22 research outputs found

    Stylistic variation on the Donald Trump Twitter account:a linguistic analysis of tweets posted between 2009 and 2018

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    Twitter was an integral part of Donald Trump's communication platform during his 2016 campaign. Although its topical content has been examined by researchers and the media, we know relatively little about the style of the language used on the account or how this style changed over time. In this study, we present the first detailed description of stylistic variation on the Trump Twitter account based on a multivariate analysis of grammatical co-occurrence patterns in tweets posted between 2009 and 2018. We identify four general patterns of stylistic variation, which we interpret as representing the degree of conversational, campaigning, engaged, and advisory discourse. We then track how the use of these four styles changed over time, focusing on the period around the campaign, showing that the style of tweets shifts systematically depending on the communicative goals of Trump and his team. Based on these results, we propose a series of hypotheses about how the Trump campaign used social media during the 2016 elections

    Linguistic variation across Twitter and Twitter trolling

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    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

    Dimensions of Abusive Language on Twitter

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    In this paper, we use a new categorical form of multidimensional register analysis to identify the main dimensions of functional linguistic variation in a corpus of abusive language, consisting of racist and sexist Tweets. By analysing the use of a wide variety of parts-of-speech and grammatical constructions, as well as various features related to Twitter and computer-mediated communication, we discover three dimensions of linguistic variation in this corpus, which we interpret as being related to the degree of interactive, antagonistic and attitudinal language exhibited by individual Tweets. We then demonstrate that there is a significant functional difference between racist and sexist Tweets, with sexists Tweets tending to be more interactive and attitudinal than racist Tweets

    Attributing the Bixby Letter using n-gram tracing

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    There is a long-standing debate around the authorship of the Bixby Letter, one of the most famous pieces of correspondence in American history. Despite being signed by President Abraham Lincoln, some historians have claimed that its true author was John Hay, Lincoln’s personal secretary. Analyses of the letter have been inconclusive in part because the text totals only 139 words and is thus far too short to be attributed using standard methods. To test whether Lincoln or Hay wrote this letter, we therefore introduce and apply a new technique for attributing short texts called n-gram tracing. After demonstrating that our method can distinguish between the known writings of Lincoln and Hay with a very high degree of accuracy, we use it to attribute the Bixby Letter, concluding that the text was authored by John Hay – rewriting this one episode in the history of the United States and offering a solution to one of the most persistent problems in authorship attribution

    A Multi-Dimensional Analysis of English Tweets

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    This paper applies Multi-Dimensional Analysis (MDA) to a corpus of English tweets to uncover the most common patterns of linguistic variation. MDA is a commonly applied method in corpus linguistics for the analysis of functional and/or stylistic variation in a particular language variety. Notably, MDA is an approach aimed at identifying and interpreting the frequent patterns of co-occurring linguistic features across a corpus, such as a corpus of spoken and written English registers (Biber, 1988). Traditionally, MDA is based on a factor analysis of the relative frequencies of numerous grammatical features measured across numerous texts drawn from that variety of language to identify a series of underlying dimensions of linguistic variation. Despite its popularity and utility, traditional MDA has an important limitation – it can only be used to analyse texts that are long enough to allow for the relative frequencies of many grammatical forms to be estimated accurately. If the texts under analysis are too short, then few forms can be expected to occur sufficiently frequently for their relative frequency to be accurately estimated. Tweets are characteristically short texts, meaning that traditional MDA cannot be used in the present research. To overcome this problem, this paper introduces a short-text version of MDA and applies it to a corpus of English tweets. Specifically, rather than measure the relative frequencies of forms in each tweet, the approach analyses their occurrence. This binary dataset is then aggregated using Multiple Correspondence Analysis (MCA), which is used much like factor analysis in traditional MDA – to return a series of dimensions that represent the most common patterns of linguistic variation in the dataset. After controlling for text length in the first dimension, four subsequent dimensions are interpreted. The results suggest that there is a great deal of linguistic variation on Twitter. Notably, the results show that Twitter is commonly used for self-commodification, as people manage their identities, engaging in practices of self-branding through stance-taking, self-reporting, promotion and persuasion, as well as broadcasting their message beyond their followership, distributing news, and expressing opposition and this often occurs in order to attract attention. Additionally, the results show that interaction is common, suggesting that Twitter is also used for social and interpersonal gain

    Eades, Diana

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    Register and social media

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