The Non-native Speaker Aspect: I ndian E nglish in Social Media (2020)

Abstract: As the largest institutionalized second language variety of English, Indian English has received a sustained focus from linguists for decades. However, to the best of our knowledge, no prior study has contrasted web-expressions of Indian English in noisy social media with English generated by a social media user base that are predominantly native speakers. In this paper, we address this gap in the literature through conducting a comprehensive analysis considering multiple structural and semantic aspects. In addition, we propose a novel application of language models to perform automatic linguistic quality assessment.

## introduction analyzing important issues through the lens of social media is a thriving field in computational social science (css) research. from policy debates @xcite to modern conflicts @xcite , web-scale analyses of social media content present an opportunity to aggregate and analyze opinions at a massive scale. english being one of the widely-spoken pluricentric languages @xcite , a considerable fraction of current css research primarily analyzes content authored in english. several recent lines of css research on indian sub-continental issues @xcite @xcite dealt with indian english (mehrotra, 1998), a regional variant of english spoken in india and among the indian diaspora. as the largest institutionalized second language variety of english, indian english has received sustained attention from linguists @xcite @xcite ; sedlatschek, * ashiqur r. khudabukhsh is the corresponding author. in this paper, via two substantial contemporaneous corpora constructed from comments on youtube videos from major news networks in the us and india, we address the above research question. to the best of our knowledge, no prior study has contrasted any indian english social media corpus with the variety of english observed in social media platforms frequented by native english speakers. we further use two existing corpora of news articles from india and the us to demonstrate that while college-educated, well-formed english across these two language centers does not differ by much, social media indian english is different from social media us english on certain aspects, hence may pose a greater challenge to conduct meaningful analysis. apart from using standard tools to assess linguistic quality, we present a novel finding that recent advances in language models can be leveraged to perform automated linguistic quality assessment of human-generated text. ## data sets we consider two social media (denoted by the superscript sm) data sets and two news article (denoted by the superscript na) data sets. we denote indian english and us english as en-in and en-us, respectively. in order to keep our vocabulary statistics comparable, we sub-sample from our en-us social media data set and ensure that both social media corpora have nearly equal number of tokens. detailed description of preprocessing steps are presented in the appendix. why youtube? both of our social media corpora are comments on youtube videos posted within an identical date range (30 th january, 2020 to 7 th may, 2020). as of january 2020, youtube is the secondmost popular social media platform in the world drawing 2 billion active users @xcite . it is the most popular social media platform in india with 265 million monthly active users, accounting for 80% of the population with internet access (hin-dustantimes, 2019; yourstory, 2018). ## conclusions in this paper, we present a comprehensive comparative analysis between indian english and us english in social media. our analyses reveal that compared to native english, social media indian english exhibits certain differences that may add to the challenges of navigating noisy, social media texts generated in the indian sub-continent and thus present an opportunity to the nlp community to address these challenges. recent lines of computational social science (css) research focusing on indian sub-continental issues emphasized on the challenges faced while processing indian social media data. however, no prior work contrasted social media indian english with social media native english. we believe our work will help the research community identify focus areas to facilitate css research in this domain. we present a novel approach to perform automated linguistic quality assessment using bert, a well-known high-performance language model. to the best of our knowledge, our work first tests bert's masked word prediction accuracy on human-generated texts obtained from noisy social media. world variants of english spoken and written form have been widely studied for several decades. however, limited literature exists on characterizing their social media expressions. comment svd the goons needs to be severely punished. svd they play victim card, like they too suffering from virus. svd every dogs come thier own day, you get what u deserves. ivf i am live in assam. ivf these people will be never change. our study makes a small step towards characterizing a broad range of aspects of indian english observed in social media. observation: loanwords borrowed from hindi heavily feature in indian english. analysis: observation: indian english uses considerably fewer articles and pronouns as compared to us english. analysis: we next compute the respective unigram distributions p en-in and p en-us . for each token y another pm help fund what is the need of that coz there is already a pm relief fund and its has a committee with opposition party memeber too . thanks to god that we have priminster like a modi ji he is our great mister i salute my priminister may god bless him always he has be long life i request news reporter to use mask, plz do this, bcoz you are facing more dangerous situation only for public sake, plz sir i request to inform our reporter,they help ussssss if sibal and singhvi becomes enjoy similar positions den its ok for dem kapilbsibal is ant national a gunda good for u sir dey r sour grapes and crook of sonia gandhi english exhibits lesser average constituency parser tree depth @xcite indicating (possible) structural issues. analysis: intuitively, length of a sentence is likely to be positively correlated with its structural complexity; a long sentence is likely to have more complex (and nested) sub-structures than a shorter one. a parser's ability to correctly identify such substructures depends on the sentence's syntactic correctness. to tease apart the relationship between sentence-length and constituency parser's depth, in figure 4 , we present the average tree depth for a given sentence length. we observe that between well-formed english, the difference is almost imperceptible. however, as the sentence length grows, the gap between tree depth obtained in social media en-in and the rest widens indicating possible structural issues. a few example long sentences with small parse-tree depth are presented in analysis: we construct an additional contemporaneous corpus of 4,034,513 comments from 57,019 videos from two highly popular british news outlets (bbc and channel 4). in