A Framework towards Computational Narrative Analysis on Blogs
Title:
A Framework towards Computational Narrative Analysis on Blogs
Authors:
Kiran Kumar Bandeli, Muhammad Nihal Hussain, Nitin Agarwal
Abstract:
Social media is widely used to express views and share opinions
with others. With the availability of inexpensive and ubiquitous
mass communication tools like social media, creating narratives, falseinformation and propaganda is both convenient and effective. Social
media users leverage this platform to further their views by framing
narratives and participating in online discourse. Almost all events, issues, crises are discussed on social media. Blogs, unlike other social
media platforms, are not regulated by any authority and have no restriction on character limit, providing bloggers with not only space
for richer content but also serve as a platform for agenda-setting and
content framing abetting development of narratives. This innate feature of blogs makes them a valuable platform for sociologists/political
scientists to gain situational awareness by tracking di↵erent opinions,
political views, and narratives as they are shaped. However, the deluge
of posts in blogosphere makes it impractical to manually identify narratives. In this paper, we propose a novel framework to computationally
identify narratives by extracting actors/actions using NLP techniques
including POS tagging, chunking, and grammar rules to identify narratives. We also employ a scoring mechanism to rank them in the
order of their dominance. Later, we evaluate the ecacy of the proposed model by validating against human annotated narratives. Our
framework achieved an accuracy of 66.8%. Our proposed framework
can help social scientists identify narratives computationally reducing
human e↵ort reasonably. Moreover, the results from this research could
also be used to build e↵ective counter narratives to stem propaganda
campaigns.
Proceedings: http://ceur-ws.org/Vol-2593/