Essays on the Rationality of Online Romance Scammers
Bibliographic record
Abstract
The rapid development of the internet has served an essential role in providing communication platforms for people to choose to have personal interactions. One manifestation is using social media platforms and dating services to establish social relationships. The use of online platforms has also provided unscrupulous individuals with malicious intent the ability to target vulnerable victims using bogus romantic intent to obtain money from them. This type of newly evolved cybercrime is called an online romance scamming. To date, online romance scams have spread to every part of the world (i.e., mainly in the United States, China, Canada, Australia, and the UK) and caused considerable financial and emotional damage to victims. Prior research on online romance fraudsters provides a preliminary understanding of the operational features (stages and persuasive techniques) and their modus operandi. However, the objectivity and relevance of the victimization data in explaining offenders' behaviors may render those studies may represent significant drawbacks. To overcome the limitations, it is important to use actual offender data to generate meaningful analyses of romance fraudsters' behaviors. Consequently, this dissertation aims to use experimental data similar to that applied in my previous work (Wang et al., 2021), combined with existing criminological and communication theories, to promote a better understanding of romance fraudsters' behaviors in the online world. This dissertation begins with a scoping review of the current online romance scam literature, intending to use a scientific strategy to address the existing scholarly gap in this field of research. Derived from rational choice theory, the criminal events perspective, interpersonal deception theory, and neutralization theory, the second and third paper uses an experimental approach to assess the influence of rewards on romance fraudsters' behaviors. The three papers' results demonstrate the rationality of online romance fraudsters when facing rewards. Moreover, such rationality can be explicitly seen from their uses of different linguistic cues. Finally, the outcomes provided in the current project also provide policymakers the information about the rationality and modus operandi of fraudsters which can be used to identify the behavioral patterns at an early phase to prevent significant harm to the victim.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".