This document discusses using social media data like tweets to analyze movie performance and risk. It provides an overview of film history, risks in the movie industry, and previous research analyzing factors like Wikipedia views and expert reviews. It focuses on analyzing tweets about movies, finding that tweet volume can predict gross revenue. It also introduces the Multilevel Model of Meme Diffusion framework for studying how memes and messages diffuse through social networks, communicators, and other societal and geographic factors.

2. Hashtags, Tweets and Movie
Receipts
Mario E. Sanguinet
Fall 2015

3. Overview
• Film History-ish
• Film Risks
• Previous Research
• Twitter
• M3D

4. Film History
The Jazz Singer (1927) Jaws (1975)

5. Film History
• Overseas
• “Creative industries” (US BEA 2014, 2015)
– $504 billion/3.2%
– $699 billion/4.32%

6. Risk
• Pan
• Tomorrowland
• Jupiter Ascending
– $175m
– $18m
• Mitigate & Predict


7. Risk
• “ethical marketing”
• Don’t make crappy movies/trailers
– (Escoffier & McKelvey, 2015)

8. Previous Research
General
• Germany
• Wikipedia
• Views/Comments
• Expert reviews
Twitter
• Tweets  Gross revenue
– By hour
– By week
• Sentiment analysis

9. Twitter
Why Twitter?
• SNS activity increases
– revenues by 64%
– “search activity” by 48%
• Crowd Wisdom
– 23%
• Public data
• Easily searchable
– #hashtags

10. M3D
Multilevel Model of Meme Diffusion (M3D)
• Memes/social media messages
• Communicators
• Structural and subjective network
structures
• Societal processes
• Geo-technical factors

11. Questions?