Authorship Analysis of Online Predatory Conversations using Character Level Convolution Neural Networks

Misra, Kanishka; Devarapalli, Hemanth; Ringenberg, Tatiana; Rayz, Julia Taylor

doi:10.1109/smc.2019.8914323

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Closed-set authorship attribution, in particular, is usually modelled as a supervised learning task, making use of text corpora labelled with author identifiers representing the classes (or authors) to be identified. Popular methods include the use of support vector machine classifiers (Schwartz et al 2013;Stamatatos 2017), recurrent neural networks (Bagnall 2016;Jafariakinabad and Hua 2019), convolution neural networks (Sari and Stevenson 2016;Shrestha et al 2017;Misra et al 2019) and stacks of ensemble classifiers (Custódio and Paraboni 2019), as we shall discuss later.…”

Section: Introductionmentioning

confidence: 99%

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Authorship attribution using author profiling classifiers

Deutsch

Paraboni

2022

Nat. Lang. Eng.

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Authorship attribution – the computational task of identifying the author of a given text document within a set of possible candidates – has been attracting interest in Natural Language Processing research for many years. At the same time, significant advances have also been observed in the related field of author profiling, that is, the computational task of learning author demographics from text such as gender, age and others. The close relation between the two topics – both of which focused on gaining knowledge about the individual who wrote a piece of text – suggests that research in these fields may benefit from each other. To illustrate this, this work addresses the issue of author identification with the aid of author profiling methods, adding demographics predictions to an authorship attribution architecture that may be particularly suitable to extensions of this kind, namely, a stack of classifiers devoted to different aspects of the input text (words, characters and text distortion patterns.) The enriched model is evaluated across a range of text domains, languages and author profiling estimators, and its results are shown to compare favourably to those obtained by a standard authorship attribution method that does not have access to author demographics predictions.

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Section: Introductionmentioning

confidence: 99%

“…2017; Misra et al . 2019) and stacks of ensemble classifiers (Custódio and Paraboni 2019), as we shall discuss later.…”

Section: Introductionmentioning

confidence: 99%

Authorship attribution using author profiling classifiers

Deutsch

Paraboni

2022

Nat. Lang. Eng.

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A Domain Knowledge Transformer Model for Occupation Profiling

Ai,

Yijia,

Mingyu

2023

Int J Comput Intell Syst

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Occupation profiling is a subtask of authorship profiling that is broadly defined as an analysis of individuals’ writing styles. Although the problem has been widely explored, no previous studies have attempted to identify Chinese classical poetry. Inspired by Trudgill’s seminal work on stylistic variation as a function of occupation, we present a novel Domain-Knowledge Transformer model to predict a poet’s occupation through their poems’ writing styles. Different from other Indo-European languages, Chinese has rarely used characters and two types of writing forms: traditional Chinese and simplified Chinese. To tackle these problems, we use the language-related component to standardize our input. We also use alphabetization to satisfy the restrictions on rhyming rules and tonal styles. As a special literal form, traditional domain knowledge, for example, named entities, themes, ages and the official career path, is valuable for poet occupation profiling. However, due to the lack of appropriate annotation datasets, it is difficult to recognize these features. Therefore, we proposed the domain knowledge component employing the latent Dirichletal location model to capture the extra theme information and establish named entity dictionaries to recognize the named entity of the datasets in this study. Finally, in the deep learning component, we combine Transformer with a convolutional neural network (CNN) model to perform occupation profiling. The experimental results suggest that our model is effective in this task. Moreover, the results demonstrate an account of other social attribution features of poetry style that are predictive of occupation in this domain.

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A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection

Choudhury

Kim

Alsoubai

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms. Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.

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Authorship Analysis of Online Predatory Conversations using Character Level Convolution Neural Networks

Cited by 5 publications

References 13 publications

Authorship attribution using author profiling classifiers

Authorship attribution using author profiling classifiers

A Domain Knowledge Transformer Model for Occupation Profiling

A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection

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