Measuring Shared Knowledge in Group Discussions Through Text Analysis
This study addresses the challenge of quantifying shared knowledge in group discussions through text analysis. Topic modeling was applied to systematically evaluate how information sharing influences knowledge structures and decision-making
This study addresses the challenge of quantifying shared knowledge in group discussions through text analysis. Topic modeling was applied to systematically evaluate how information sharing influences knowledge structures and decision-making. In an online group discussion setting, two mock jury experiments involving 204 participants were conducted to reach a consensus on a verdict for a fictional murder case.
The first experiment investigated whether the bias in pre-shared information influenced the topic ratios of each participant. Topic ratios, derived from a Latent Dirichlet Allocation model, were assigned to each participant’s chat lines.
The presence or absence of shared information, as well as the type of information shared, systematically influenced the topic ratios that appeared in group discussions. In Experiment 2, false memories were assessed before and after the discussion to evaluate whether the topics identified in Experiment 1 measured shared knowledge. Mediation analysis indicated that a higher topic ratio related to evidence was statistically associated with an increased likelihood of false memory for evidence.
These results suggested that topics yielded by LDA reflected the knowledge structure shared during group discussions.
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