  Identifying user stance related to a political event has several
applications, like determination of individual stance, shaping of public
opinion, identifying popularity of government measures and many others. The
huge volume of political discussions on social media platforms, like, Twitter,
provide opportunities in developing automated mechanisms to identify individual
stance and subsequently, scale to a large volume of users. However, issues like
short text and huge variance in the vocabulary of the tweets make such exercise
enormously difficult. Existing stance detection algorithms require either event
specific training data or annotated twitter handles and therefore, are
difficult to adapt to new events. In this paper, we propose a sign network
based framework that use external information sources, like news articles to
create a signed network of relevant entities with respect to a news event and
subsequently use the same to detect stance of any tweet towards the event.
Validation on 5,000 tweets related to 10 events indicates that the proposed
approach can ensure over 6.5% increase in average F1 score compared to the
existing stance detection approaches.
