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Synthetic Dialogue Dataset for Romance Scam Detection

Ranaweera, Kanishka ; Ghari Neiat, Azadeh (2026) — Harvard Dataverse

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This is an RSRC catalog record. The original source provides the dataset and its terms of use.

License listed in source metadata: Creative Commons Zero v1.0 Universal (opens in a new tab)

DataCite source metadata checked Sep 29, 2026.

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DOI: 10.7910/dvn/o317px (opens in a new tab)

Type:   Dataset
Tags:   Dating App Conversations, Fraud Classification, Large Language Models, Natural Language Processing, Romance Scam Detection, Social Engineering, Synthetic Dialogue
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Synopsis

            
The publication presents a synthetic dialogue dataset designed to support romance scam detection research. It describes 1,000 conversations generated with two paired personas per scenario: scam (Marcus and Sarah) and legitimate (Jake and Emily). Each conversation comprises 20 messages, resulting in 20,000 total messages, stored as JSONL objects. The scam conversations follow a three-phase arc—warm early exchanges, mid-stage love-bombing and hardship claims, and late-stage financial requests—while legitimate conversations focus on ordinary topics and reciprocal curiosity. The data are produced by a local LLM setup using Ollama, with system prompts guiding each persona’s behavior, and the conversations are archived with a labeled flag indicating scam or legitimate origin. The dataset details, generation workflow, and intended uses are documented extensively. Each line in the dataset includes a conversation_id, label (1 for scam, 0 for legitimate, with 500 of each), a messages array containing sender, message text, and turn number, and a source tag (synthetic_scam or synthetic_legit). The authors outline potential applications such as training binary classifiers, reinforcing RL environments for real-time intervention, federated learning experiments, NLP feature testing, and temporal pattern analysis. They also enumerate limitations, including the synthetic origin, a fixed four-character set, a single scam archetype, uniform 20-message length, and English-only content, noting that real-world patterns may differ. The work emphasizes ethical use for defensive purposes and cautions that models trained only on synthetic data may not generalize to all real-world variants.

          

        
                  

            
Identified Gaps

            
The dataset does not represent authentic user conversations, diverse scam playbooks, multilingual interactions, variable scam timelines, or platform metadata. Its fixed four-persona design and balanced classes leave uncertainty about how well models trained on it generalize to real-world romance-scam detection.

          

        
                  

            
Methods

            
The dataset uses a Python script and Ollama-hosted Meta LLaMA 3 8B to generate 1,000 English dating-app dialogues. Two prompted personas alternate for 20 turns per conversation: 500 scripted scam dialogues and 500 legitimate dialogues. Messages are post-processed to remove quotation marks and character-name prefixes, then stored as labeled JSONL records.

          

        
                  

            
Limitations

            
All dialogues are synthetic and may differ from authentic interactions. Only four fixed personas, one romance-scam archetype, English text, and exactly 20 messages are represented. The dataset lacks timestamps, profiles, multimedia, and other platform metadata; its balanced class distribution is unrealistic; and dialogue quality depends on LLaMA 3 8B and possible safety refusals.

          

        
                  

            
Future Work

            
Supplement synthetic data with ethically obtained real-world conversations for production classifiers. Extend coverage to additional scam variants, communication styles, cultural backgrounds, languages, variable conversation lengths, and non-textual platform metadata.

          

              

    
    

        
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