Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation

TL;DR

This paper introduces an explainable model that detects implicit influential patterns in conversations, leveraging data augmentation with language models, resulting in improved accuracy and interpretability in identifying subtle influence tactics.

Contribution

It presents a novel approach combining data augmentation and explainability to detect and locate implicit influence patterns in conversations, surpassing previous methods.

Findings

6% improvement in implicit pattern detection

33% enhancement in influence technique classification

43% better vulnerability identification

Abstract

In the era of digitalization, as individuals increasingly rely on digital platforms for communication and news consumption, various actors employ linguistic strategies to influence public perception. While models have become proficient at detecting explicit patterns, which typically appear in texts as single remarks referred to as utterances, such as social media posts, malicious actors have shifted toward utilizing implicit influential verbal patterns embedded within conversations. These verbal patterns aim to mentally penetrate the victim's mind in order to influence them, enabling the actor to obtain the desired information through implicit means. This paper presents an improved approach for detecting such implicit influential patterns. Furthermore, the proposed model is capable of identifying the specific locations of these influential elements within a conversation. To achieve this, the existing dataset was augmented using the reasoning capabilities of state-of-the-art language models. Our designed framework resulted in a 6% improvement in the detection of implicit influential patterns in conversations. Moreover, this approach improved the multi-label classification tasks related to both the techniques used for influence and the vulnerability of victims by 33% and 43%, respectively.