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Cheng-Jun Wang

Publications and source records attributed to Cheng-Jun Wang.

9 recordsLinked to original sources
arXiv2026

TRGCN: A Hybrid Framework for Social Network Rumor Detection

Accurate and efficient rumor detection is critical for information governance, particularly in the context of the rapid spread of misinformation on social networks. Traditional rumor detection relied primarily on manual analysis. With the continuous advancement of technology, machine learning and deep learning approaches for rumor identification have gradually emerged and gained prominence. However, previous approaches often struggle to simultaneously capture both the sequential and the global structural relationships among topological nodes within a social network. To tackle this issue, we introduce a hybrid model for detecting rumors that integrates a Graph Convolutional Network (GCN) with a Transformer architecture, aiming to leverage the complementary strengths of structural and semantic feature extraction. Positional encoding helps preserve the sequential order of these nodes within the propagation structure. The use of Multi-head attention mechanisms enables the model to capture features across diverse representational subspaces, thereby enhancing both the richness and depth of text comprehension. This integration allows the framework to concurrently identify the key propagation network of rumors, the textual content, the long-range dependencies, and the sequence among propagation nodes. Experimental evaluations on publicly available datasets, including Twitter 15 and Twitter 16, demonstrate that our proposed fusion model significantly outperforms both standalone models and existing mainstream methods in terms of accuracy. These results validate the effectiveness and superiority of our approach for the rumor detection task.

cs.SI↗
arXiv2023

Breaking the Boundaries of Knowledge Space: Analyzing the Knowledge Spanning on the Q&A Website through Word Embeddings

The challenge of raising a creative question exists in recombining different categories of knowledge. However, the impact of recombination remains controversial. Drawing on the theories of knowledge recombination and category spanning, we propose that both the distance of knowledge spanning and the hierarchy of knowledge shape the appeal of questions. Using word embedding models and the data collected from a large online knowledge market (N = 463,545), we find that the impact of knowledge spanning on the appeal of questions is parabolic: the appeal of questions increases up to a threshold, after which point the positive effect reverses. However, the nonlinear influence of knowledge spanning is contingent upon the hierarchy of knowledge. The theoretical and practical implications of these findings for future research on knowledge recombination are discussed. We fill the research gap by conceptualizing question asking as knowledge spanning and highlighting the theoretical underpinnings of the knowledge hierarchy.

cs.SI↗
arXiv2023

Unpacking the Essential Tension of Knowledge Recombination: Analyzing the Impact of Knowledge Spanning on Citation Counts and Disruptive Innovation

Drawing on the theories of knowledge recombination, we aim to unpack the essential tension between tradition and innovation in scientific research. Using the American Physical Society data and computational methods, we analyze the impact of knowledge spanning on both citation counts and disruptive innovation. The findings show that knowledge spanning has a U-shaped impact on disruptive innovation. In contrast, there is an inverted U-shaped relationship between knowledge spanning and citation counts, and the inverted U-shaped effect is moderated by team size. This study contributes to the theories of knowledge recombination by suggesting that both intellectual conformism and knowledge recombination can lead to disruptive innovation. That is, when evaluating the quality of scientific research with disruptive innovation, the essential tension seems to disappear.

cs.SI↗
arXiv2020

The Geometry of Information Cocoon: Analyzing the Cultural Space with Word Embedding Models

Accompanied by the development of digital media, the threat of information cocoon has become a significant issue. However, little is known about the measure of information cocoon as a cultural space and its relationship with social class. This study addresses this problem by constructing the cultural space with word embedding models and random shuffling methods among three large-scale digital media use datasets. In the light of field theory of cultural production, we investigate the information cocoon effect on different social classes among 979 computer users, 100,000 smartphone users, and 159,373 mobile reading application users. Our analysis reveals that information cocoons widely exist in the daily use of digital media. Moreover, people of lower social class have a higher probability of getting stuck in the information cocoon filled with the entertainment content. In contrast, the people of higher social class have more capability to stride over the constraints of the information cocoon. The results suggest that the disadvantages for vulnerable groups in acquiring knowledge may further widen social inequality.

cs.CY↗
arXiv2017

Leveraging the Flow of Collective Attention for Computational Communication Research

Human attention becomes an increasingly important resource for our understanding or collective human behaviors in the age of information explosion. To better understand the flow of collective attention, we construct the attention flow network using anonymous smartphone data of 100,000 users in a major city of China. In the constructed network, nodes are websites visited by users, and links denote the switch of users between two websites. We quantify the flow of collective attention by computing the flow network statistics, such as flow impact, flow dissipation, and flow distance. The findings reveal a strong concentration and fragmentation of collective attention for smartphone users, while the duplication of attention cross websites proves to be unfounded in mobile using. We further confirmed the law of dissipation and the allowmetric scaling of flow impact. Surprisingly, there is a centralized flow structure, suggesting that the website with large traffic can easily control the circulated collective attention. Additionally, we find that flow network analysis can effectively explain the page views and sale volume of products. Finally, we discuss the benefits and limitations of using the flow network analysis for computational communication research.

cs.CY↗
arXiv2016

Tracing the Attention of Moving Citizens

With the widespread use of mobile computing devices in contemporary society, our trajectories in the physical space and virtual world are increasingly closely connected. Using the anonymous smartphone data of $1 \times 10^5$ users in 30 days, we constructed the mobility network and the attention network to study the correlations between online and offline human behaviours. In the mobility network, nodes are physical locations and edges represent the movements between locations, and in the attention network, nodes are websites and edges represent the switch of users between websites. We apply the box-covering method to renormalise the networks. The investigated network properties include the size of box $l_B$ and the number of boxes $N(l_B)$. We find two universal classes of behaviours: the mobility network is featured by a small-world property, $N(l_B) \simeq e^{-l_B}$, whereas the attention network is characterised by a self-similar property $N(l_B) \simeq l_B^{-γ}$. In particular, with the increasing of the length of box $l_B$, the degree correlation of the network changes from positive to negative which indicates that there are two layers of structure in the mobility network. We use the results of network renormalisation to detect the community and map the structure of the mobility network. Further, we located the most relevant websites visited in these communities, and identified three typical location-based behaviours, including the shopping, dating, and taxi-calling. Finally, we offered a revised geometric network model to explain our findings in the perspective of spatial-constrained attachment.

cs.SI↗
arXiv2015

The Hidden Geometry of Attention Diffusion

We propose a geometric model to quantify the dynamics of attention in online communities. Using clicks as a proxy of attention, we find that the diffusion of collective attention in Web forums and news sharing sites forms time-invariant "fields" whose density vary solely with distance from the center of the fields that represents the input of attention from the physical world. As time goes by, old information pieces are pushed farther from the center by new pieces, receive fewer and fewer clicks, and eventually become invisible in the virtual world. The discovered "attention fields" not only explain the fast decay of attention to information pieces, but also predict the accelerating growth of clicks against the active user population, which is a universal pattern relevant to the economics of scales of online interactions.

physics.soc-ph↗
arXiv2013

Bringing Reference Groups Back: Agent-based Modeling of the Spiral of Silence

The purpose of this study is threefold: first, to bring reference groups back into the framework of spiral of silence (SOS) by proposing an extended framework of dual opinion climate; second, to investigate the boundary conditions of SOS; third, to identify the characteristics of SOS in terms of spatial variation and temporal evolution. Modeling SOS with agent-based models, the findings suggest (1) there is no guarantee of SOS with reference groups being brought back; (2) Stable existence of SOS is contingent upon the comparative strength of mass media over reference groups; (3) SOS is size-dependent upon reference groups and the population; (4) the growth rate of SOS decreases over time. Thus, this research presents an extension of the SOS theory.

cs.CY↗