The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Probabilistic Evaluation of SMS Messages as Forensic Evidence: Likelihood Ratio Based Approach with Lexical Features
Abstract
This study is one of the first likelihood ratio-based forensic text comparison studies in forensic authorship analysis. The likelihood-ratio-based evaluation of scientific evidence has started being adopted in many disciplines of forensic evidence comparison sciences, such as DNA, handwriting, fingerprints, footwear, voice recording, etc., and it is largely accepted that this is the way to ensure the maximum accountability and transparency of the process. Due to its convenience and low cost, short message service (SMS) has been a very popular medium of communication for quite some time. Unfortunately, however, SMS messages are sometimes used for reprehensible purposes, e.g., communication between drug dealers and buyers, or in illicit acts such as extortion, fraud, scams, hoaxes, and false reports of terrorist threats. In this study, the author performs a likelihood-ratio-based forensic text comparison of SMS messages focusing on lexical features. The likelihood ratios (LRs) are calculated in Aitken and Lucy’s (2004) multivariate kernel density procedure, and are calibrated. The validity of the system is assessed based on the magnitude of the LRs using the log-likelihood-ratio cost (Cllr). The strength of the derived LRs is graphically presented in Tippett plots. The results of the current study are compared with those of previous studies.
Related Content
|
Hossam Nabil Elshenraki.
© 2024.
23 pages.
|
|
Ibtesam Mohammed Alawadhi.
© 2024.
9 pages.
|
|
Akashdeep Bhardwaj.
© 2024.
33 pages.
|
|
John Blake.
© 2024.
12 pages.
|
|
Wasswa Shafik.
© 2024.
36 pages.
|
|
Amar Yasser El-Bably.
© 2024.
12 pages.
|
|
Sameer Saharan, Shailja Singh, Ajay Kumar Bhandari, Bhuvnesh Yadav.
© 2024.
23 pages.
|
|
|