670 research outputs found
Follow Whom? Chinese Users Have Different Choice
Sina Weibo, which was launched in 2009, is the most popular Chinese
micro-blogging service. It has been reported that Sina Weibo has more than 400
million registered users by the end of the third quarter in 2012. Sina Weibo
and Twitter have a lot in common, however, in terms of the following
preference, Sina Weibo users, most of whom are Chinese, behave differently
compared with those of Twitter.
This work is based on a data set of Sina Weibo which contains 80.8 million
users' profiles and 7.2 billion relations and a large data set of Twitter.
Firstly some basic features of Sina Weibo and Twitter are analyzed such as
degree and activeness distribution, correlation between degree and activeness,
and the degree of separation. Then the following preference is investigated by
studying the assortative mixing, friend similarities, following distribution,
edge balance ratio, and ranking correlation, where edge balance ratio is newly
proposed to measure balance property of graphs. It is found that Sina Weibo has
a lower reciprocity rate, more positive balanced relations and is more
disassortative. Coinciding with Asian traditional culture, the following
preference of Sina Weibo users is more concentrated and hierarchical: they are
more likely to follow people at higher or the same social levels and less
likely to follow people lower than themselves. In contrast, the same kind of
following preference is weaker in Twitter. Twitter users are open as they
follow people from levels, which accords with its global characteristic and the
prevalence of western civilization. The message forwarding behavior is studied
by displaying the propagation levels, delays, and critical users. The following
preference derives from not only the usage habits but also underlying reasons
such as personalities and social moralities that is worthy of future research.Comment: 9 pages, 13 figure
Sensing Subjective Well-being from Social Media
Subjective Well-being(SWB), which refers to how people experience the quality
of their lives, is of great use to public policy-makers as well as economic,
sociological research, etc. Traditionally, the measurement of SWB relies on
time-consuming and costly self-report questionnaires. Nowadays, people are
motivated to share their experiences and feelings on social media, so we
propose to sense SWB from the vast user generated data on social media. By
utilizing 1785 users' social media data with SWB labels, we train machine
learning models that are able to "sense" individual SWB from users' social
media. Our model, which attains the state-by-art prediction accuracy, can then
be used to identify SWB of large population of social media users in time with
very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT
2014, Warsaw, Poland, August 11-14, 2014. Proceeding
Toward Order-of-Magnitude Cascade Prediction
When a piece of information (microblog, photograph, video, link, etc.) starts
to spread in a social network, an important question arises: will it spread to
"viral" proportions -- where "viral" is defined as an order-of-magnitude
increase. However, several previous studies have established that cascade size
and frequency are related through a power-law - which leads to a severe
imbalance in this classification problem. In this paper, we devise a suite of
measurements based on "structural diversity" -- the variety of social contexts
(communities) in which individuals partaking in a given cascade engage. We
demonstrate these measures are able to distinguish viral from non-viral
cascades, despite the severe imbalance of the data for this problem. Further,
we leverage these measurements as features in a classification approach,
successfully predicting microblogs that grow from 50 to 500 reposts with
precision of 0.69 and recall of 0.52 for the viral class - despite this class
comprising under 2\% of samples. This significantly outperforms our baseline
approach as well as the current state-of-the-art. Our work also demonstrates
how we can tradeoff between precision and recall.Comment: 4 pages, 15 figures, ASONAM 2015 poster pape
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