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This paper aims to explore fraud’s pervasive nature and social media’s critical role in modern forensic investigations. As fraudsters increasingly exploit social media, investigators must continuously educate themselves on emerging means of effectively identifying, collecting and analyzing social media data. This paper highlights the role of social media in detecting and preventing fraud by providing digital evidence. It outlines the forensic process – evidence identification, collection and examination – and emphasizes using commercial and open-source tools to gather, analyze and secure evidence. The research contributes insights into the evolving fraud detection techniques using social media.

This paper uses a narrative review (Sylvester et al., 2013).

Social media can aid fraud investigations by revealing suspect behaviors (witness tampering in The Prosecutor v. Jean-Pierre Bemba Gombo), networks (Operation Firewall) and communications, offering real-time data, and providing evidence of fraudulent activities through messages, connections and posts (US v. Brooklyn Men). An extensive review of commercial forensic software packages highlights their utility in preserving and verifying social media data’s authenticity for legal proceedings. In addition, the paper discusses open-source tools like HTTrack and TweetBeaver.

An extensive review of commercial forensic software packages highlights their utility in preserving and verifying social media data’s authenticity for legal proceedings. In addition, the paper discusses open-source tools like HTTrack and TweetBeaver. Future research directions discussed include the development of advanced SOCMINT tools, AI and machine learning integration and examining how social media policy changes impact forensic investigations.

The social media forensic analysis procedure includes these steps: forensic investigator orientation, fraud identification, due diligence search, collection and preservation of social media data, technical search and forensic analysis. The paper also addresses the challenge posed by increasing awareness among criminals, who may alter their online behaviors to evade detection.

All content in this paper is original in its creation.

The purpose of this paper is to define artificial intelligence (AI) and examine its history, positive and negative impacts, ethical and social implications and implementation within management education. This paper offers various suggestions for the use of AI, as well as context surrounding the current AI landscape.

The paper uses a narrative review (Sylvester et al., 2013).

This paper identifies several areas of AI innovation, including AI tutoring systems, feedback systems for student papers, utilization of AI for innovative lesson plans and the use of AI to predict potential student dropout from a course or institution. In addition, there are significant concerns regarding the lack of ethical guidelines with current AI.

Practical implications include the ability to immediately use certain AI tools to enhance lesson plans as well as enhance student work using AI as a tool.

This paper was originally created as a conference presentation and presented at the society for advancement of management (SAM) International Business Conference before being reworked to be submitted to the journal. All content in this paper is original in their creation.