Abstract
Language usage behavior of users evolves over time, as they interact on social media such as Twitter. We study the evolution of language usage behavior of individuals, across topics, on microblogs. We propose Man-O-Meter, a framework to model such evolution. We model the evolution using a combination of three dimensions: (a) time, (b) content (topics) and (c) influence flow over social relationships. We assert the goodness of our approach, by predicting ranks of experts, with respect to their influence in their respective expertise category, using the change in language used in time. We apply our framework on 2, 273 influential microbloggers on Twitter, across 62 categories, spanning over 10 domains. Our work is applicable in predicting activity and influence, interest evolution, job change and community change expected to happen to a user, in future.
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Dey, K., Kaushik, S., Lamba, H., Nagar, S. (2016). Man-O-Meter: Modeling and Assessing the Evolution of Language Usage of Individuals on Microblogs. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_28
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