Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discovering patterns of online popularity from time series   How is popularity gained online? Is being successful strictly related to
rapidly becoming viral in an online platform or is it possible to acquire
popularity in a steady and disciplined fashion? What are other temporal
characteristics that can unveil the popularity of online content? To answer
these questions, we leverage a multi-faceted temporal analysis of the evolution
of popular online contents. Here, we present dipm-SC: a multi-dimensional
shape-based time-series clustering algorithm with a heuristic to find the
optimal number of clusters. First, we validate the accuracy of our algorithm on
synthetic datasets generated from benchmark time series models. Second, we show
that dipm-SC can uncover meaningful clusters of popularity behaviors in a
real-world Twitter dataset. By clustering the multidimensional time-series of
the popularity of contents coupled with other domain-specific dimensions, we
uncover two main patterns of popularity: bursty and steady temporal behaviors.
Moreover, we find that the way popularity is gained over time has no
significant impact on the final cumulative popularity.

27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat