Text Analysis in Adversarial Settings

Abstract

Textual deception constitutes a major problem for online security. Many studies have argued that deceptiveness leaves traces in writing style, which could be detected using text classification techniques. By conducting an extensive literature review of existing empirical work, we demonstrate that while certain linguistic features have been indicative of deception in certain corpora, they fail to generalize across divergent semantic domains. We suggest that deceptiveness as such leaves no content-invariant stylistic trace , and textual similarity measures provide a superior means of classifying texts as potentially deceptive. Additionally, we discuss forms of deception beyond semantic content, focusing on hiding author identity by writing style obfuscation . Surveying the literature on both author identification and obfuscation techniques, we conclude that current style transformation methods fail to achieve reliable obfuscation while simultaneously ensuring semantic faithfulness to the original text. We propose that future work in style transformation should pay particular attention to disallowing semantically drastic changes.

The computational and the representational language-of-thought hypotheses

“…Finally, as was noted long ago, neural-network architectures might be able to implement an LoT architecture (Fodor & Pylyshyn, 1988). Indeed, some recent work on DNNs explores implementations of variable binding (Webb, Sinha, & Cohen, 2021; though see Gröndahl & Asokan, 2022;Miller, Naderi, Mullinax, & Phillips, 2022), a classic example of LoT-like symbolic computation Green & Quilty-Dunn, 2021;Marcus, 2001;. Our six core LoT properties help specify a cluster of features that such an implementation should aim for.…”

The Importance of Text Mining for Services Management

“…It also provides the organisation with the clear picture of customers that react to several contents and brands. It is considered as the main act that takes vast volumes regarding raw data texts and analyse in producing structured results [13]. In addition, it also delegates the mining tools that are AI-powered which benefits the automation process of mining.…”

Automatic Deception Detection using Multiple Speech and Language Communicative Descriptors in Dialogs

“…turn-level and word-level embeddings. Inspired by [18], we compute a turn-level representation by utilizing BERT, which is a neural language model proposed by [31] to be used as language representation. The BERT model leverages a large amount of plain text data publicly available on the web and is trained in unsupervised objective functions.…”

“…Other literature also utilized behaviors of language use (e.g. features derived from Linguistic Inquiry and Word Count (LIWC) [10,17] or pre-trained BERT model [18]) to train deception classifiers to be used in a conversation setting. Lastly, several types of research have investigated fusion methods of multimodal behavior data, including acoustic features, LIWC-embeddings, and facial expressions, for automatic deception detection [19].…”