We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.

/pdf/the-covid-19-social-media-infodemic-7p7dgg6vb1.pdf

The COVID-19 social media infodemic

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction numbers $R_0$ for each social media platform. Moreover, we characterize information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.

/pdf/the-covid-19-social-media-infodemic-4a8ytom0wb.pdf

The COVID-19 Social Media Infodemic

Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.

Leveraging Joint Interactions for Credibility Analysis in News Communities with Continuous Conditional Random Field

The goal of a group recommendation involves providing appropriate information for all members in a group. Most extant studies use aggregation methods to determine group preferences. An aggregation method is an approach that aggregates individual preferences of group members to recommend items to a group. Previous studies on aggregation methods only consider high averages, counts, and rankings to provide recommendations. However, the most important component of a group recommendation involves ensuring that majority of the members in a group are satisfied with the recommended results. Therefore, it is necessary to consider the deviation as an important element in aggregation methods. The present study involves proposing an upward leveling (UL) aggregation method that considers deviations for group recommendations. The UL recommends items with low deviations and high averages in conjunction with frequency of positive rating counts for group members. Furthermore, the effectiveness of the UL is validated to perform a comparative evaluation with existing aggregation methods by using the normalized discounted cumulative gain (NDCG) and diversity. The results indicate that the UL outperforms all the baselines and that the deviation plays an important role in the aggregation method.

An enhanced aggregation method considering deviations for a group recommendation

The proliferation of fake news on social media has the probability to bring an unfavorable impact on public opinion and social development. Many efforts have been paid to develop effective detection and intervention algorithms in recent years. Most of the existing propagation-based fake news detection methods focus on static networks and assume the whole information propagation network structure is accessible before performing learning algorithms. However, in real-world information diffusion networks, new nodes and edges constantly emerge. Therefore, in this paper, we introduce a novel temporal propagation-based fake news detection framework, which could fuse structure, content semantics, and temporal information. In particular, our model can model temporal evolution patterns of real-world news as the graph evolving under the setting of continuous-time dynamic diffusion networks. We conduct extensive experiments on large-scale real-world datasets and the experimental results demonstrate that our proposed model outperforms state-of-the-art fake news detection methods.

Temporally evolving graph neural network for fake news detection

In recent years, social media has increasingly become one of the popular ways for people to consume news. As proliferation of fake news on social media has the negative impacts on individuals and society, automatic fake news detection has been explored by different research communities for combating fake news. With the development of multimedia technology, there is a phenomenon that cannot be ignored is that more and more social media news contains information with different modalities, e.g., texts, pictures and videos. The multiple information modalities show more evidence of the happening of news events and present new opportunities to detect features in fake news. First, for multimodal fake news detection task, it is a challenge of keeping the unique properties for each modality while fusing the relevant information between different modalities. Second, for some news, the information fusion between different modalities may produce the noise information which affects model’s performance. Unfortunately, existing methods fail to handle these challenges. To address these problems, we propose a multimodal fake news detection framework based on Crossmodal Attention Residual and Multichannel convolutional neural Networks (CARMN). The Crossmodal Attention Residual Network (CARN) can selectively extract the relevant information related to a target modality from another source modality while maintaining the unique information of the target modality. The Multichannel Convolutional neural Network (MCN) can mitigate the influence of noise information which may be generated by crossmodal fusion component by extracting textual feature representation from original and fused textual information simultaneously. We conduct extensive experiments on four real-world datasets and demonstrate that the proposed model outperforms the state-of-the-art methods and learns more discriminable feature representations.

A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks

As the microblogging services are becoming more prosperous in everyday life for users on Online Social Networks (OSNs), it is more favorable for hot topics and breaking news to gain more attraction very soon than ever before, which are so-called "super-spreading events". In the information diffusion process of these super-spreading events, messages are passed on from one user to another and numerous individuals are influenced by a relatively small portion of users, a.k.a. super-spreaders. Acquiring an awareness of super-spreading phenomena and an understanding of patterns of wide-ranged information propagations benefits several social media data mining tasks, such as hot topic detection, predictions of information propagation, harmful information monitoring and intervention. Taking into account that super-spreading in both information diffusion and spread of a contagious disease are analogous, in this study, we build a parameterized model, the SAIR model, based on well-known epidemic models to characterize super-spreading phenomenon in tweet information propagation accompanied with super-spreaders. For the purpose of modeling information diffusion, empirical observations on a real-world Weibo dataset are statistically carried out. Both the steady-state analysis on the equilibrium and the validation on real-world Weibo dataset of the proposed model are conducted. The case study that validates the proposed model shows that the SAIR model is much more promising than the conventional SIR model in characterizing a super-spreading event of information propagation. In addition, numerical simulations are carried out and discussed to discover how sensitively the parameters affect the information propagation process.

Characterizing super-spreading in microblog: An epidemic-based information propagation model

Local Community Detection Based on Network Motifs

Recently, group recommendation becomes substantially significant when it frequently happens that a group of users need to determine which item (e.g. movie, music, restaurant, etc.) to choose. In this paper we employ the information of friend network to propose a Community-Oriented Group Recommendation framework (CoGrec) consisting of non-negative matrix factorization based user profile generation, community detection based group identification, and overlapping community membership based group decision. Along with four inherent aggregation and allocation strategies, our proposed framework is evaluated through extensive experiments on real-world datasets. The experimental results show that the proposed framework is promising and more accurate when the given friend network is much denser, which is suitable for modern review and rating systems.

Yunlei Zhang

Papers

A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks

Characterizing super-spreading in microblog: An epidemic-based information propagation model

Local Community Detection Based on Network Motifs

CoGrec: A Community-Oriented Group Recommendation Framework

Knowledge augmented transformer for adversarial multidomain multiclassification multimodal fake news detection