A Needle in a Haystack? Harnessing Onomatopoeia and User-specific Stylometrics for Authorship Attribution of Micro-messages

Theóphilo, Antônio; Pereira, Luiz Haroldo; Rocha, Anderson

doi:10.1109/icassp.2019.8683747

Cited by 23 publications

(9 citation statements)

References 12 publications

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“…Additionally, temporal patterns can be extracted from a user's posting behavior to build a real-valued representation of a user profile. Follow-up studies aimed at closed-set user recognition on OSNs, with focus on user tweets and linguistic and stylistic signals [98]. Recently, many neural networks have been trained to efficiently learn representations of graph data to be further used for tasks such as node classification, link prediction, and user identity linkage [99].…”

Section: Sensor-based Biometric De-identificationmentioning

confidence: 99%

Biometric Systems De-Identification: Current Advancements and Future Directions

Shopon

Tumpa

Bhatia

et al. 2021

JCP

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Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.

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Section: Sensor-based Biometric De-identificationmentioning

confidence: 99%

Biometric Systems De-Identification: Current Advancements and Future Directions

Shopon

Tumpa

Bhatia

et al. 2021

JCP

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“…Em Rocha et al (2017), os autores apresentam uma revisão do problema e do uso de técnicas de IA nesse contexto. Atualmente, o estado da arte para o problema consiste no uso de modernos modelos de IA discriminativos como CNN Pereira;Rocha, 2019;Ruder;Ghaffari;Breslin, 2016;Shrestha et al, 2017), mas ainda com amplo espaço para melhora Pereira;Rocha, 2019). Figura 6 -As técnicas de detecção automática de notícias falsas se baseiam em: verifi cação do conteúdo textual e visual da matéria; análise do contexto da notícia, como comentários e perfi s de usuários que a compartilham; e análise da rede de difusão da notícia suspeita, investigando como ela se espalha pelas mídias sociais.…”

Section: Análise Do Mundo Virtualunclassified

A Inteligência Artificial e os desafios da Ciência Forense Digital no século XXI

et al. 2021

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RESUMO A Ciência Forense Digital surgiu da necessidade de tratar problemas forenses na era digital. Seu mais recente desafio está relacionado ao surgimento das mídias sociais, intensificado pelos avanços da Inteligência Artificial. A produção massiva de dados nas mídias sociais tornou a análise forense mais complexa, especialmente pelo aperfeiçoamento de modelos computacionais capazes de gerar conteúdo artificial com alto realismo. Assim, tem-se a necessidade da aplicação de técnicas de Inteligência Artificial para tratar esse imenso volume de informação. Neste artigo, apresentamos desafios e oportunidades associados à aplicação dessas técnicas, além de fornecer exemplos de seu uso em situações reais. Discutimos os problemas que surgem em contextos sensíveis e como a comunidade científica tem abordado esses tópicos. Por fim, delineamos futuros caminhos de pesquisa a serem explorados.

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“…Peng et al (2016b) apply this method to detect astroturfing on social media. Theóphilo et al (2019) employ deep learning specifically for authorship attribution of short messages. Suh et al (2010) leverages features such as URL, number of hashtags, number of followers and followees etc.…”

Section: Author Identification and Verificationmentioning

confidence: 99%

The Trumpiest Trump? Identifying a Subject’s Most Characteristic Tweets

Pethe

Skiena

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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The sequence of documents produced by any given author varies in style and content, but some documents are more typical or representative of the source than others. We quantify the extent to which a given short text is characteristic of a specific person, using a dataset of tweets from fifteen celebrities. Such analysis is useful for generating excerpts of highvolume Twitter profiles, and understanding how representativeness relates to tweet popularity. We first consider the related task of binary author detection (is x the author of text T?), and report a test accuracy of 90.37% for the best of five approaches to this problem. We then use these models to compute characterization scores among all of an author's texts. A user study shows human evaluators agree with our characterization model for all 15 celebrities in our dataset, each with p-value < 0.05. We use these classifiers to show surprisingly strong correlations between characterization scores and the popularity of the associated texts. Indeed, we demonstrate a statistically significant correlation between this score and tweet popularity (likes/replies/retweets) for 13 of the 15 celebrities in our study.

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A Needle in a Haystack? Harnessing Onomatopoeia and User-specific Stylometrics for Authorship Attribution of Micro-messages

Cited by 23 publications

References 12 publications

Biometric Systems De-Identification: Current Advancements and Future Directions

Biometric Systems De-Identification: Current Advancements and Future Directions

A Inteligência Artificial e os desafios da Ciência Forense Digital no século XXI

The Trumpiest Trump? Identifying a Subject’s Most Characteristic Tweets

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