Mining e-mail content for author identification forensics

By Olivier Y. de Vel, Alison Anderson, Malcolm Corney, George M. Mohay. SGMD, 2001

604 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/67a09f2a68726f676b05a6f15635c566218db373

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Comparative study on methods of detecting research fronts using different types of citation (2009, methodology): “( de Vel, et al., 2001; Zheng, et al., 2006).”
Computational methods in authorship attribution (2009, positive): “Finally, for documents such as email, blogs and other online content, formatting and other structural features can also be profitably exploited for authorship attribution (De Vel et al. 2001; Corney et al. 2002; Abbasi & Chen 2008).”
A survey of modern authorship attribution methods (2009, methodology): “…be used to build a classification model, including discriminant analysis (Chaski, 2005; Stamatatos et al., 2000; Tambouratzis et al., 2004), SVM (de Vel et al., 2001; Diederich et al, 2003; Li et al., 2006; Sanderson & Guenter, 2006; Teng et al., 2004), decision trees (Uzuner & Katz, 2005; Zhao &…”
Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying (2015, neutral): “authorship of, for example, e-mails [14].”
A framework for authorship identification of online messages: Writing-style features and classification techniques (2006, methodology): “Different sets of function words, ranging from 12 to 122, have been tested in various studies (Baayen et al., 1996; Burrows, 1989; de Vel et al., 2001; Holmes & Forsyth, 1995; Tweedie & Baayen, 1998).”
Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset (2018, neutral): “In such cases, Support Vector Machines have been used to identify the author of an e-mail based on the text itself [22].”
Applying authorship analysis to extremist-group Web forum messages (2005, background): “Paragraph level ( 4 ) (9) Special character Character level”
Crime data mining: a general framework and some examples (2004, methodology): “This technique has been used to identify the source of e-mail spamming based on the sender’s linguistic patterns and structural features.(10) Often used to predict crime trends, classification can reduce the time required to identify crime entities.”
Behavioural biometrics: a survey and classification (2008, methodology): “Vel et al. (2001) have applied authorship identification techniques to determine the likely author of an e-mail message.”
Connecting users across social media sites: a behavioral-modeling approach (2013, methodology): “In particular, machine learning techniques have been employed to detect authors in online messages [20] and in E-mails [8].”
On the Feasibility of Internet-Scale Author Identification (2012, neutral): “Some important works are [22; 23; 24].”