social_bias_frames

Warning: this document and dataset contain content that may be offensive or upsetting.

Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language. For example, these frames are meant to distill the implication that "women (candidates) are less qualified" behind the statement "we shouldn't lower our standards to hire more women." The Social Bias Inference Corpus (SBIC) supports large-scale learning and evaluation of social implications with over 150k structured annotations of social media posts, spanning over 34k implications about a thousand demographic groups.

Supported Tasks and Leaderboards
This dataset supports both classification and generation. Sap et al. developed several models using the SBIC. They report an F1 score of 78.8 in predicting whether the posts in the test set were offensive, an F1 score of 78.6 in predicting whether the posts were intending to be offensive, an F1 score of 80.7 in predicting whether the posts were lewd, and an F1 score of 69.9 in predicting whether the posts were targeting a specific group.

Another of Sap et al.'s models performed better in the generation task. They report a BLUE score of 77.9, a Rouge-L score of 68.7, and a WMD score of 0.74 in generating a description of the targeted group given a post as well as a BLUE score of 52.6, a Rouge-L score of 44.9, and a WMD score of 2.79 in generating a description of the implied offensive statement given a post. See the paper for further details.

Languages
The language in SBIC is predominantly white-aligned English (78%, using a lexical dialect detector, Blodgett et al., 2016). The curators find less than 10 percentage of posts in SBIC are detected to have the AAE dialect category. The BCP-47 language tag is, presumably, en-US.

The main aim for this dataset is to cover a wide variety of social biases that are implied in text, both subtle and overt, and make the biases representative of real world discrimination that people experience RWJF 2017. The curators also included some innocuous statements, to balance out biases, offensive, or harmful content.

Source Data
The curators included online posts from the following sources sometime between 2014-2019:

r/darkJokes, r/meanJokes, r/offensiveJokes
Reddit microaggressions (Breitfeller et al., 2019)
Toxic language detection Twitter corpora (Waseem & Hovy, 2016; Davidson et al., 2017; Founa et al., 2018)
Data scraped from hate sites (Gab, Stormfront, r/incels, r/mensrights)

columns:
whoTarget: a string, '0.0' if the target is a group, '1.0' if the target is an individual, and blank if the post is not offensive
intentYN: a string indicating if the intent behind the statement was to offend. This is a categorical variable with four possible answers, '1.0' if yes, '0.66' if probably, '0.33' if probably not, and '0.0' if no.
sexYN: a string indicating whether the post contains a sexual or lewd reference. This is a categorical variable with three possible answers, '1.0' if yes, '0.5' if maybe, '0.0' if no.
sexReason: a string containing a free text explanation of what is sexual if indicated so, blank otherwise
offensiveYN (target): a string indicating if the post could be offensive to anyone. This is a categorical variable with three possible answers, '1.0' if yes, '0.5' if maybe, '0.0' if no.
annotatorGender: a string indicating the gender of the MTurk worker
annotatorMinority: a string indicating whether the MTurk worker identifies as a minority
sexPhrase: a string indicating which part of the post references something sexual, blank otherwise
speakerMinorityYN: a string indicating whether the speaker was part of the same minority group that's being targeted. This is a categorical variable with three possible answers, '1.0' if yes, '0.5' if maybe, '0.0' if no.
WorkerId: a string hashed version of the MTurk workerId
HITId: a string id that uniquely identifies each post
annotatorPolitics: a string indicating the political leaning of the MTurk worker
annotatorRace: a string indicating the race of the MTurk worker
annotatorAge: a string indicating the age of the MTurk worker
post: a string containing the text of the post that was annotated
targetMinority: a string indicating the demographic group targeted
targetCategory: a string indicating the high-level category of the demographic group(s) targeted
targetStereotype: a string containing the implied statement
dataSource: a string indicating the source of the post (t/...: means Twitter, r/...: means a subreddit)

paper_url = "https://aclanthology.org/2020.acl-main.486.pdf"

original_data_url = "https://huggingface.co/datasets/allenai/social_bias_frames"