20 research outputs found
Stance characterization and detection on social media
Stance detection refers to the task of identifying a viewpoint as either supporting or
opposing a given topic. The current research on socio-political opinion mining on
social media is still in its infancy. Most computational approaches in this field are
limited to the independent use of textual elements of a user’s posts from social factors
such as homophily and network structure. This thesis provides a thorough study of
stance detection on social media and assesses various online signals to identify the
stance and understand its association with the analysed topic. We explore the task of
detecting stance on Twitter, which is a well-known social media platform where people
often express stance implicitly or explicitly.
First, we examine the relation between sentiment and stance and analyse the inter-play between sentiment polarity and expressed stance. For this purpose, we extend the
current SemEval stance dataset by annotating tweets related to four new topics with
sentiment and stance labels. Then, we evaluate the effectiveness of sentiment analysis
methods on stance prediction using two stance datasets.
Second, we examine the multi-modal representation of stance on social media by
evaluating multiple stance detection models using textual content and online interactions. The finding of this chapter suggests that using social interactions along with
other textual features can improve the stance detection model. Moreover, we show
how an unconscious social interaction can reveal the stance.
Next, we design an online framework to preserve users’ privacy concerning the
implicitly inferred stance on social media. Thus, we evaluate the effectiveness of the
two stance obfuscation methods and use different stance detection models to measure
the overall performance of the proposed framework.
Finally, we study the dynamics of polarized stance to understand the factors that
influence online stance. Particularly, we extend the analysis of online stance signals
and examine the interplay between stance and automated accounts (bots). Furthermore,
we pose the problem of gauging the bots’ effect on polarized stance through a sole
focus on the diffusion of bots on the online social network