Rumor Detection on Social Media with Crowd Intelligence and C hat GPT -Assisted Networks (2023)

Abstract: In the era of widespread dissemination through social media, the task of rumor detection plays a pivotal role in establishing a trustworthy and reliable information environment. Nonetheless, existing research on rumor detection confronts several challenges: the limited expressive power of text encoding sequences, difficulties in domain knowledge coverage and effective information extraction with knowledge graph-based methods, and insufficient mining of semantic structural information. To address these issues, we propose a Crowd Intelligence and ChatGPT-Assisted Network(CICAN) for rumor classification. Specifically, we present a crowd intelligence-based semantic feature learning module to capture textual content’s sequential and hierarchical features. Then, we design a knowledge-based semantic structural mining module that leverages ChatGPT for knowledge enhancement. Finally, we construct an entity-sentence heterogeneous graph and design Entity-Aware Heterogeneous Attention to effectively integrate diverse structural information meta-paths. Experimental results demonstrate that CICAN achieves performance improvement in rumor detection tasks, validating the effectiveness and rationality of using large language models as auxiliary tools.

## introduction in recent years, the number of rumors posted on the internet has increased significantly, and the harm of rumors is that they may cause panic and unnecessary actions, undermine social stability and trust, and negatively affect individuals, organizations, and society @xcite . unlike traditional media channels, information on social networks has the characteristics of fast dissemination and wide coverage, leading to a large amount of information that cannot be verified in real-time @xcite . in response to this problem, rumor detection methods based on crowd intelligence have attracted much attention. crowd intelligence means social media users spread and interpret information by sharing personal opinions positions, questioning, and providing evidence @xcite . we realize that rumors are the result of the interaction between social media and swarm intelligence, a phenomenon of information dissemination among groups. that is, the characteristics of swarm intelligence are the fundamental reason why rumor detection becomes more complicated. from the perspective of crowd intelligence, we redefine rumors based on existing viewpoints @xcite @xcite : the rumor is an influential but unverified statement widely disseminated on social networks and other channels through people's collective participation and information interaction. social media users usually share their views on the veracity of rumors through collective, subjective understanding @xcite , which may help reveal the truth behind the rumors. therefore, the crowd's cognitive mechanism for rumors has important guiding significance for rumor detection, especially the detection method based on crowd intelligence. however, there are still some problems in effectively utilizing crowd intelligence in rumor detection: based on the above challenges and shortcomings, this paper proposes crowd intelligence and chatgpt-assisted network(cican) that uses crowd intelligence and introduces a large language model as an auxiliary tool, combining semantic content and structure information for rumor detection. the model includes the crowd intelligencebased semantic feature learning module (cis) and the knowledge-based semantic structure mining module (kss). first, we propose a contextaware semantic encoder and a multi-granularity feature extractor for extracting semantic content features, leveraging tweetbert to enhance the model's ability to handle semantic and contextual relevance, and combining bilstm and capsule networks for sequential and hierarchical feature learning. then, by leveraging chatgpt to extract refined and structured knowledge from the raw data, we construct the entity-sentence heterogeneous graph containing two types of nodes and design a new hierarchical attention mechanism to enhance the model's ability to capture complex relationships. our main contributions are as follows: ## problem statement in this paper, we formulate the rumor detection problem as a classification task. we aim to learn a function p(y |v, z; θ), where v represents the semantic content information, and z represents the semantic structure information. the input consists of text information containing source tweets and comments, as well as the entity-sentence heterogeneous graph structure information constructed from external knowledge. all the learnable parameters of the model are denoted as θ. the class label @xmath0, f, n, u } represents the four categories: t (true rumor), f (false rumor), n (non-rumor), and u (unverified rumor). the semantic content information v comprises a collection of event sentences, including source tweets and comments. for each event @xmath1, c 1 , c 2 , ..., c p } , where x represents the source tweet, c i represents the comment corresponding to the source tweet, and p is the maximum number of comments. to capture the semantic structure information, we construct the entity-sentence heterogeneous graph denoted as ## methodology we detail the overall framework of the proposed crowd intelligence and chatgpt-assisted network(cican), as shown in figure 1 . we elaborate on the main constituent modules of cican and its rumor detection process. ## conclusion and future work this study introduces a novel rumor detection model, the crowd intelligence and chatgpt-assisted network (cican), which combines se- figure 3 : early rumor detection mantic content and structure information for rumor detection. in the crowd intelligence-based semantic feature learning module, we employ contextual semantic encoders and multi-granularity feature extractors to capture the semantic content information. in the knowledge-based semantic structure mining module, to capture complex semantic structural information, we extract refined knowledge utilizing chatgpt to construct an entity-sentence heterogeneous graph and design entity-aware heterogeneous attention to integrate representations from different meta-paths effectively. experimental results validate the effectiveness of the cican model in leveraging crowd intelligence and external knowledge, resulting in significant performance improvements in rumor detection tasks. future research directions can further expand the field of rumor detection in several ways. first, take inspiration from truth-finding systems that leverage human intelligence to extract reliable information from multiple sources. second, to explore quantum cognition, using quantum theory to explain human behavior related to rumor detection. finally, the application of large models in rumor detection can be further mined to improve the performance and interpretability of the models.