Modeling and analysis of identity threat behaviors through text mining of identity theft stories

Zaeem, Razieh Nokhbeh; Manoharan, Monisha; Yang, Yongpeng; Barber, K. Suzanne

doi:10.1016/j.cose.2016.11.002

Cited by 55 publications

(17 citation statements)

References 14 publications

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“…Moreover, text mining professionals are increasingly becoming high in demand. Furthermore, text mining may have the power to deliver significant insights to society and individuals, especially with respect to public health [258,259], healthcare [260,261], and education [262][263][264][265], and help evaluate social issues, such as crime (including cybercrime) [245,266,267], child abuse [268], and poverty [269]. Nevertheless, actions must be taken in time to efficiently solve the legal, ethical, and privacy concerns contained in the use of personal data.…”

Section: Discussionmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Section: Discussionmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

View full text Add to dashboard Cite

show abstract

“…Online text is delivered from vast points of network to the servers at different arrival time and in various styles which is very informal in nature. Language dependent factors which do not have an impact to information retrieval are also identified obstacle (Singh and Kumari, 2016;Nokhbeh Zaeem et al, 2017) points out the challenge in dealing with social media text data for fortification of sentiment classification especially in terms of short length and internet slang word. In addition, an existence of uninformative text such as HTML tags, scripts, internet abbreviations and advertisements rises the computational complexity to an upper level as compared to a well-presented text (Petz et al, 2012;Dos Santos and Ladeira, 2014) underline the significant of having a language detector as an additional component to standard text pre-processing process in case of multilingual responses are eligible to a system of response.…”

Section: Role Of Pre-processingmentioning

confidence: 99%

“…Hence, words that do not have the same meaning should be kept separate. In fact, a text classifier can be sometimes negatively affected from text stemming (Nokhbeh Zaeem et al, 2017).…”

Section: Stemmingmentioning

confidence: 99%

“…The definition grasps a spirit of encouragement and inspirations for data analysts to continuously conduct data exploration/exploitation to appropriately enrich the value of existing information about an object of interest. In regard to application-wise, data fusion is nowadays beyond the dominant and matured domain; remote sensor and signal processing, as case studies can be found in condition monitoring (Raheja et al, 2006), crime analysis (Nokhbeh Zaeem et al, 2017), forest management (Chen et al, 2005), and engineering (Steinberg, 2001). The key of data fusion being applied in diverse research domains is about the way the problem is formulated and the choice of methods (Hannah et al, 2000;Starr et al, 2002) recognize the capacity of identifying a parallel between problems under consideration with data fusion models is the key to success.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Role of Pre-processing in Textual Data Fusion: Learn From the Croydon Tram Tragedy

Osman

Kaewunruen

2018

Front. Built Environ.

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Tram/train derailment subject to human mistakes makes investments in an advanced control room as well as information gathering system exaggerated. A disaster in Croydon in year 2016 is recent evidence of limitation of the acquired systems to mitigate human shortcoming in disrupted circumstances. One intriguing way of resolution could be is to fuse continuous online textual data obtained from tram travelers and apply the information for early cautioning of risk discovery. This resolution conveys our consideration regarding a resource of data fusion. The focal subject of this paper is to discuss about role of pre-processing ventures in a low-level data fusion that have been distinguished as a pass to avoid time and exertion squandering amid information retrieval. Inclines in online text data pre-processing is reviewed which comes about an outline suggestion that concede traveler's responses through social media channels. The research outcome shows by a case of data fusion could go about as an impetus to railway industry to effectively partake in data exploration and information investigation.

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“…These studies investigate one or more sociodemographic factors, and many of them use data from social media. The profiling of the author (Wright and Chin, 2014;Stamatatos et al, 2015;Rangel et al, 2016) is a recent task, and the findings are important for Forensic Linguistics among other disciplines (van de Loo et al, 2016;Zaeem et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Identifying the Authors’ National Variety of English in Social Media Texts

Simaki¹,

Simakis²,

Paradis³

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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In this paper, we present a study for the identification of authors' national variety of English in texts from social media. In data from Facebook and Twitter, information about the author's social profile is annotated, and the national English variety (US, UK, AUS, CAN, NNS) that each author uses is attributed. We tested four feature types: formal linguistic features, POS features, lexicon-based features related to the different varieties, and databased features from each English variety. We used various machine learning algorithms for the classification experiments, and we implemented a feature selection process. The classification accuracy achieved, when the 31 highest ranked features were used, was up to 77.32%. The experimental results are evaluated, and the efficacy of the ranked features discussed.

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Modeling and analysis of identity threat behaviors through text mining of identity theft stories

Cited by 55 publications

References 14 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

Role of Pre-processing in Textual Data Fusion: Learn From the Croydon Tram Tragedy

Identifying the Authors’ National Variety of English in Social Media Texts

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