Abstract
We investigate the temporal aspects of user behavior in this paper and relate it to the evolution of particular topics being discussed on the Twitter OSN. Studies have shown that a small number of frequent users are responsible for the maximum percentage of tweets. We further hypothesize that users deviate from their usual tweeting hours when a major event occurs. With these as our underlying concepts, we introduce a new metric called “tweet strength” that gives more weight to tweets by users who in general tweet less or those who do not usually tweet at a given time. We study the evolution of a set of topics through the lens of tweet strength and try to identify the classes of users driving the popularity at different times. We also study word-of-mouth diffusion mechanism through the network by defining a “copying” behavior. When a follower of a user tweets on the same topic as the user, the follower is said to have copied. We further make the definition time-dependent by imposing temporal thresholds on it.
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Jain, M., Rajyalakshmi, S., Tripathy, R.M., Bagchi, A. (2013). Temporal Analysis of User Behavior and Topic Evolution on Twitter. In: Bhatnagar, V., Srinivasa, S. (eds) Big Data Analytics. BDA 2013. Lecture Notes in Computer Science, vol 8302. Springer, Cham. https://doi.org/10.1007/978-3-319-03689-2_2
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DOI: https://doi.org/10.1007/978-3-319-03689-2_2
Publisher Name: Springer, Cham
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