). Learning the Structures of Online Asynchronous Conversations. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_2
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