Local Variation of Collective Attention in Hashtag Spike Trains
| Autor: | Sanli, C., Renaud Lambiotte |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Computer Science - Computers and Society Quantitative Biology::Neurons and Cognition behavior statistical signal processing Computers and Society (cs.CY) online social media Computer Science - Social and Information Networks data mining Computer Science::Social and Information Networks Computer Science::Computers and Society |
| Zdroj: | Scopus-Elsevier Sanli Cakir, C & Lambiotte, R 2015, Local variation of collective attention in hashtag spike trains . in AAAI Workshop-Technical Report: Ninth International AAAI Conference on Web and Social Media : Papers from the 2015 ICWSM Workshop . vol. WS-15-17, AI Access Foundation, pp. 8-12, 9th International Conference on Web and Social Media, ICWSM 2015, Oxford, United Kingdom, 26/05/15 . |
| DOI: | 10.48550/arxiv.1504.01637 |
| Popis: | In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event. Comment: 5 pages, 3 figures. Technical Report of the International AAAI Conference on Weblogs and Social Media (ICWSM-15) Workshop 3: Modeling and Mining Temporal Interactions |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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