Lifetime lexical variation in social media

As the rapid growth of online social media attracts a large number of Internet users, the large volume of content generated by these users also provides us with an opportunity to study the lexical variation of people of different ages. In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users’ ages. Our model inherently assumes that a topic has not only a word distribution but also an age distribution. We propose a Gibbs-EM algorithm to perform inference on our model. Empirical evaluation shows that our model can learn meaningful age-specific topics such as “school” for teenagers and “health” for older people. Our model can also be used for age prediction and performs better than a number of baseline methods

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

Linguistic variation across Twitter and Twitter trolling

Trolling is used to label a variety of behaviours, from the spread of misinformation and hyperbole to targeted abuse and malicious attacks. Despite this, little is known about how trolling varies linguistically and what its major linguistic repertoires and communicative functions are in comparison to general social media posts. Consequently, this dissertation collects two corpora of tweets – a general English Twitter corpus and a Twitter trolling corpus using other Twitter users’ accusations – and introduces and applies a new short-text version of Multi-Dimensional Analysis to each corpus, which is designed to identify aggregated dimensions of linguistic variation across them. The analysis finds that trolling tweets and general tweets only differ on the final dimension of linguistic variation, but share the following linguistic repertoires: “Informational versus Interactive”, “Personal versus Other Description”, and “Promotional versus Oppositional”. Moreover, the analysis compares trolling tweets to general Twitter’s dimensions and finds that trolling tweets and general tweets are remarkably more similar than they are different in their distribution along all dimensions. These findings counter various theories on trolling and problematise the notion that trolling can be detected automatically using grammatical variation. Overall, this dissertation provides empirical evidence on how trolling and general tweets vary linguistically