‘twazn me!!! ;(’ Automatic Authorship Analysis of Micro-Blogging Messages

Sousa‐Silva, Rui; Laboreiro, Gustavo; Sarmento, Luís; Grant, Tim; Oliveira, Eugénio; Maia, Belinda

doi:10.1007/978-3-642-22327-3_16

Cited by 52 publications

(13 citation statements)

References 9 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notwithstanding, in recent years forensic linguists have acknowledged the potential of computational analyses in forensic settings (see e.g. Sousa-Silva et al 2011;Grieve et al 2018), so the provision of forensic linguistics expertise is increasingly seen as indissociable from a lower or higher degree of computational analysis.…”

Section: Computational Forensic Linguisticsmentioning

confidence: 99%

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Sayers¹,

Sousa‐Silva²,

Höhn³

et al. 2021

View full text Add to dashboard Cite

New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.

show abstract

Section: Computational Forensic Linguisticsmentioning

confidence: 99%

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Sayers¹,

Sousa‐Silva²,

Höhn³

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Textual genre and length are key issues in AA. Most work on social media [2,3,33] have to tackle with the limited size of the texts.…”

Section: Related Workmentioning

confidence: 99%

“…[9] worked on gender classification (Author Profiling task) based on Twitter data in Portuguese. Silva et al [33] focused on idiosyncratic usage on a corpus of social media data using SVM.…”

Section: Related Workmentioning

confidence: 99%

“…This algorithm with default parameters is considered among the best for the AA task in both the English and the Portuguese languages [23,33,36,37].…”

Section: Using Other Machine-learning Algorithmsmentioning

confidence: 99%

“…This paper focuses on the AA task in the Portuguese language. Work on AA for the Portuguese language is still scarce [13,33,36,37], and several strategies have been put in practice with varying results. From the morphological point of view, Portuguese is a moderately rich language: besides a small set of productive prefixes and suffixes, most of these affixes can only be analysed by resourcing to the language's history; there is a relatively complex verbal inflection system, yielding around 70 inflected forms, several of them homographs; nominal and adjectival is limited to gender and number, within limited set of morphemes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Authorship Attribution in Portuguese Using Character N-grams

2017

APH

View full text Add to dashboard Cite

For the Authorship Attribution (AA) task, character n-grams are considered among the best predictive features. In the English language, it has also been shown that some types of character n-grams perform better than others. This paper tackles the AA task in Portuguese by examining the performance of different types of character n-grams, and various combinations of them. The paper also experiments with different feature representations and machine-learning algorithms. Moreover, the paper demonstrates that the performance of the character n-gram approach can be improved by fine-tuning the feature set and by appropriately selecting the length and type of character n-grams. This relatively simple and language-independent approach to the AA task outperforms both a bag-of-words baseline and other approaches, using the same corpus.

show abstract

Corpus Analysis in Forensic Linguistics

Nini¹

2021

The Encyclopedia of Applied Linguistics

View full text Add to dashboard Cite

This entry is an overview of the applications of corpus linguistics to forensic linguistics, in particular to the analysis of language as evidence. Three main areas are described, following the influence that corpus linguistics has had on them in recent times: the analysis of texts of disputed authorship, the provision of evidence in cases of trademark disputes, and the analysis of disputed meanings in criminal or civil cases. In all of these areas considerable advances have been made that revolve around the use of corpus data, for example, to study forensically realistic corpora for authorship analysis, or to provide naturally occurring evidence in cases of trademark disputes or determination of meaning. Using examples from real‐life cases, the entry explains how corpus analysis is therefore gradually establishing itself as the norm for solving certain forensic problems and how it is becoming the main methodological approach for forensic linguistics.

show abstract

‘twazn me!!! ;(’ Automatic Authorship Analysis of Micro-Blogging Messages

Cited by 52 publications

References 9 publications

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Authorship Attribution in Portuguese Using Character N-grams

Corpus Analysis in Forensic Linguistics

Contact Info

Product

Resources

About