Mapping Semantic Domains Across I ndia’s Social Media: Networks, Geography, and Social Factors (2025)

Abstract: This study examines socially-conditioned variation within semantic domains like kinship and weather using thirteen Indian cities as a case-study. Using bilingual social media data, we infer six semantic domains from corpora representing individual cities with a lexicon including terms from English, Hindi and Transliterated Hindi. The process of inferring semantic domains uses character-based embeddings to retrieve nearest neighbors and Jaccard similarity to operationalize the edge weights between lexical items within each domain. These representations reveal distinct regional variation across all six domains. We then examine the relationship between variation in semantic domains and external social factors such as literacy rates and local demographics. The results show that semantic domains exhibit systematic influences from sociolinguistic factors, a finding that has significant implications for the idea that semantic domains can be studied as abstractions distinct from specific speech communities.

## introduction india is a country with many diverse cultures and languages. this creates interactions between languages, particularly hindi and english in the northern regions of india and between hindi and other languages elsewhere. this paper asks whether such longstanding linguistic and cultural contact changes the character of semantic domains present within thirteen indian cities. much previous work views semantic domains as language-specific, so that a language like hindi has a single semantic map for a domain like kinship. the contribution of this paper is to show that social factors like language contact have a systematic influence on the structure of semantic domains. india provides an ideal case-study because of these longstanding contact situations. we focus on six semantic domains: weather, kinship, emotion, animals, professions and temporal units. these domains were chosen because of their known variation in lexical granularity between english and hindi. for example, hindi distinguishes between paternal and maternal grandfathers lexically, whereas english uses the same term for both relationships. additional modifiers (paternal, maternal) are used in english when necessary. in contrast, hindi uses the same term for yesterday and tomorrow, disambiguating based on verb tense and context; english uses distinct words for these two concepts. these examples show how languages can encode conceptual distinctions with differing levels of granularity. a speaker's lexical choices are shaped by grammatical and cultural systems enforced in the lexicon. our question here is the degree to which linguistic and cultural contact create variation within semantic domains within the same languages. to investigate this question, we analyze data from indian social media. this kind of spontaneous, everyday language use provides insight into how different populations lexicalize these six semantic domains. social media offers a large-scale, naturalistic corpus to capture regional variation. in particular, it allows us to ask whether the same semantic concepts are realized with consistent lexical patterns across cities or whether these patterns diverge due to differences in language contact and social environment. we develop a corpus of over 50 million samples containing a mix of english and hindi usage across thirteen indian cities as a means of observing semantic domains across regional populations. given population-specific corpora, we need to infer a representation of these semantic domains in order to compare them across populations. we take a data-driven approach based on non-contextual character embeddings from fasttext, learning a separate model from each city-specific corpus. these embeddings can be seen as approximations of con-ceptual structure in which lexical items from the same domain form a neighborhood within the embedding space. this approach to operationalizing a semantic domain as similarities within an embedding space aligns with an opposition theory approach to signs (de saussure, @xcite . this approach posits that the value of a concept is determined by its contrasting relations within the system of language, particularly how it contrasts with other similar terms. therefore, embeddings offer a way to operationalize the structure of these semantic domains which can then be used to measure the degree to which these domains vary across speech communities. importantly, many concepts in these six domains exhibit co-lexification: there is not a one-to-one mapping between form and meaning. for example, the cases of paternal/maternal grandfather (in english) and of yesterday/tomorrow (in hindi) are instances in which one language co-lexifies what the other splits into two separate items. in our bilingual corpus data, however, a speaker is not limited to the co-lexification patterns of either language. we hypothesize that this provides additional flexibility to the mapping between form and meaning within lexical items, allowing them to vary systematically across populations due to sociolinguistic conditions. if this is the case, we would expect that the operationalizations of these semantic domains, created using an embedding space, will also differ across regions in predictable ways. this paper makes three main contributions: first, we show that these semantic domains, as inferred from corpora, vary significantly across indian cities in way that corresponds with different levels of overall language contact. second, we show that these variations are relatively stable across all six domains and are not artifacts within only a single domain. and, third, we show that these variations are not simply random but are significantly related to social and demographic factors. taken together, these findings suggest that semantic domains are not a single entity shared by all speakers of a language but rather systems which are influenced by social factors like differing degrees of language contact. after reviewing related work in semantic domains and social factors in section 2, we present a dataset derived from twitter/x posts from various indian cities in section 3. this dataset contains samples in english, hindi and transliterated hindi. our method for operationalizing semantic domains using an embedding space is detailed in section 4, along with the social factors used for later analysis. the analysis of variation in semantic domains across cities is presented in section 5, with a special focus on the relationship between these variations and external social factors like language contact. we end, in section 6, by discussing the larger implications of this work on the interface between sociolinguistics and computational semantics. while previous computational work has abstracted away from sociolinguistic factors in the representation of semantic domains, the findings in this paper show that such idealized representations will not capture variations within the speech community. ## data this study uses a large scale social media corpus containing 49,801,176 english tweets and 5,545,724 hindi tweets, all originating from india. it is important to note that the english corpus contains a huge amount of transliterated hindi data which is often used by people in the region especially for online communication. to analyze the data, we combined the two corpora into a single embedding space to capture semantic representations across languages. the resulting corpus contains approximately 55 million documents. ## methodology the basic approach in this paper is to infer a representation of six semantic domains from populationspecific corpora representing different cities in india. once we have inferred these semantic domains, we compare them to one another in order to quantify variation and then use regression models to understand the relationship between these variations and external factors like language contact. ## analysis figure 2 shows us the average correlation matrix for the mean square differences in the mappings between cities for our six domains. here i is the taken as the national average. a correlation close to 1 shows high positive correlation shown by dark red. this signifies that the two cities had similar mean square differences for that domain suggesting a similar structure. a correlation close to -1 shows high negative correlation between the cities shown by dark blue. this shows that the cities have very different structures as their mean square differences compared to other cities in our matrix are not very similar and are quite contrasting. a value close to 0 suggests that there is no correlation between the cities. we want to observe whether there are any significant differences in the structure of semantic domains as operationalized. this would be seen if our correlation matrix has a range of values from -1 to 1 as this would suggest that each city has some difference in structure. however, if we see the same very extreme values that would suggest that all cities are correlated meaning that all structures look the same (uniformity in semantic structures). on the other hand, if we see no correlation (values just ranging near 0), that would suggest that there is no commonality in any of the structures and all cities have a very different way to portray the lexicon in the embedding space. we decided to average out and create one matrix for our analysis. this is because the matrices for each of the domains had similar values. these matrices can be seen in appendix. ## conclusion this study investigated how the mapping between concepts and lexical items within specific semantic domains varies across geographical regions within india. our analysis revealed that semantic mappings do vary across regions, offering insight into language contact and multilingual variation in online communication. it showed consistent clusters of cities which had similar semantic structures. this suggests that geographic proximity influences variation in these representations. these clusters show that semantic similarity is influenced by spatial and social features. notably, chennai, despite not being hindi-dominant, showed the highest similarity to the national average, while pune showed the least, indicating that the national semantic norm reflects more than just northern, hindi-speaking patterns. the linear regression model showed that social features correlate closely with semantic conformity of a domain structure to the national average. 1. literacy rates and number of hindi speakers both showed gendered divergence. high male rates correlated with a strong positive effect on semantic conformity, while female rates shows a strong negative effect. these opposing effects suggest distinct linguistic networks across gendered populations. furthermore, it suggests that male population conforms to national average whereas female population might be driving diverse language change.