CLMIR: A Textual Dataset for Rumor Identification and Marking

TL;DR

This paper introduces CLMIR, a new dataset for rumor detection that not only identifies rumors but also marks the specific content within posts that constitutes the rumor, enhancing interpretability.

Contribution

The creation of CLMIR, a fine-grained rumor dataset that enables content marking, facilitating more interpretable and precise rumor detection algorithms.

Findings

Enables training of rumor detection models with content marking

Improves interpretability and reasoning in rumor detection systems

Supports practical applications like rumor tracing and moderation

Abstract

With the rise of social media, rumor detection has drawn increasing attention. Although numerous methods have been proposed with the development of rumor classification datasets, they focus on identifying whether a post is a rumor, lacking the ability to mark the specific rumor content. This limitation largely stems from the lack of fine-grained marks in existing datasets. Constructing a rumor dataset with rumor content information marking is of great importance for fine-grained rumor identification. Such a dataset can facilitate practical applications, including rumor tracing, content moderation, and emergency response. Beyond being utilized for overall performance evaluation, this dataset enables the training of rumor detection algorithms to learn content marking, and thus improves their interpretability and reasoning ability, enabling systems to effectively address specific rumor segments. This paper constructs a dataset for rumor detection with fine-grained markings, named CLMIR (Content-Level Marking Dataset for Identifying Rumors). In addition to determining whether a post is a rumor, this dataset further marks the specific content upon which the rumor is based.