Gender Stereotypes in Hollywood Movies and Their Evolution over Time: Insights from Network Analysis
2. Materials and Methods
2.1. Description of Data Source
2.2. Network Construction
2.2.1. Common Themes for Male and Female Characters in Movies
2.2.2. The Lives of Male and Female Characters in Movies
- Gender-specific story tropes and their evolution. The main goal of this analysis was to find the most significant story tropes associated with male and female characters across the entire time period of analysis (i.e., from 1940 to 2019). To this end, we used a novel method involving path analysis to identify the most significant story tropes in movies. First, we identified the most significant association of each of the primary vertices. Next, we computed the (weighted) path length from the gender vertices to these secondary vertices, through a path described by: gender vertex–primary vertex–secondary vertex. This resulted in 3-tuples of vertices and their path weights, which showed how significant they were. For example, along the path described by ‘female/characters–love/noun–fell/verb’, the cumulative weight would be the sum of the log-likelihood ratio significances (i.e., the weights) along the path. In other words, it would be the sum of LL (female/characters–love/noun) + LL (love/noun–fell/verb). Rather than simply inferring story tropes from primary vertices alone, the 3-tuples with the addition of secondary vertices provided us with additional context to infer story tropes of each gender. Twenty significant character-specific paths were obtained for each character, one for each of their primary associations. Figure 2 depicts the ten most significant paths for each gender within the co-occurrence network.
- Roles, actions, and descriptions of male and female characters and their evolution. In this part, three network visualisations were created by subsetting the network formed previously based on three word classes: nouns, verbs, and adjectives. The vertices connected to edges of the gender vertices with the highest weight were analysed to find the most common associations for male and female characters. By analysing separate networks of nouns, verbs, and adjectives, we aimed to identify the top 20 most significant roles, actions, and descriptions, respectively, that were associated with male and female characters.
3.1. Common Themes for Male and Female Characters in Hollywood Movies
3.2. Gender-Specific Story Tropes and Their Evolution
3.3. Roles, Actions, and Descriptions of Male and Female Characters
3.3.1. Nouns: What Roles Do Male and Female Characters Play?
3.3.2. Verbs: What Do Male and Female Characters Do?
3.3.3. Adjectives: How Are Male and Female Characters Described?
4.1. Community Analysis
4.2. Trope Analysis
4.3. Edge-Weight Analysis
4.4. Summary of Results
4.5. Real-Life Implications
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|a. Male Character||b. Female Character|
|Community||Number of Vertices||Community||Number of Vertices|
|Male-kill-attempts * [increasing]||0.55||0.40||0.03|
|Female-fall-love * [decreasing]||0.73||−3.89||0.01|
|Wife * [decreasing]||0.58||−0.62||0.03|
|Love * [decreasing]||0.59||−1.03||0.03|
|Relationship + [increasing]||0.43||0.77||0.08|
|Wedding + [increasing]||0.45||0.29||0.07|
|Widow * [decreasing]||0.63||−0.44||0.02|
|Kill * [increasing]||0.55||0.17||0.03|
|Marry ** [decreasing]||0.89||−1.15||<0.01|
|Beautiful * [decreasing]||0.70||−0.37||0.01|
|Attractive * [decreasing]||0.69||−0.32||0.02|
|Story trope||Male (top 20)||male–friend–old, male–named–woman, male–brother–older, male–is–able,|
male-son–eldest, male–tells--wants, male–wife–children, male–takes–liking, male–former–turned, male–agent–government, male–kill–attempts, male–meets–bar, male–help-seeks,
male–asks-help, male–partner–new, male–arrives–time, male–father–stepmother, male–meet–ends, male–girlfriend–dumped, male–learns–language
|Female (top 20)||female–love–fall, female–daughter–grown, female–sister–younger, female–wife–children, female–named–young, female–relationship–romantic, female–husband–abusive, female–girlfriend–dumped, female–mother–single, female–marriage–proposal, female–tells–wants, female–house–beach, female–affair–extramarital, female–marry–intends, female–meets–bar, female–girl–chorus, female–woman–elderly, female–pregnant–abortion, female–boyfriend–player,|
male–kill–attempts * [increasing] (R2 = 0.55, beta = 0.40, p = 0.03)
female–fall–love * [decreasing] (R2 = 0.73, beta = −3.89, p = 0.01)
|friend, brother, son, wife, partner, agent, father, girlfriend, boyfriend, help, boss, death, attorney, manager, owner, women, people, murder, gun, office|
|daughter, sister, love, mother, husband, girlfriend, wife, relationship, boyfriend, affair, house, marriage, girl, woman, wedding, friend, date, actress, home, feelings|
death, murder, gun
wife * [decreasing] (R2 = 0.58, beta = −0.62, p = 0.03), girlfriend, boyfriend
love * [decreasing] (R2 = 0.59, beta = −1.03, p = 0.03), girlfriend, wife * [decreasing] (R2 = 0.67, beta = −1.15, p = 0.01), relationship + [increasing] (R2 = 0.43, beta = 0.77, p = 0.08), affair, marriage, wedding + [increasing] (R2 = 0.45, beta = 0.29, p = 0.07), crush, widow * [decreasing] (R2 = 0.63, beta = −0.44, p = 0.02)
|named, meets, tells, is, arrives, kill, takes, meet, learns, asks, killed, visits, hires, led, returns, kills, suspects, calls, convinces, sends|
|Female (top 20)||meets, marry, named, married, tells, is, dating, having, meet, marries, attracted, asks, goes, loves, leave, returns, visits, lives, marrying, invites|
kill * [increasing] (R2 = 0.55, beta = 0.17, p = 0.03)
marry ** [decreasing] (R2 = 0.89, beta = −1.15, p < 0.01), attracted, loves, dating
|Adjective||Male (top 20)||former, wealthy, best, jealous, married, suspicious, young, new, undercover, many, old, real, older, younger, private, interested, local, corrupt, about, handsome|
|Female (top 20)||pregnant, married, beautiful, young, jealous, romantic, wealthy, teenage, attractive, best, younger, socialite, girlfriend, estranged, older, upset, wife, former, military, large|
beautiful * [decreasing] (R2 = 0.70, beta = −0.37, p = 0.01), attractive * [decreasing] (R2 = 0.69, beta = −0.32, p = 0.02), married, romantic
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Kumar, A.M.; Goh, J.Y.Q.; Tan, T.H.H.; Siew, C.S.Q. Gender Stereotypes in Hollywood Movies and Their Evolution over Time: Insights from Network Analysis. Big Data Cogn. Comput. 2022, 6, 50. https://doi.org/10.3390/bdcc6020050
Kumar AM, Goh JYQ, Tan THH, Siew CSQ. Gender Stereotypes in Hollywood Movies and Their Evolution over Time: Insights from Network Analysis. Big Data and Cognitive Computing. 2022; 6(2):50. https://doi.org/10.3390/bdcc6020050Chicago/Turabian Style
Kumar, Arjun M., Jasmine Y. Q. Goh, Tiffany H. H. Tan, and Cynthia S. Q. Siew. 2022. "Gender Stereotypes in Hollywood Movies and Their Evolution over Time: Insights from Network Analysis" Big Data and Cognitive Computing 6, no. 2: 50. https://doi.org/10.3390/bdcc6020050