The #MeToo movement has encouraged women to share their personal stories of sexual harassment. While the movement amplifies previously unheard voices, a Carnegie Mellon University analysis of #MeToo media coverage shows accusers are often portrayed as sympathetic, but with less power and agency than their alleged perpetrators.

"The goal of the movement is to empower women, but according to our computational analysis that's not what's happening in news stories," said Yulia Tsvetkov, assistant professor in the School of Computer Science's Language Technologies Institute.

Tsvetkov's research team used natural language processing (NLP) techniques to analyze online media coverage of #MeToo narratives that included 27,602 articles in 1,576 outlets. In a paper published earlier this year, they also looked at how different media outlets portrayed perpetrators, and considered the role of third-party actors in news stories.

"Bias can be unconscious, veiled and hidden in a seemingly positive narrative," Tsvetkov said. "Such subtle forms of biased language can be much harder to detect and to date we have no systematic way of identifying them automatically. The goal of our research was to provide tools to analyze such biased framing."

Their work draws insights from social psychology research, and looks at the dynamics of power, agency and sentiment, which is a measurement of sympathy. The researchers analyzed verbs to understand their meaning, and put them into context to discern their connotation. Take, for instance, the verb "deserves." In the sentence "The boy deserves praise," the verb takes on a very different meaning than in the context of "The boy deserves to be punished."

"We were inspired by previous work that looked at the meaning of verbs in individual sentences," Tsvetkov said. "Our analysis incorporates context." This method allowed her team to consider much longer chunks of text and to analyze narrative.

The research team developed ways to generate scores for words in context, and mapped out the power, sentiment and agency of each actor within a news story. Their results show that the media consistently presents men as powerful, even after sexual harassment allegations. Tsvetkov said this threatens to undermine the goals of the #MeToo movement, which is often characterized as "empowerment through empathy."

The team's analysis also showed that the people portrayed with the most positive sentiment in #MeToo stories were those not directly involved with allegations, like activists, journalists or celebrities commenting on the movement, such as Oprah Winfrey.

A supplementary paper extending the analysis was presented by graduate student Anjalie Field in Florence, Italy, last month at the Association for Computational Linguistics conference. This paper proposes different methods for measuring power, agency and sentiment, and analyzes the portrayals of characters in movie plots, as well as prominent members of society in general newspaper articles.

One of the consistent trends detected in both papers is that women are portrayed as less powerful than men. This was evident in an analysis of the 2016 Forbes list of most powerful people. In news stories from myriad outlets about women and men who ranked similarly, men were consistently described as being more powerful.

"These methodologies can extend beyond just people," Tsvetkov said. "You could look at narratives around countries, if they are described as powerful and sympathetic, or unfriendly, and compare that with reactions on social media to understand the language of manipulation, and how people actually express their personal opinions as a consequence of different narratives."

Tsvetkov said she hopes this work will raise awareness of the importance of media framing.

"Journalists can choose which narratives to highlight in order to promote certain portrayals of people," she said. "They can encourage or undermine movements like #MeToo. We also hope that the tools we developed will be useful to social and political scientists, to analyze narratives about people and abstract entities such as markets and countries, and to improve our understanding of the media landscape by analyzing large volumes of texts."