{'fraction_non_alphanumeric': 0.02186761229314421, 'fraction_numerical': 0.013002364066193853, 'mean_word_length': 5.317164179104478, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Bayesian Belief Network Methodology for Modeling Social Systems in Virtual Communities: Opportunities for Database Technologies

Bayesian Belief Networks (BBNs) are increasingly used for understanding and simulating computational models in many domains. Though BBN techniques are elegant ways of capturing uncertainties, knowledge engineering effort required to create and initialize the network has prevented many researchers from using them. Even though the structure of the network and its conditional & initial probabilities could be learned from data, data is not always available and/or too costly to obtain. Further, current algorithms that can be used to learn relationships among variables, initial and conditional probabilities from data are often complex and cumbersome to employ. Qualitative-based approaches applied to the creation of graphical models can be used to create initial computational models that can help researchers analyze complex problems and provide guidance/support for decision-making. Once created, initial BBN models can be refined once appropriate data is obtained. This chapter extends the use of BBNs to help experts make sense of complex social systems (e.g., social capital in virtual communities) using a Bayesian model as an interactive simulation tool. Scenarios are used to update the model and to find out whether the model is consistent with the expert’s beliefs. A sensitivity analysis was conducted to help explain how the model reacted to different sets of evidence. Currently, we are in the process of refining the initial probability values presented in the model using empirical data and developing more DOI: 10.4018/978-1-60566-814-7.ch008