  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.
