presentation

1. “Roles for the Boys ?” Mining
Cast Lists for Gender and Role
Distributions over Time
Speaker: 陳彥彤
January 11, 2016

2. Outline
 Introduction
 Dataset and the Methodology
 Role
 Gender
 Conclusions

3. Introduction
 Film and television are an integral part of culture and one
way that people understand and interact with it
 However studies that require watching and annotating
video are time-consuming and expensive to run at scale
 This paper explore from media database cast lists to
explore the evolution of different roles for gender over
time

4. Dataset and the Methodology
 Internet Movie Database (IMDb)
IMDb is the world's most popular and
authoritative source for movie, TV and
celebrity content.

5. Dataset and the Methodology
 The dataset are available from IMDb and
examine cast lists
 Cast list contains performer names,
images and the character name
 Exploit following three factors from data
 Release date
 Gender
 Role

6. Dataset and the Methodology
 We downloaded the plain text data files actors.list.gz and actresses.list.gz
 Applied several cleaning phases
 filtering roles marked n/a, or reference selves (e.g. himself or herself)
 Remove any text in parentheses
 split multi-role characters
 p(F|r, y) is the count of records with role r in year y by a performer from the
actresses list
 p(M|r, y) = 1 - p(F|r, y)

7. Role
 The dataset show what roles are popular in onscreen media and how
has this changed over time
 While the analysis shows the enduring popularity of hosted screen
entertainment, this can obscure some of the emerging roles through
time

8. Role
 For the same period, which roles are new and did not
appear in the top 50 roles of the previous period

9. Gender
 One of the most valuable characteristics of dataset is that
each performer has gender information.
 Aggregating by role allows us to consider
biases of the gender of onscreen roles

10. Gender
 The author also analyze the gender distribution of common
roles to characterise how gender relates to roles at a high
level

11. Gender
 The author then analyze dataset and show strong biases in the
representation of executive roles, looking for key roles in areas
 IT, Doctor, Corporate, Law, Politics, Science, Religion, Engineering

12. Gender
 The analyses to this point have only
referenced IMDb data
 It is also interesting to compare with their
real-world counterparts

13. Conclusions
 This paper presents methodologies for mining information
about onscreen media gender from cast lists.
 The IMDb data release does not report the information of
country directly and it would have to be inferred