Identifying Fabricated Networks within Authorship-for-Sale Enterprises (2401.04022v1)

Abstract: Fabricated papers do not just need text, images, and data, they also require a fabricated or partially fabricated network of authors. Most authors' on a fabricated paper have not been associated with the research, but rather are added through a transaction. This lack of deeper connection means that there is a low likelihood that co-authors on fabricated papers will ever appear together on the same paper more than once. This paper constructs a model that encodes some of the key characteristics of this activity in anauthorship-for-sale' network with the aim to create a robust method to detect this type of activity. A characteristic network fingerprint arises from this model that provides a robust statistical approach to the detection of paper-mill networks. The model suggested in this paper detects networks that have a statistically significant overlap with other approaches that principally rely on textual analysis for the detection of fraudulent papers. Researchers connected to networks identified using the methodology outlined in this paper are shown to be connected with 37% of papers identified through the tortured-phrase and clay-feet methods deployed in the Problematic Paper Screener website. Finally, methods to limit the expansion and propagation of these networks is discussed both in technological and social terms.

• The paper introduces an innovative co-authorship model using network metrics like clustering and publication age to distinguish fraudulent collaborations.
• The methodology flagged 72% of suspicious cases by correlating unusual network patterns with independent indicators of research fraud.
• The findings offer actionable insights for publishers and institutions to conduct targeted risk profiling against paper mills.

Identifying Fabricated Networks within Authorship-for-Sale Enterprises: A Co-authorship Network Approach

The paper, "Identifying Fabricated Networks within Authorship-for-Sale Enterprises," authored by Simon J. Porter and Leslie D. McIntosh, introduces an innovative methodology for detecting paper mills by focusing on social network anomalies, specifically within co-authorship networks. Paper mills represent a serious breach of research integrity, generating fabricated papers with fabricated or inauthentic author collaborations. This paper meticulously constructs a model to identify the haLLMark patterns of such activity, diverging from traditional approaches that rely on analysis of the paper content itself.

Core Model and Methodology

The proposed model is built on a theoretical foundation that leverages identifiable patterns left by authorship-for-sale enterprises in co-authorship networks. Notably, authors involved in such operations often show unique network characteristics, such as:

• Youth of Publication History: Authors are predominantly in earlier stages of their careers, seeking to rapidly inflate their publication lists.
• Low Clustering Coefficient: High-volume authorship tends to form around a central author, lacking the typical interconnectedness seen in genuine research communities.
• Foundation Authors: Some authors, with established yet possibly compromised reputations, appear recurrently to lend believability to these fraudulent publications.
• Limited Mentorship Patterns: Authentic co-authorship networks typically show mentorship roles, often absent in fabricated networks.
• Higher-than-normal Author Counts: Similar to a commercial enterprise, paper mills aim to maximize return by selling more authorship slots per paper.

The methodology employs data from Dimensions, a comprehensive database including unique researcher identifiers and affiliations, enabling robust academic network analysis. By calculating network shapes—conceptual patterns of nodes and edges in co-authorship maps—the authors assign a uniqueness measure to these shapes. The paper methodically categorizes network shapes and establishes thresholds that indicate deviations consistent with paper-mill activities.

Results and Validation

The paper's validation is noteworthy, as the model's predictions show substantial overlap with independent datasets like the Problematic Paper Screener (PPS), identifying fraudulent papers based on text anomalies. Impressively, 72% of researchers flagged by the authorship-for-sale model connected to papers with known language manipulations, underscoring the method's effectiveness. Additionally, the linkage of suspicious authors to over 7,400 peer reviews within this network suggests an entrenched system that supports fraudulent papers through compromised peer review processes.

Implications and Applications

This research holds significant implications for academic publishing, suggesting more efficient ways to uncover authorship-for-sale participation and potentially curtail fraudulent practices. Application fronts include:

• Publisher Risk Profiling: Enabling publishers to pinpoint journals or sections with high exposure to suspect authorships, facilitating targeted investigations.
• Institutional Actions: Institutions can utilize author-centric evaluations to internally manage and potentially preempt integrity issues before they escalate.

Future Directions

Looking forward, the research opens avenues for leveraging machine learning and network theory further to enhance detection models, integrating with extant research databases, and yielding promising collaboration between research entities for improved monitoring. Converging technological and social strategies could greatly aid in preemptive measures against the establishment of suspicious author profiles, maintaining the integrity of scientific literature in an era increasingly challenged by sophisticated fraudulent practices.

In conclusion, Porter and McIntosh's work exemplifies the evolving landscape of research integrity preservation, advocating for a systemic approach encompassing technological prowess and human curatorial diligence. Collaborative and transparent efforts remain paramount in safeguarding the academic enterprise's foundational trust.