It’s going to be okay: Measuring Access to Support in Online Communities

By Zijian Wang, David Jurgens. 2018

58 citations recorded by the source corpus.

People use online platforms to seek out support for their informational and emotional needs. Here, we ask what effect does revealing one’s gender have on receiving support. To answer this, we create (i) a new dataset and method for identifying supportive replies and (ii) new methods for inferring gender from text and name. We apply these methods to create a new massive corpus of 102M online interactions with gender-labeled users, each rated by degree of supportiveness. Our analysis shows wide-spread and consistent disparity in support: identifying as a woman is associated with higher rates of support - but also higher rates of disparagement.

Subfields: Communication, Gender Studies, Natural Language Processing, Human-Computer Interaction, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. Diyi Yang
2. Yang Trista Cao
3. Hal Daumé Iii
4. Dirk Hovy
5. Christopher Potts
6. Isar Nejadgholi
7. Svetlana Kiritchenko
8. Zeerak Talat

In Gender Studies

1. Hal Daumé Iii
2. Yang Trista Cao
3. Eshwar Chandrasekharan
4. David Jurgens
5. Zijian Wang

In Natural Language Processing

1. Diyi Yang
2. Hal Daumé Iii
3. Yang Trista Cao
4. Dirk Hovy
5. David Jurgens
6. Christopher Potts
7. Mohit Bansal
8. Libby Hemphill

In Human-Computer Interaction

1. Hal Daumé Iii
2. Yang Trista Cao
3. Dirk Hovy
4. Seth C. Lewis
5. Andrea L. Guzman

In Social Sciences & Humanities

1. Hal Daumé Iii
2. Yang Trista Cao
3. Christopher Potts
4. Dirk Hovy

Citations in the corpus

• Toward Gender-Inclusive Coreference Resolution (2019, background): “(2018a), and Webster et al. (2018) focused largely on the case of binary gender discrimination in trained coreference systems, showing that current systems over-rely on social stereotypes when resolving HE and SHE pronouns1 (see §2).”
• The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “Chen and Yang, 2021), argumentation techniques (Stab and Gurevych, 2014), rhetorical structures (Rapp, 2011), and the exchange of social support (Wang and Jurgens, 2018; Yang et al., 2019b).”
• A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (2019, background): “, 2016), or the level of support given between community members (Wang and Jurgens, 2018).”
• TalkDown: A Corpus for Condescension Detection in Context (2019, background): “Third, users can develop strong identities on the site, which could facilitate user-level modeling, but these identities are generally pseudonymous, which is useful when studying charged social phenomena (Hamilton et al., 2017; Wang and Jurgens, 2018).”