Authorship Verification of Online Messages for Forensic Investigation

Nirkhi, Smita; Dharaskar, Rajiv V.; Thakare, V. M.

doi:10.1016/j.procs.2016.02.111

Cited by 23 publications

(9 citation statements)

References 11 publications

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“…This task has been well studied in multiple applications; the most traditional one is assigning anonymous literary to authors [ 78 , 79 ]. Additionally, it has been used in forensics to identify authors that are involved in internet-based activity in different text genres such as online messaging (eg, emails) [ 80 ], news text data set [ 81 ], and social media [ 82 , 83 ]. However, in our work, we focus on attribution of short text or sentences in notes.…”

Section: Methodsmentioning

confidence: 99%

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Zirikly¹,

Desmet²,

Newman-Griffis³

et al. 2022

JMIR Med Inform

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Natural language processing (NLP) in health care enables transformation of complex narrative information into high value products such as clinical decision support and adverse event monitoring in real time via the electronic health record (EHR). However, information technologies for mental health have consistently lagged because of the complexity of measuring and modeling mental health and illness. The use of NLP to support management of mental health conditions is a viable topic that has not been explored in depth. This paper provides a framework for the advanced application of NLP methods to identify, extract, and organize information on mental health and functioning to inform the decision-making process applied to assessing mental health. We present a use-case related to work disability, guided by the disability determination process of the US Social Security Administration (SSA). From this perspective, the following questions must be addressed about each problem that leads to a disability benefits claim: When did the problem occur and how long has it existed? How severe is it? Does it affect the person’s ability to work? and What is the source of the evidence about the problem? Our framework includes 4 dimensions of medical information that are central to assessing disability—temporal sequence and duration, severity, context, and information source. We describe key aspects of each dimension and promising approaches for application in mental functioning. For example, to address temporality, a complete functional timeline must be created with all relevant aspects of functioning such as intermittence, persistence, and recurrence. Severity of mental health symptoms can be successfully identified and extracted on a 4-level ordinal scale from absent to severe. Some NLP work has been reported on the extraction of context for specific cases of wheelchair use in clinical settings. We discuss the links between the task of information source assessment and work on source attribution, coreference resolution, event extraction, and rule-based methods. Gaps were identified in NLP applications that directly applied to the framework and in existing relevant annotated data sets. We highlighted NLP methods with the potential for advanced application in the field of mental functioning. Findings of this work will inform the development of instruments for supporting SSA adjudicators in their disability determination process. The 4 dimensions of medical information may have relevance for a broad array of individuals and organizations responsible for assessing mental health function and ability. Further, our framework with 4 specific dimensions presents significant opportunity for the application of NLP in the realm of mental health and functioning beyond the SSA setting, and it may support the development of robust tools and methods for decision-making related to clinical care, program implementation, and other outcomes.

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Section: Methodsmentioning

confidence: 99%

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Zirikly¹,

Desmet²,

Newman-Griffis³

et al. 2022

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…al. , [12], Focus on comparing the similarity between given unknown documents against the known documents using various features so that an unknown document can be classified as having been written by the same author by application of unsupervised techniques for authorship verification problem. Farkhund I., and et.…”

Section: Previous Workmentioning

confidence: 99%

“…Due to their privacy issues and their secrecy, few e-mail data are available publicly for experiments. The exception to the above statement is the Enron Corpus [12]. It has been followed the concepts and principles mentioned in this section to preprocessing and evaluation metric a reader may be fixed…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

Digital Cyber Forensics Contribution for Email Analysis

Hamdi

Radhi

2020

jeasd

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In the past two decades, the Internet has become as open, publicly and widely used as a source of data transmission and exchanging the messages between criminals, terrorists and those who have illegal motivations. Moreover, exchanging important data between various military and financial institutions, even ordinary citizens. From this view, there is one of the important means of exchanging information widely used on the Internet medium is e-mail. Email messages are digital evidence that has been become one of the important means to adopt by courts in many countries and societies as evidence relied upon in condemnation. This paper presents a distinct technique for classifying emails based on data processing and mining, trimming, refinement, and then adapts several algorithms to classify these emails and then using SWARM algorithm to obtain practical and accurate results also using hybrid English lexical dictionary SentiWordNet3.0 for email forensic analysis then deal with a machine learning algorithm. The proposed system is capable of learning in an environment with large and variable data. To test the proposed system, have to select available data which Enron Data set. A high accuracy rate (95%) was obtained, which is higher than the classification rates mentioned in previous research papers presented in section 2 in this paper.

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“…This is useful if the number of authors is fixed, and the problem is modeled as multi-class classification. Mohsen et al ( 2016) also approach multi-class author identification, using deep learning for feature extraction, and Nirkhi et al (2016) using hierarchical clustering. Potha and Stamatatos (2018) propose an intrinsic profile-based verification method that uses latent semantic indexing (LSI), which is effective for longer texts.…”

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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Authorship Verification of Online Messages for Forensic Investigation

Cited by 23 publications

References 11 publications

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Digital Cyber Forensics Contribution for Email Analysis

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

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