1,135 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
A Local-Global LDA Model for Discovering Geographical Topics from Social Media
Micro-blogging services can track users' geo-locations when users check-in
their places or use geo-tagging which implicitly reveals locations. This "geo
tracking" can help to find topics triggered by some events in certain regions.
However, discovering such topics is very challenging because of the large
amount of noisy messages (e.g. daily conversations). This paper proposes a
method to model geographical topics, which can filter out irrelevant words by
different weights in the local and global contexts. Our method is based on the
Latent Dirichlet Allocation (LDA) model but each word is generated from either
a local or a global topic distribution by its generation probabilities. We
evaluated our model with data collected from Weibo, which is currently the most
popular micro-blogging service for Chinese. The evaluation results demonstrate
that our method outperforms other baseline methods in several metrics such as
model perplexity, two kinds of entropies and KL-divergence of discovered
topics
Using social media for air pollution detection-the case of Eastern China Smog
Air pollution has become an urgent issue that affecting public health and peopleâs daily life in China. Social media as potential air quality sensors to surveil air pollution is emphasized recently. In this research, we picked up a case-2013 Eastern China smog and focused on two of the most popular Chinese microblog platforms Sina Weibo and Tencent Weibo. The purpose of this study is to determine whether social media can be capable to be used as âsensorsâ to monitor air pollution in China and to provide an innovative model for air pollution detection through social media. Based on that, we propose our research question, how a salient change of air quality expressed on social media discussions to reflect the extent of air pollution. Hence, our research (1) determine the correlation between the volume of air quality-related messages and observed Air quality index (AQI) with the help of time series analysis model; (2) investigate further the impact of a salient change of air quality on the relationship between the peopleâs subjective perceptions regarding to air pollution released on the Weibo and the extent of air pollution through a co-word network analysis model. Our study illustrates that the discussions on social media about air quality reflect the level of air pollution when the air quality changes saliently
Sentiment Analysis for Troll Activity Detection on Sina Weibo
The impact of social media on the modern world is difficult to overstate. Virtually all companies and public figures have social media accounts on popular platforms such as Twitter and Facebook. In China, the micro-blogging service provider Sina Weibo is the most popular such service. To overcome negative publicity, Weibo trolls the so called Water Army can be hired to post deceptive comments.
In recent years, troll detection and sentiment analysis have been studied, but we are not aware of any research that considers troll detection based on sentiment analysis. In this research, we focus on troll detection via sentiment analysis with other user activity data gathered on the Sina Weibo platform, where the content is mainly in Chinese. We implement techniques for Chinese sentence segmentation, word embeddings, and sentiment score calculations. We employ the resulting techniques to develop and test a sentiment analysis approach for troll detection, based on a variety of machine learning strategies. Experimental results are generated, analyzed and the troll detection model we proposed achieved 89% accuracy for the dataset presented in this research. A Chrome extension is presented that implements our proposed technique, which enables real-time troll detection and troll comments filtering when a user browses Sina Weibo tweets and comments
FACTORS INFLUENCING USERâS CONTINUANCE INTENTION ON PAID QUESTION AND ANSWER SERVICE ----A STUDY ON WEIBO IN CHINA
This thesis addresses the research question âWhy do users continue to use paid Q&A in Chinaâ by means showed below:
First, this research introduces research background of paid Q&A in China and raises corresponding research question and highlights the research significance of this thesis topic;
Second, the author concludes previous research on paid Q&A in aspects of Q&A system, paid subscription and sharing economy, and finds that most of prior research focuses on exploring the influence of usefulness but not enjoyment on the usersâ willingness of continuing using a paid Q&A system;
Third, the thesis introduces the VAM theory and build a modified model based on it, this modified model highlights the importance of pleasure on usersâ continuance intention in using paid Q&A;
Finally, the empirical study combining an Exploratory Factor Analysis and a Confirmatory Factor Analysis proves that, after integrating factors extracted from previous research and the proposed model, the research is tested to be explanatorily capable and hypotheses related to the model are mostly proved to be supported.
As a conclusion, this study conducts an investigation on the constructs and related theories that influence usersâ continuance intention to use paid Q&A, from a hedonic perspective. In this thesis, VAM theory is selected as the prototype of proposed research model which reveals factors affecting usersâ continuance intention to use a Chinese paid Q&A product named Weibo Paid Q&A. In this thesis, the proposed model makes predictions that the constructs perceived fee and community atmosphere along with perceived enjoyment construct have critical effect on usersâ continuance willingness in using Weibo Paid Q&A in China. With the assistance of PLSâSEM, this study analyzes data collected from users in WPQA, the empirical study verifies that users' continuance intention is assuredly dependent on perceived fee and community atmosphere along with perceived enjoyment. The study also reveals that quality of answerers and quality of answer positively exert significant influences on perceived enjoyment
Biaoqing on Chinese Social Media:Practices, products, communities and markets in a knowledge economy
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