6 research outputs found
Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media
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.Comment: Accepted as a full paper at CSCW 2019. Please cite the CSCW versio
Stance detection on social media: State of the art and trends
Stance detection on social media is an emerging opinion mining paradigm for
various social and political applications in which sentiment analysis may be
sub-optimal. There has been a growing research interest for developing
effective methods for stance detection methods varying among multiple
communities including natural language processing, web science, and social
computing. This paper surveys the work on stance detection within those
communities and situates its usage within current opinion mining techniques in
social media. It presents an exhaustive review of stance detection techniques
on social media, including the task definition, different types of targets in
stance detection, features set used, and various machine learning approaches
applied. The survey reports state-of-the-art results on the existing benchmark
datasets on stance detection, and discusses the most effective approaches. In
addition, this study explores the emerging trends and different applications of
stance detection on social media. The study concludes by discussing the gaps in
the current existing research and highlights the possible future directions for
stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this
paper. Please withdraw this article before we finish the new versio