StoryFlow:
Tracking the
Evolution of Stories
Shixia Liu,
Senior Member, IEEE,
Microsoft Research
Asia
Yingcai Wu,
Member, IEEE,
Microsoft Research
Asia
Yang Liu,
Microsoft Research
Asia
Enxun Wei,
Shanghai Jiao Tong
University
IEEE INFOVIS 2013
Mengchen Liu,
Tsinghua University
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• Understanding how entity relationships evolve
from the beginning to the end in a story is very
important.
• Existing visualization techniques either produce an
aesthetically pleasing and legible storyline picture
with much time.
• An interactive visualization with too many wiggly
lines and too much visual inconsistency.
• May not fully support real-world storytelling and
analysis tasks.
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• Fails to meet the requirements of real-time
interactions. The method proposed by Tanahashi
et al. may take hours to generate a storyline with
hundreds of entities and hundreds of time frames.
• In many real-world applications, settings/locations
are naturally organized in a hierarchy.
• The existing visualizations are not designed to
accommodate more than hundreds of entity lines.
They cannot provide legible results when the
number of entities is in the thousands or even
hundreds.
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• Hybrid optimization strategy:
• The discrete method creates an initial layout
through ordering and aligning line entities.
• The continuous method optimizes the layout
based on quadratic convex optimization.
• The efficient algorithm enables a rich set of realtime user interactions, including adding, removing,
dragging, straightening, and bundling line entities.
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• One drawback of genetic algorithms is their
expensive computational cost.
• Genetic algorithm has drawbacks such as
premature convergence and local optima.
• Line crossings:
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• StoryFlow: 25
• TM: 80
• Original: 53
Line wiggles:
StoryFlow: 110
TM: 110
Original: 107
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• Twitter dataset
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• 2012 US presidential election
• 89,174,308 tweets
• Query word: ”Obama”, ”Romney” and “election”
• Film professor:
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• It is a great way to show the interactions between
characters over time, which can definitely help
filmmaking.
• Film directors:
• StoryFlow enables a fast review of the shooting timetable
and allows the directors to make a better decision on the
most advantageous shot order.
• Script adapters:
• Use Storyflow to review a script quickly to decide whether
to add or remove certain characters or scenes, also be used
as an effective tool to communicate their ideas to the film
directors and producers.
• Actors:
• Use StoryFlow to better trace their related scenes and see
immediately where and who they will interact with, so that
they can better prepare for their performance.
• Sociology PhD student
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• Level-of-detail feature of StoryFlow, which enables to
immediately see the overall patterns as well as to
directly interact with the visualization to see more
detail.
• Interesting to see the dynamic relationships of the
liberal and conservative opinion leaders over time.
• A professor in media and communication studies
• StoryFlow would be particularly useful for data-driven
journalism because it not only provides a clear visual
summary of events but also shows informative context
for investigative analysis.
• Adding sentiment information to the StoryFlow
visualization to provide richer context for further
analysis.
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• Investigate which interactions are useful for what
kind of analysis tasks.
• An entity only belongs to one session at one time.
• The timeline in StoryFlow is linear, which does not
scale well with thousands of time frames.
• For a simple flashback, can still leverage the
StoryFlow visualization, while for a narration
interspersed with flashbacks, it is quite challenging
to illustrate the story with one storyline layout.
Motivation
System Overview
Evaluation
Case Studies
User Feedback
Future Work
Conclusion
• Presented an efficient optimization approach to
generate a storyline layout with thousands of
entities and hundreds of time frames.
• Discrete optimization to minimize the number of
line crossings and wiggles, and continuous
optimization for minimizing the wiggle distance
and white space, can quickly achieve a better local
optimum.