(OS invited talk) Applying Text Analysis to Group Discussion Experiments with Hidden Profile Task

<p>Group decision prediction using MatLab's LSTM network model was performed on the chat logs collected in the mock jury experiments. LSTM is a deep-learning classification method for long-term dependency learning on sequence data. Participants in the experiment were given information about a fictitious murder case and made group decisions in 4-person discussion groups, choosing between "guilty," "not guilty," and "presumed innocent. " The group decisions were predicted from the chats of each discussion group with 82.3% accuracy. However, a sufficient number of judgments of guilt, not guilty, and presumed innocence had to be included in the test data used for the training session. In this experiment, we set the conditions with the information distributed to each group member. An analysis using the LDA topic model was conducted to explore changes in chatlogs due to the allocated information for each member. An ANOVA revealed that the experimental conditions, "sharing (high/low) X information (alibi/dummy)," had effects on the topic proportions. Semantic memory (false memory) measured before and after the group decisions were also correlated with topic proportions. Based on these findings, we will discuss the reliability and usefulness of using machine learning in experimental psychological research.</p>