Abstract
Useful information can be extracted by analyzing the temporal distributions of both social media user account creation and message traffic data. When applied over message traffic, the approach can differentiate top trending topics and persons in different geographical regions. Our analysis can help discover whether (and where) an influencer’s followers are localized, even in the absence of geospatial tags. An important application is in finding local experts in a social network, by identifying which experts are relevant to the geographic region of interest. We demonstrate how several temporal features can be utilized for distinguishing local vs. global influencers. For global influencers, spatiotemporal analysis helps understand the evolution of their popularity over time. We can also infer the number of followers that were gained in a specified period, which assists in estimating link creation times. Thus, temporal features can assist in deducing and utilizing information about the numbers and locations of influencers’ followers.
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Notes
- 1.
We use the term influencer and expert interchangeably when referring to an authoritative user.
- 2.
UTC is the time standard used globally, defined by the International Telecommunication Union Recommendation (ITU-R TF.460-6); it is a refinement of previous time standards such as Greenwich Mean Time. For instance, the UTC offset is − 5 for the time zone that includes the northeastern USA.
- 3.
- 4.
download.geonames.org/export/dump/timeZones.txt.
- 5.
Complex Fourier transform was used with the SciPy mathematical Python library. The real coefficients corresponding to the cosine terms recorded.
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Panasyuk, A., Mehrotra, K.G., Yu, E.SL., Mohan, C.K. (2022). Inferring Degree of Localization and Popularity of Twitter Topics and Persons Using Temporal Features. In: Özyer, T. (eds) Social Media Analysis for Event Detection. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-031-08242-9_8
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