  False news that spreads on social media has proliferated over the past years
and has led to multi-aspect threats in the real world. While there are studies
of false news on specific domains (like politics or health care), little work
is found comparing false news across domains. In this article, we investigate
false news across nine domains on Weibo, the largest Twitter-like social media
platform in China, from 2009 to 2019. The newly collected data comprise 44,728
posts in the nine domains, published by 40,215 users, and reposted over 3.4
million times. Based on the distributions and spreads of the multi-domain
dataset, we observe that false news in domains that are close to daily life
like health and medicine generated more posts but diffused less effectively
than those in other domains like politics, and that political false news had
the most effective capacity for diffusion. The widely diffused false news posts
on Weibo were associated strongly with certain types of users -- by gender,
age, etc. Further, these posts provoked strong emotions in the reposts and
diffused further with the active engagement of false-news starters. Our
findings have the potential to help design false news detection systems in
suspicious news discovery, veracity prediction, and display and explanation.
The comparison of the findings on Weibo with those of existing work
demonstrates nuanced patterns, suggesting the need for more research on data
from diverse platforms, countries, or languages to tackle the global issue of
false news. The code and new anonymized dataset are available at
https://github.com/ICTMCG/Characterizing-Weibo-Multi-Domain-False-News.
