Why Misinformation is Created? Detecting them by Integrating Intent Features

TL;DR

This work tackles misinformation detection by integrating article-level intent reasoning through a psychology-informed intent hierarchy. DM-INTER employs a T5-based encoder-decoder to progressively reason intents, produce an intent feature, and fuse it with token representations to improve veracity classification, optimized with a self-training objective L_D and adaptive sample weighting. Experiments across GossipCop, PolitiFact, and Snopes show consistent improvements over strong baselines, with ablations confirming the contribution of each component and case studies illustrating interpretable reasoning traces. The approach offers a concrete, pluggable extension to existing MD systems and highlights the practical value of modeling creator intent as a discriminative signal for misinformation.

Abstract

Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full of misinformation, causing damage to diverse aspects of our daily lives. To reduce the negative impact, timely identification of misinformation, namely Misinformation Detection (MD), has become an active research topic receiving widespread attention. As a complex phenomenon, the veracity of an article is influenced by various aspects. In this paper, we are inspired by the opposition of intents between misinformation and real information. Accordingly, we propose to reason the intent of articles and form the corresponding intent features to promote the veracity discrimination of article features. To achieve this, we build a hierarchy of a set of intents for both misinformation and real information by referring to the existing psychological theories, and we apply it to reason the intent of articles by progressively generating binary answers with an encoder-decoder structure. We form the corresponding intent features and integrate it with the token features to achieve more discriminative article features for MD. Upon these ideas, we suggest a novel MD method, namely Detecting Misinformation by Integrating Intent featuRes (DM-INTER). To evaluate the performance of DM-INTER, we conduct extensive experiments on benchmark MD datasets. The experimental results validate that DM-INTER can outperform the existing baseline MD methods.

Figures (3)

• Figure 1: The hierarchy of expressed intents of articles.
• Figure 2: The overall framework of Dm-inter. An article $\mathbf{x}_i$ is fed into the T5 encoder to obtain an article feature $\mathbf{h}_i^e$. This feature is then input into the T5 decoder to reason the intents and extract the intent feature $\mathbf{\widehat{h}}_i^d$. Finally, the two features are fused and classified for veracity.
• Figure 3: Sensitivity analysis of the trade-off parameter $\beta$.