To what extent user's stance towards a given topic could be inferred? Most of
the studies on stance detection have focused on analysing user's posts on a
given topic to predict the stance. However, the stance in social media can be
inferred from a mixture of signals that might reflect user's beliefs including
posts and online interactions. This paper examines various online features of
users to detect their stance towards different topics. We compare multiple set
of features, including on-topic content, network interactions, user's
preferences, and online network connections. Our objective is to understand the
online signals that can reveal the users' stance. Experimentation is applied on
tweets dataset from the SemEval stance detection task, which covers five
topics. Results show that stance of a user can be detected with multiple
signals of user's online activity, including their posts on the topic, the
network they interact with or follow, the websites they visit, and the content
they like. The performance of the stance modelling using different network
features are comparable with the state-of-the-art reported model that used
textual content only. In addition, combining network and content features leads
to the highest reported performance to date on the SemEval dataset with
F-measure of 72.49%. We further present an extensive analysis to show how these
different set of features can reveal stance. Our findings have distinct privacy
implications, where they highlight that stance is strongly embedded in user's
online social network that, in principle, individuals can be profiled from
their interactions and connections even when they do not post about the topic.

本文旨在探讨利用用户在社交媒体上的行为特征来检测其对不同主题的立场。实验结果表明，用户的立场可以通过其相对应的社交网络特征得到比较好的检测，同时发现这些特征具有一定的隐私影响。