StoryFlow : Tracking the Evolution of Stories IEEE INFOVIS 2013

1. StoryFlow: Tracking the Evolution of Stories IEEE INFOVIS 2013 Shixia Liu, Senior Member, IEEE, Microsoft Research Asia YingcaiWu, Member, IEEE, Microsoft Research Asia Yang Liu, Microsoft Research Asia Enxun Wei, Shanghai Jiao Tong University Mengchen Liu, Tsinghua University

2. 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.

3. Motivation System Overview Evaluation Case Studies User Feedback Future Work Conclusion • Failsto 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.

4. 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 real-time user interactions, including adding, removing, dragging, straightening, and bundling line entities.

5. Motivation System Overview Evaluation Case Studies User Feedback Future Work Conclusion

6. 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.

7. Motivation System Overview Evaluation Case Studies User Feedback Future Work Conclusion • Line wiggles: • StoryFlow: 110 • TM: 110 • Original: 107 • Line crossings: • StoryFlow: 25 • TM: 80 • Original: 53

8. Motivation System Overview Evaluation Case Studies User Feedback Future Work Conclusion

9. Motivation System Overview Evaluation Case Studies User Feedback Future Work Conclusion • Twitter dataset • 2012USpresidential election • 89,174,308 tweets • Query word: ”Obama”, ”Romney” and “election”

10. Motivation System Overview Evaluation Case Studies User Feedback Future Work Conclusion • Film professor: • It is a great way to show the interactions between characters over time, which can definitely help filmmaking. • Film directors: • StoryFlowenables 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 StoryFlowto better trace their related scenes and see immediately where and who they will interact with, so that they can better prepare for their performance.

11. Motivation System Overview Evaluation Case Studies User Feedback Future Work Conclusion • Sociology PhD student • 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 • StoryFlowwould 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 StoryFlowvisualization to provide richer context for further analysis.

12. 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.

13. 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 quicklyachieve a better local optimum.