Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Selfie is Worth a Thousand Words: Mining Personal Patterns behind User
  Selfie-posting Behaviours   Selfies have become increasingly fashionable in the social media era. People
are willing to share their selfies in various social media platforms such as
Facebook, Instagram and Flicker. The popularity of selfie have caught
researchers' attention, especially psychologists. In computer vision and
machine learning areas, little attention has been paid to this phenomenon as a
valuable data source. In this paper, we focus on exploring the deeper personal
patterns behind people's different kinds of selfie-posting behaviours. We
develop this work based on a dataset of WeChat, one of the most extensively
used instant messaging platform in China. In particular, we first propose an
unsupervised approach to classify the images posted by users. Based on the
classification result, we construct three types of user-level features that
reflect user preference, activity and posting habit. Based on these features,
for a series of selfie related tasks, we build classifiers that can accurately
predict two sets of users with opposite selfie-posting behaviours. We have
found that people's interest, activity and posting habit have a great influence
on their selfie-posting behaviours. For example, the classification accuracy
between selfie-posting addict and nonaddict reaches 89.36%. We also prove that
using user's image information to predict these behaviours achieve better
performance than using text information. More importantly, for each set of
users with a specific selfie-posting behaviour, we extract and visualize
significant personal patterns about them. In addition, we cluster users and
extract their high-level attributes, revealing the correlation between these
attributes and users' selfie-posting behaviours. In the end, we demonstrate
that users' selfie-posting behaviour, as a good predictor, could predict their
different preferences toward these high-level attributes accurately.

17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an