The study, which also uncovers how media communicates about race and age, was able to quantify the sophistication and the tone of language of 7,000 characters and over 53,000 dialogues in nearly 1,000 film scripts.
Researchers from University of Southern California in the US analysed content of characters' language and their interactions across gender, race and age.
They looked at cast, genre, the production teams across films including writers, directors and casting agents.
"In an ideal world, gender is in an auxiliary fact, it has nothing to do with the way actors are presented and what they say," said Ramakrishna.
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Of the scripts and dialogues reviewed, men had over 37,000 dialogues; women had just over 15,000. Women portrayed just over 2,000 characters, while men portrayed almost 4,900.
Overall, female characters regardless of race, tended to be about five years younger than their male counterparts.
Researchers found that the dialogue of Latino and mixed- race characters had more dialogue related to sexuality. African-American characters had a greater percentage of swear words in their dialogues than other races.
Overall, researchers found that female characters tend to be more positive but this tended to be correlated with using language connecting with family values.
Beyond the volume of dialogue attributed to men, male dialogue contained more words related to achievement, death and more swear words than the dialogue scripted for women.
In addition, the language of elder characters steers towards what has most traditionally associated with men.
In additional to the content of a dialogue, the researchers used graph theory to determine how central characters are to the plot of a movie by analyzing the ties and relationships to the other characters within the film.
They then model the network and web of relationships between the characters in a similar fashion to the way one would study a transit hub.
The exception was when women were in horror movies when they were most likely to be portrayed as victims. Thus, to leave female characters out did not cause much of a disruption.
"Computational language analysis and interaction modeling tools allow us to understand not just what someone says, but how they say it, how much they say, to whom they speak and in what context, thereby offering new insights into media content and its potential impact on people," said Professor Shrikanth Narayanan from University of Southern California.