Online romance scam: Expensive e-living for romantic happiness
- Authors: Kopp, Christian , Sillitoe, James , Gondal, Iqbal , Layton, Robert
- Date: 2016
- Type: Text , Conference proceedings
- Relation: Proceedings of the 29th Bled eConference: Digital Economy (BLED 2016), Slovenia, pp.175-189 p. 15
- Full Text:
- Description: The Online Romance Scam is a very successful scam which causes considerable financial and emotional damage to its victims. It is based on building a relationship which establishes a deep trust that causes victims to voluntarily transfer funds to the scammer. The aim of this research is to explore online dating scams as a type of e-Living which initially creates happiness for the victim in a virtual romantic relationship, but tragically then causes the victim to be separated from his or her savings. Using narrative research methodology, this research will establish a model of the romance scam structure and its variations regarding human romantic attitudes, and will develop a theory which explains how the victim is moved through the phases of the scam. Findings of this research will contribute to the knowledge of the Online Romance Scam as e-Crime and provide information about the structure and the development of the modus operandi which can be used to identify an online relationship as a scam at an early phase in order to prevent significant harm to the victim.
Improving authorship attribution in twitter through topic-based sampling
- Authors: Pan, Luoxi , Gondal, Iqbal , Layton, Robert
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 30th Australasian Joint Conference on Artificial Intelligence, AI 2017 : Advances in Artificial Intelligence; Melbourne, Australia; 19th-20th August 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10400 LNAI, p. 250-261
- Full Text: false
- Reviewed:
- Description: Aliases are used as a means of anonymity on the Internet in environments such as IRC (internet relay chat), forums and micro-blogging websites such as Twitter. While there are genuine reasons for the use of aliases, such as journalists operating in politically oppressive countries, they are increasingly being used by cybercriminals and extremist organisations. In recent years, we have seen increased research on authorship attribution of Twitter messages, including authorship analysis of aliases. Previous studies have shown that anti-aliasing of randomly generated sub-aliases yields high accuracies when linking the sub-aliases, but become much less accurate when topic-based sub-aliases are used. N-gram methods have previously been demonstrated to perform better than other methods in this situation. This paper investigates the effect of topic-based sampling on authorship attribution accuracy for the popular micro-blogging website Twitter. Features are extracted using character n-grams, which accurately capture differences in authorship style. These features are analysed using support vector machines using a one-versus-all classifier. The predictive performance of the algorithm is then evaluated using two different sampling methodologies - authors that were sampled through a context-sensitive topic-based search and authors that were sampled randomly. Topic-based sampling of authors is found to produce more accurate authorship predictions. This paper presents several theories as to why this might be the case. © Springer International Publishing AG 2017.