Paper detail

Topical differences between Chinese language Twitter and Sina Weibo

Sina Weibo, China's most popular microblogging platform, is currently used by over $500M$ users and is considered to be a proxy of Chinese social life. In this study, we contrast the discussions occurring on Sina Weibo and on Chinese language Twitter in order to observe two different strands of Chinese culture: people within China who use Sina Weibo with its government imposed restrictions and those outside that are free to speak completely anonymously. We first propose a simple ad-hoc algorithm to identify topics of Tweets and Weibo. Different from previous works on micro-message topic detection, our algorithm considers topics of the same contents but with different \#tags. Our algorithm can also detect topics for Tweets and Weibos without any \#tags. Using a large corpus of Weibo and Chinese language tweets, covering the period from January $1$ to December $31$, $2012$, we obtain a list of topics using clustered \#tags that we can then use to compare the two platforms. Surprisingly, we find that there are no common entries among the Top $100$ most popular topics. Furthermore, only $9.2\%$ of tweets correspond to the Top $1000$ topics on Sina Weibo platform, and conversely only $4.4\%$ of weibos were found to discuss the most popular Twitter topics. Our results reveal significant differences in social attention on the two platforms, with most popular topics on Sina Weibo relating to entertainment while most tweets corresponded to cultural or political contents that is practically non existent in Sina Weibo.

preprint2015arXivOpen access

Signal facts

What is known right now

Open access2 authors3 topics

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.