Extracting Associations of Intersectional Identities with Discourse about Institution from N igeria (2022)

Abstract: Word embedding models have been used in prior work to extract associations of intersectional identities within discourse concerning institutions of power, but restricted its focus on narratives of the nineteenth-century U.S. south. This paper leverages this prior work and introduces an initial study on the association of intersected identities with discourse concerning social institutions within social media from Nigeria. Specifically, we use word embedding models trained on tweets from Nigeria and extract associations of intersected social identities with institutions (e.g., domestic, culture, etc.) to provide insight into the alignment of identities with institutions. Our initial experiments indicate that identities at the intersection of gender and economic status groups have significant associations with discourse about the economic, political, and domestic institutions.

## introduction social scientists have leveraged quantitative methods to extract cultural knowledge from text, such as semantic networks @xcite , topic modeling @xcite , and language models @xcite . recent work by @xcite focused on using language models (specifically word embedding models) to extract intersectional identity associations inherent in narrative texts. intersectionality @xcite ) is a theoretical framework for understanding how social identities such as gender and race, compound to create experiences that would otherwise be obscured by focusing on the identities separately. specifically, @xcite applied word2vec @xcite to understand how intersected social identities associate with discourse about institutions of power (e.g. domestic, culture, etc.) from narratives of the nineteenth-century u.s. south. while this method was successfully able to extract intersectional associations from u.s. narratives, it remains an open question whether this method generalizes to other forms of text from outside the u.s., such as social media data from nigeria. social media data outside the u.s. presents an interesting challenge as social media may not be accessible or used by everyone outside the u.s. this means that these types of datasets can inherently contain an imbalance in population representation, making analyses with them need careful attention. this paper presents an initial study on using word embedding models to understand how intersected identities associate with discourse concerning institutions found within social media text from nigeria. our main contributions are (1) the application of prior work by @xcite to tweets from nigeria, and (2) an analysis of intersected gender and economic identities and their associations to the domestic, economic, political, and cultural spheres. we leverage skip gram with negative sampling (sgns) @xcite models and look at the relationship of intersected gender and economic identities within discourse concerning the political, cultural, domestic, and economic spheres within tweets. our results indicate that a female, poor category of individuals is more associated with discourse from lagos and federal capital territories (fct) concerning the domestic sphere while a male, poor category is associated with economic and political spheres. this paper is structured as follows. section 2 provides prior work on intersectionality and the extraction of cultural associations from language models. section 3 describes the twitter dataset used to train sgns models used in our analysis. section 4 describes the method used by @xcite , which is leveraged for our analysis in section 5. section 6 provides a discussion about our analysis and section 7 provides our conclusion. ## related work our analysis is situated at the crossroads of intersectionality and extraction of cultural associations from word embedding models. the concept of intersectionality can be traced to @xcite , who argued and showed that the experiences of inequality of black women were obscured by the experiences of inequality of women and black people. both quantitative and qualitative methods have been used to analyze intersectionality. @xcite argues that graphical causal models can be used to represent claims about the causal effects of occupying intersected social identities. a survey of quantitative research that uses the intersectionality framework is provided by @xcite . there has also been qualitative work by @xcite , who analyzed the intersection of lgbt+ and other social identities in the context of the healthcare in nigeria, discovering that sub-identities within lgbt+ suffer from bias more than their peer sub-identities, particularly when intersected with mental and sexual health conditions. language models have been shown to be effective at extracting cultural associations @xcite @xcite and bias @xcite @xcite @xcite ) from text; our work focuses on extracting cultural associations from text. @xcite studied how word embedding models could be used to understand trends in gender and ethnic stereotypes in the u.s. over time. @xcite studied how word embedding models could be used to construct cultural vectors, and applied this to understand social class in the u.s. the work closest to ours was done by @xcite , who studied how intersectional identities associated with u.s. narratives about institutions of power. our work differs from @xcite al. (2018), kozlowski et al. (2019), and nelson (2021) 3 social media dataset the present work leverages language models trained on an international social media dataset used in prior work @xcite for the darpa understanding group bias (ugb) project and approved for use by an independent irb. among other countries, the original ugbgathered dataset includes approximately 30 million tweets from various states in nigeria from 2018, gathered by a university teammate. this data is not used directly in this work, but the derived word embedding models are. to create the word embedding models for ugb, tweets were tokenized for whitespace and lower-cased. no stemming or lemmatization was performed, thereby preserving the original vocabulary for our analysis (preservation was necessary as the vocabulary affects the seed words used in the analysis). ## extracting intersectional associations from word embedding models the main goal of our analysis is to extract intersectional associations within discourse about social institutions found in tweets from nigeria. to this end, we leverage recent work by @xcite which used a word2vec model to understand how intersected social identities (black and white men and women) mapped within four social institutions (domestic, economic, polity, and culture) in a corpus of first-person narratives from the u.s. south. the method used by @xcite required constructing geometric vectors and spaces for the institutions and identities using trained word2vec models. this section describes their construction. ## analysis figure 1 provides the results of our analysis for the gender and economic status identity groups. foot_0 for each social institution, we compute a 95% confidence interval for the difference between the association scores (described in section 4.3) for pairs of intersected identities (e.g., the first row of figure 1 compares the difference between association scores for female, poor and female, rich for each social institution). we note that any confidence intervals that contain a difference of 0 (middle black dotted line in figure 1 ) is not statistically significant. similar to @xcite and @xcite , we use the percentile bootstrap method to construct the confidence intervals, where the number of samples used is 40 (interval spans the 2 nd and 39 th association score differences). we used 40 pretrained sgns models that were each trained on datasets generated by sampling the original twitter dataset of the same size with replacement (any words whose frequency is less than five were re-moved). we then compute differences between association scores using the process in section 4 for each sgns model. the pretrained models have an embedding size of 200, context window of five, and were trained using five negative samples. within a particular gender identity, the poor are significantly more associated with the discourse about the political, economic, and domestic sphere than the rich (@xmath0.05). this can be seen in the first and last rows of figure 1 . within a particular economic identity, females are significantly more associated with discourse about the domestic sphere than males. on the other hands, males are more significantly associated with discourse concerning the economic and political sphere than females. this can be seen in the second and fifth rows of figure 1 . according to our results, discourse concerning the domestic sphere has an intersectional association towards female, poor individuals. this can be seen by the fact that female, poor individuals are always significantly associated with domestic sphere discourse compared to the other intersected identities (see the first, second, and fourth rows of figure 1 ). similarly, discourse about the economic and political spheres has an intersectional association towards male, poor individuals (second, third, and sixth rows of figure 1 ). recall from section 3 that the majority of tweets are from lagos and fct. as such, a majority of the discourse in the dataset is biased towards those two states in nigeria. this means that the associations detected for the intersected identities are not representative of individuals in all of nigeria, but rather those that live in lagos and fct. ## discussion our analysis provides insight into what social institutions are of discursive interest to intersected social identities in nigeria with a bias towards individuals from lagos and fct. in particular, our results show who is more vocal about a particular institution, and which individuals are less vocal about a given institution, but it does not explicitly mention whose voice is the most marginalized. this analysis is a good starting point for detecting bias in discourse about an institution, but work is needed to extract the most marginalized voices. similar to @xcite , we find that machine learning can enhance qualitative research methods, allowing us to juxtapose quantitative outcomes with qualitative examples. for example, "i came across a poor women who had recently delivered five children. she needed money for food and medical bills. such a sad example of poverty in nigeria" is reflective of the lived experience of intersectionality while our results provide evidence for how intersected identities are linked to particular institutions at a larger scale. the results of our analysis also aligns with several recent qualitative works that look at discrimination and bias in nigeria. @xcite showed that females are heavily associated with the domestic sphere (i.e., having children and domestic skills). additionally, enfield (2019) mentioned that females are represented in the labor markets, but they are penalized through low wages and activity. enfield (2019) also described that females (especially poor females) join the labor market late due to the cultural pressure of early marriage and having children. this implies that males have more freedom in the labor market than females, aligning with our results that males are more associated with the economic spheres than females. ## conclusion this paper presents an initial study which uses sgns models trained on twitter data from nigeria to determine how intersectional identities are associated with discourse on social institutions. our results show that female, poor individuals are more associated with discourse from lagos and fct concerning the domestic sphere while male, poor individuals are associated with discourse about the economic and political spheres. there are several avenues for future work. first, the efficiency of the analysis could be improved, particularly to handle large corpora. second, the sensitivity of associations to model hyperparameters could be assessed to ensure the associations hold under different hyperparameter choices. finally, the analysis could be made sensitive to dataset statistics such as geographic distribution.