143,071 research outputs found
Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?
This paper investigates when users create profiles in different social
networks, whether they are redundant expressions of the same persona, or they
are adapted to each platform. Using the personal webpages of 116,998 users on
About.me, we identify and extract matched user profiles on several major social
networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence
for distinct site-specific norms, such as differences in the language used in
the text of the profile self-description, and the kind of picture used as
profile image. By learning a model that robustly identifies the platform given
a user's profile image (0.657--0.829 AUC) or self-description (0.608--0.847
AUC), we confirm that users do adapt their behaviour to individual platforms in
an identifiable and learnable manner. However, different genders and age groups
adapt their behaviour differently from each other, and these differences are,
in general, consistent across different platforms. We show that differences in
social profile construction correspond to differences in how formal or informal
the platform is.Comment: Accepted at the 11th International AAAI Conference on Web and Social
Media (ICWSM17
Collaborative Feature Learning from Social Media
Image feature representation plays an essential role in image recognition and
related tasks. The current state-of-the-art feature learning paradigm is
supervised learning from labeled data. However, this paradigm requires
large-scale category labels, which limits its applicability to domains where
labels are hard to obtain. In this paper, we propose a new data-driven feature
learning paradigm which does not rely on category labels. Instead, we learn
from user behavior data collected on social media. Concretely, we use the image
relationship discovered in the latent space from the user behavior data to
guide the image feature learning. We collect a large-scale image and user
behavior dataset from Behance.net. The dataset consists of 1.9 million images
and over 300 million view records from 1.9 million users. We validate our
feature learning paradigm on this dataset and find that the learned feature
significantly outperforms the state-of-the-art image features in learning
better image similarities. We also show that the learned feature performs
competitively on various recognition benchmarks
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