A system for the automated author attribution of text and instant messages

Donais, Jonathan A.; Frost, Richard A.; Peelar, Shane; Roddy, Robert A.

doi:10.1145/2492517.2500308

Cited by 7 publications

(9 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instance-based approaches are usually implemented using clustering algorithms or machine learning classifiers such as Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), or Decision Trees (DTs). Some studies have been conducted specifically using short digital texts such as Tweets [6], [16], [17], [18], [19], [30], [33], SMS [4], [5], or small pieces of texts from blog posts [35]. They are indicated in bold in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

Writer Identification Using Microblogging Texts for Social Media Forensics

Alonso-Fernandez,

Belvisi,

Hernandez-Diaz

et al. 2020

Preprint

View full text Add to dashboard Cite

Establishing the authorship of online texts is a fundamental issue to combat several cybercrimes. Unfortunately, some platforms limit the length of the text, making the challenge harder. Here, we aim at identifying the author of Twitter messages limited to 140 characters. We evaluate popular stylometric features, widely used in traditional literary analysis, which capture the writing style at different levels (character, word, and sentence). We use a public database of 93 users, containing 1142 to 3209 Tweets per user. We also evaluate the influence of the number of Tweets per user for enrolment and testing. If the amount is sufficient (>500), a Rank 1 of 97-99% is achieved. If data is scarce (e.g. 20 Tweets for testing), the Rank 1 with the best individual feature method ranges from 54.9% (100 Tweets for enrolment) to 70.6% (1000 Tweets). By combining the available features, a substantial improvement is observed, reaching a Rank 1 of 70% when using 100 Tweets for enrolment and only 20 for testing. With a bigger hit list size, accuracy of the latter case increases to 86.4% (Rank 5) or 95% (Rank 20). This demonstrates the feasibility of identifying writers of digital texts, even with few data available.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Writer Identification Using Microblogging Texts for Social Media Forensics

Alonso-Fernandez,

Belvisi,

Hernandez-Diaz

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Some other approaches detected intrusion and compromised accounts in short messages by applying text mining techniques as Authorship Attribution (AA) and AV [5,4]. Their main contribution was to aid the search for cyber criminals [27] or to increase cyber space security and reliability [5].…”

Section: Related Workmentioning

confidence: 99%

Recognition of Compromised Accounts on Twitter

Igawa

Almeida

Zarpelão

et al. 2015

Anais Do Simpósio Brasileiro De Sistemas De Informação (SBSI)

View full text Add to dashboard Cite

In this work, we propose an approach for recognition of compromised Twitter accounts based on Authorship Verification. Our solution can detect accounts that became compromised by analysing their user writing styles. This way, when an account content does not match its user writing style, we affirm that the account has been compromised, similar to Authorship Verification. Our approach follows the profile-based paradigm and uses N-grams as its kernel. Then, a threshold is found to represent the boundary of an account writing style. Experiments were performed using a subsampled dataset from Twitter. Experimental results showed that the developed model is very suitable for compromised recognition of Online Social Networks accounts due to the capability of recognize user styles over 95% accuracy.

show abstract

“…Some other approaches detected intrusion and compromised accounts in short messages by applying text mining techniques as Authorship Attribution (AA) and AV [Donais et al 2013, Brocardo et al 2013, Brocardo et al 2014. Their main contribution was to aid the search for cyber criminals [Zhang et al 2014] or to increase cyber space security and reliability [Donais et al 2013].…”

Section: Related Workmentioning

confidence: 99%

Recognition on Online Social Network by user's writing style

Igawa

Almeida

Zarpelão

et al. 2015

iSys

View full text Add to dashboard Cite

Compromising legitimate accounts is the most popular way of disseminating fraudulent content in Online Social Networks (OSN). To address this issue, we propose an approach for recognition of compromised Twitter accounts based on Authorship Verification. Our solution can detect accounts that became compromised by analysing their user writing styles. This way, when an account content does not match its user writing style, we affirm that the account has been compromised, similar to Authorship Verification. Our approach follows the profile-based paradigm and uses N-grams as its kernel. Then, a threshold is found to represent the boundary of an account writing style. Experiments were performed using two subsampled datasets from Twitter. Experimental results showed the developed model is very suitable for compromised recognition of Online Social Networks accounts due to the capacity of recognizing user styles over 95% accuracy for both datasets.

show abstract

A system for the automated author attribution of text and instant messages

Abstract: The paper presents a summary of the ChatSafe

Cited by 7 publications

References 1 publication

Writer Identification Using Microblogging Texts for Social Media Forensics

Writer Identification Using Microblogging Texts for Social Media Forensics

Recognition of Compromised Accounts on Twitter

Recognition on Online Social Network by user's writing style

Contact Info

Product

Resources

About