Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Marked Self-Exciting Point Process Modelling of Information Diffusion on
  Twitter   Information diffusion occurs on microblogging platforms like Twitter as
retweet cascades. When a tweet is posted, it may be retweeted and henceforth
further retweeted, and the retweeting process continues iteratively and
indefinitely. A natural measure of the popularity of a tweet is the number of
retweets it generates. Accurate predictions of tweet popularity can assist
Twitter to rank contents more effectively and facilitate the assessment of
potential for marketing and campaigning strategies. In this paper, we propose a
model called the Marked Self-Exciting Process with Time-Dependent Excitation
Function, or MaSEPTiDE for short, to model the retweeting dynamics and to
predict the tweet popularity. Our model does not require expensive feature
engineering but is capable of leveraging the observed dynamics to accurately
predict the future evolution of retweet cascades. We apply our proposed
methodology on a large amount of Twitter data and report substantial
improvement in prediction performance over existing approaches in the
literature.

13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg