Why Misinformation is Created? Detecting them by Integrating Intent Features

TL;DR

This paper introduces DM-INTER, a novel misinformation detection method that leverages intent reasoning based on psychological theories to improve accuracy in identifying false information on social media.

Contribution

It proposes a hierarchical intent reasoning framework integrated with article features, enhancing misinformation detection beyond existing methods.

Findings

DM-INTER outperforms baseline methods on benchmark datasets.

Intent features significantly improve veracity discrimination.

Hierarchical intent reasoning effectively captures misinformation characteristics.

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.