Modern social media platforms facilitate the rapid spread of information online. Modelling phenomena such as social contagion and information diffusion are contingent upon a detailed understanding of the information-sharing processes. In Twitter, an important aspect of this occurs with retweets, where users rebroadcast the tweets of other users. To improve our understanding of how these distributions arise, we analyse the distribution of retweet times. We show that a power law with exponential cutoff provides a better fit than the power laws previously suggested. We explain this fit through the burstiness of human behaviour and the priorities individuals place on different tasks.
Of the four late Pleistocene and early post-Pleistocene lithic complexes defined by Edward P. Lanning, only one, the Las Vegas complex, is supported by existing evidence from the Santa Elena Peninsula of southwest Ecuador. The Achallan culture, previously defined by Stothert and assigned to the post-Las Vegas period, is now unsupported by satisfactory evidence.
Hieroglyphic and comparative linguistic evidence indicate that a Lowland Maya stela cult had been in existence, with monuments being erected predominantly or exclusively at the end of the 360-day year, in Late Preclassic times. These data corroborate Hammond's (1982) evidence for Late Preclassic stela erection. With his demonstration that the stela cult was associated with public architecture and a sacrificial burial at Cuello, the inference that contemporaneous stelae were erected primarily at year-endings establishes the complete Lowland Maya form of the stela cult well before the end of the Late Preclassic. It indicates that the cult was contemporaneous with Pacific coastal and adjacent highland stela cults, and developed at least partially in independence of the latter.
Tweet clustering for event detection is a powerful modern method to automate the real-time detection of events. In this work we present a new tweet clustering approach, using a probabilistic approach to incorporate temporal information. By analysing the distribution of time gaps between tweets we show that the gaps between pairs of related tweets exhibit exponential decay, whereas the gaps between unrelated tweets are approximately uniform. Guided by this insight, we use probabilistic arguments to estimate the likelihood that a pair of tweets are related, and build an improved clustering method. Our method Social Media Event Response Clustering (SMERC) creates clusters of tweets based on their tendency to be related to a single event. We evaluate our method at three levels: through traditional event prediction from tweet clustering, by measuring the improvement in quality of clusters created, and also comparing the clustering precision and recall with other methods. By applying SMERC to tweets collected during a number of sporting events, we demonstrate that incorporating temporal information leads to state of the art clustering performance.
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