Detecting Textual Deception in Courtroom Testimonies for Romanian Language Using Neural Network Methods

Detecting deception through automated tools involves computational techniques to identify falsehoods in text, which is crucial for evaluating the truthfulness of testimonies in the legal field. The present research explores deception detection in Romanian texts using various methods. Initially, ANNs with different text representations, such as TF-IDF, LSA, and LIWC features were employed. These approaches were compared with BERT-based models on augmented data, assessing improvements in classification performance. Additionally, CNNs with embedding representations were used to examine the impact of context on detection accuracy. To gain deeper insights, LIME (Local Interpretable Model-agnostic Explanations) was applied to visualise the importance of specific words and phrases in court statements. The present work is particularly novel for Romanian, as research on textual perjury detection in this language is limited. The obtained results are competitive with existing literature, highlighting the potential of the proposed approaches and impacting the fields of forensic linguistics and natural language processing, especially given the lack of research in non-English languages.