Contextual Affective Analysis: A Case Study of People Portrayals in Online #MeToo Stories
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Abstract (arXiv)
In October 2017, numerous women accused producer Harvey Weinstein of sexual harassment. Their stories encouraged other women to voice allegations of sexual harassment against many high profile men, including politicians, actors, and producers. These events are broadly referred to as the #MeToo movement, named for the use of the hashtag "#metoo" on social media platforms like Twitter and Facebook. The movement has widely been referred to as "empowering" because it has amplified the voices of previously unheard women over those of traditionally powerful men. In this work, we investigate dynamics of sentiment, power and agency in online media coverage of these events. Using a corpus of online media articles about the #MeToo movement, we present a contextual affective analysis---an entity-centric approach that uses contextualized lexicons to examine how people are portrayed in media articles. We show that while these articles are sympathetic towards women who have experienced sexual harassment, they consistently present men as most powerful, even after sexual assault allegations. While we focus on media coverage of the #MeToo movement, our method for contextual affective analysis readily generalizes to other domains.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Apr 2019.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 3; 2024: 1.
7 papers.
- 2021A Survey on Gender Bias in Natural Language Processingcited by 89
- 2024Are Large Language Models Capable of Generating Human-Level Narratives?cited by 79
- 2021To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violencecited by 12
- 2021"Don't quote me on that": Finding Mixtures of Sources in News Articlescited by 5
- 2023Riveter: Measuring Power and Social Dynamics Between Entitiescited by 2
- 2023Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharingcited by 1
- 2023Extracting Incidents, Effects, and Requested Advice from MeToo Postscited by 0
- 2024Are Large Language Models Capable of Generating Human-Level Narratives?18 Jul 2024
- 2023Riveter: Measuring Power and Social Dynamics Between Entities15 Dec 2023
- 2023Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing16 May 2023
- 2023Extracting Incidents, Effects, and Requested Advice from MeToo Posts19 Mar 2023
- 2021A Survey on Gender Bias in Natural Language Processing28 Dec 2021
- 2021To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence11 Sep 2021
- 2021"Don't quote me on that": Finding Mixtures of Sources in News Articles19 Apr 2021