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This thesis is the first long-form analysis of formal, especially stylistic changes in Thomas Pynchon’s oeuvre by digital methods. By digitally examining stylistic aspects which scholars have described as “Pynchonian” or characteristic of Pynchon’s texts — including ambiguity/vagueness, acronyms, ellipsis marks, and profanity — I present evidence that some of these devices are not as characteristic of Pynchon’s texts as previously assumed, while considerable variation in frequency between texts challenges our assumptions of Pynchon’s style. Within a literature review of formal, stylistic, and digital Pynchon studies, I demonstrate how digital humanities may confirm, contest, and improve upon a wide variety of Pynchon scholarship. Through experiments on formal overviews of Pynchon’s oeuvre, results indicate that songs/poems decrease in later works, while direct discourse generally increases over time, and I present hypotheses to understand these trends. By closely examining stylistic features of the mock 18th-century pastiche of Mason & Dixon — archaic spelling, censored words, and irregular capitalization — I argue that these should be interpreted within continuities across Pynchon’s oeuvre. Pynchon has been dubbed “The Voice of Ambiguity” by Thomas Schaub, and in experiments to quantify ambiguity/vagueness in texts, Pynchon’s works do indeed score highest by certain measures, while Pynchon is increasing the use of his “preferred” vagueness words (lexis used statistically higher than comparison corpora). By querying acronyms and ellipsis marks, it emerges that certain novels by Pynchon reduce their use dramatically, while the historical backgrounds of these devices in English literature challenge and contextualize prior understanding of Pynchon’s use of these. Finally, distant and close reading of profanity in Pynchon’s texts reveal an early pattern of “coded” profanity via non-English words and character names. In the Conclusion, I draw these results together to present the most extensive description of Pynchon’s “late style” thus far.

Purpose The purpose of this paper is to explore the spillover effects of offensive commenting in online community from the lens of emotional and behavioral contagion. Specifically, it examines the contagion of swearing – a linguistic mannerism that conveys high-arousal emotion – based upon two mechanisms of contagion: mimicry and social interaction effect. Design/methodology/approach The study performs a series of mixed-effect logistic regressions to investigate the contagious potential of offensive comments collected from YouTube in response to Donald Trump’s 2016 presidential campaign videos posted between January and April 2016. Findings The study examines non-random incidences of two types of swearing online: public and interpersonal. Findings suggest that a first-level (a.k.a. parent) comment’s public swearing tends to trigger chains of interpersonal swearing in the second-level (a.k.a. child) comments. Meanwhile, among the child-comments, a sequentially preceding comment’s swearing is contagious to the following comment only across the same swearing type. Based on the findings, the study concludes that offensive comments are contagious and have impact on shaping the community-wide linguistic norms of online user interactions. Originality/value The study discusses the ways in which an individual’s display of offensiveness may influence and shape discursive cultures on the internet. This study delves into the mechanisms of text-based contagion by differentiating between mimicry effect and social interaction effect. While online emotional contagion research to this date has focused on the difference between positive and negative valence, internet research that specifically looks at the contagious potential of offensive expressions remains sparse.

Hate speech is one type of harmful online content which directly attacks or promotes hate towards a group or an individual member based on their actual or perceived aspects of identity, such as ethnicity, religion, and sexual orientation. With online hate speech on the rise, its automatic detection as a natural language processing task is gaining increasing interest. However, it is only recently that it has been shown that existing models generalise poorly to unseen data. This survey paper attempts to summarise how generalisable existing hate speech detection models are and the reasons why hate speech models struggle to generalise, sums up existing attempts at addressing the main obstacles, and then proposes directions of future research to improve generalisation in hate speech detection.

Social media is a more common and powerful platform for communication to share views about any topic or article, which consequently leads to unstructured toxic, and hateful conversations. Curbing hate speeches has emerged as a critical challenge globally. In this regard, Social media platforms are using modern statistical tools of AI technologies to process and eliminate toxic data to minimize hate crimes globally. Demanding the dire need, machine and deep learning-based techniques are getting more attention in analyzing these kinds of data. This survey presents a comprehensive analysis of hate speech definitions along with the motivation for detection and standard textual analysis methods that play a crucial role in identifying hate speech. State-of-the-art hate speech identification methods are also discussed, highlighting handcrafted feature-based and deep learning-based algorithms by considering multimodal and multilingual inputs and stating the pros and cons of each. Survey also presents popular benchmark datasets of hate speech/offensive language detection specifying their challenges, the methods for achieving top classification scores, and dataset characteristics such as the number of samples, modalities, language(s), number of classes, etc. Additionally, performance metrics are described, and classification scores of popular hate speech methods are mentioned. The conclusion and future research directions are presented at the end of the survey. Compared with earlier surveys, this paper gives a better presentation of multimodal and multilingual hate speech detection through well-organized comparisons, challenges, and the latest evaluation techniques, along with their best performances.

The combination of profanity and blasphemy can be said to be one of the most delicate taboo categories to deal with on the screen. It is in the context of audiovisual translation (AVT) where professionals have to make challenging decisions when transferring these elements. Thus, should audiovisual translators be faithful to the source text or is it legitimate that they tone down the load of profanity and blasphemy? This paper describes the subtitling into European Spanish of a corpus composed of some of Tarantino’s films on the grounds of profane and blasphemous phrases which could provoke a strong reaction from the audience. Among the main goals of this paper are: scrutinising (1) if the religious phrases under analysis are transferred faithfully; and (2) whether or not cases of blasphemy in the target text have been encountered. In a nutshell, this study aims to explore the treatment of profanity and blasphemy in the subtitles produced for the Spanish audience.

We address a challenging problem of identifying main sources of hate speech on Twitter. On one hand, we carefully annotate a large set of tweets for hate speech, and deploy advanced deep learning to produce high quality hate speech classification models. On the other hand, we create retweet networks, detect communities and monitor their evolution through time. This combined approach is applied to three years of Slovenian Twitter data. We report a number of interesting results. Hate speech is dominated by offensive tweets, related to political and ideological issues. The share of unacceptable tweets is moderately increasing with time, from the initial 20% to 30% by the end of 2020. Unacceptable tweets are retweeted significantly more often than acceptable tweets. About 60% of unacceptable tweets are produced by a single right-wing community of only moderate size. Institutional Twitter accounts and media accounts post significantly less unacceptable tweets than individual accounts. In fact, the main sources of unacceptable tweets are anonymous accounts, and accounts that were suspended or closed during the years 2018–2020.