Linguistic deception of Chinese cyber fraudsters

Cybercrimes are on the increase in China and ‘QQ’, an instant messenger platform, is frequently exploited for these crimes. Fraudsters manipulate language to deceive users into revealing their bank accounts or depositing sums in the cheats’ accounts. Employing the theoretical framework that includes Speech Act Theory and Politeness Theory, the researchers attempted to identify the strategies used by such fraudsters. The subjects of this study included 50 interlocutors who had already chatted with different online cheats and had a record of their conversations. The data were collected and analysed on the basis of the type of discourse themes displayed. Findings indicated that the chats displayed various themes like Business Invitation, Money Transfer, Account Hacking and Online Shopping. In addition, the three levels of speech acts of locutionary, illocutionary, and perlocutionary were discernible and most fraudsters did not bother to address face threatening acts. In comparison to hoax email writers, the fraudsters in instant communication regularly came across as more aggressive and imperative, but then softened their diction if victims were not interested to chat with them in real time. The implications of this study lie in the possibility of developing a model for fraudster or cheat discourse structure, thus alerting QQ users in particular of such crimes. Other online instant messenger users will also benefit from this study. Better informed of how cheats manipulate language to present untruth as truth and be alerted of the modus operandi involved in online deception, victims can be saved and the crime curbed. The issue of the victim’s vulnerability and the reasons behind it certainly deserve further linguistic and metalinguistic scrutiny

Detecting Deceptive Impression Management Behaviors in Interviews Using Natural Language Processing

Deceptive impression management (IM) is often used by applicants in employment interviews to improve their chances of receiving a job offer. Self-report measures of deceptive IM are typically used to evaluate interview faking in a lab setting but are limited when used in practice due to social desirability concerns. Given this limitation, natural language processing (NLP) has potential as a tool to unobtrusively assess raw interview content and measure deceptive IM. This study examined the use of open and closed-vocabulary NLP approaches for the detection of deceptive IM in mock employment interviews. In general, neither of these approaches successfully predicted deceptive IM. Several possible conclusions based on these findings are discussed. However, given the lack of empirical support for this method, organizations should proceed with caution when deciding to use NLP techniques to predict deceptive IM in employment interviews

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author