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1. Demystify Epidemic Containment in Directed Networks: Theory and Algorithms

   https://doi.org/10.1145/3701551.3703575  

   He, Yinhan; Chen, Chen; Wang, Song; Min, Guanghui; Li, Jundong (March 2025, ACM)

   Epidemic containment has long been a crucial task in many high-stake application domains, ranging from public health to misinformation dissemination. Existing studies for epidemic containment are primarily focused on undirected networks, assuming that the infection rate is constant throughout the contact network regardless of the strength and direction of contact. However, such an assumption can be unrealistic given the asymmetric nature of the real-world infection process. To tackle the epidemic containment problem in directed networks, simply grafting the methods designed for undirected network can be problematic, as most of the existing methods rely on the orthogonality and Lipschitz continuity in the eigensystem of the underlying contact network, which do not hold for directed networks. In this work, we derive a theoretical analysis on the general epidemic threshold condition for directed networks and show that such threshold condition can be used as an optimization objective to control the spread of the disease. Based on the epidemic threshold, we propose an asymptotically greedy algorithm DINO (DIrected NetwOrk epidemic containment) to identify the most critical nodes for epidemic containment. The proposed algorithm is evaluated on real-world directed networks, and the results validate its effectiveness and efficiency. 

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2. A Multi-Feature Diffusion Model: Rumor Blocking in Social Networks

   https://doi.org/10.1109/TNET.2020.3032893  

   Guo, Jianxiong; Chen, Tiantian; Wu, Weili (February 2021, IEEE/ACM Transactions on Networking)

   null (Ed.)

   Online social networks provide a convenient platform for the spread of rumors, which could lead to serious aftermaths such as economic losses and public panic. The classical rumor blocking problem aims to launch a set of nodes as a positive cascade to compete with misinformation in order to limit the spread of rumors. However, most of the related researches were based on a one-dimensional diffusion model. In reality, there is more than one feature associated with an object. A user’s impression on this object is determined not just by one feature but by her overall evaluation of all features associated with it. Thus, the influence spread of this object can be decomposed into the spread of multiple features. Based on that, we design a multi-feature diffusion model (MF-model) in this paper and formulate a multi-feature rumor blocking (MFRB) problem on a multi-layer network structure according to this model. To solve the MFRB problem, we design a creative sampling method called Multi-Sampling, which can be applied to this multi-layer network structure. Then, we propose a Revised-IMM algorithm and obtain a satisfactory approximate solution to MFRB. Finally, we evaluate our proposed algorithm by conducting experiments on real datasets, which shows the effectiveness of our Revised- IMM and its advantage to their baseline algorithms. 

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3. Usable Cryptographic Provenance: A Proactive Complement to Fact-Checking for Mitigating Misinformation

   Sidnam-Mauch, E.; Ivancsics, B.; Monroe, A.; Washington, E.; Francis II, E.; Caine, K.; Bonneau, J.; McGregor, S.E. (January 2022, Proceedings of the International AAAI Conference on Weblogs and Social Media)

   This paper describes how cryptographic provenance can serve as a proactive, partial solution for mitigating misinformation. Drawing on literature from human-centered computing and usable security, journalism, and cryptography, we discuss the advantages and limitations of both content-based and technical approaches to the problem of online misinformation. We argue cryptographic provenance systems designed for usability can reduce the spread of misinformation by surfacing provenance information and making this information salient and acceptable to information consumers. We highlight challenges and open research areas related to designing usable cryptographic provenance systems, specifically concerning two key stakeholder groups: journalists and news consumers. 

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4. Usable Cryptographic Provenance: A Proactive Complement to Fact-Checking for Mitigating Misinformation

   Sidnam-Mauch, E.; Ivancsics, B.; Monroe, A.; Washington, E.; Francis II, E.; Caine, K.; Bonneau, J.; McGregor, S.E. (June 2022, Proceedings of the International AAAI Conference on Weblogs and Social Media)

   This paper describes how cryptographic provenance can serve as a proactive, partial solution for mitigating misinformation. Drawing on literature from human-centered computing and usable security, journalism, and cryptography, we discuss the advantages and limitations of both content-based and technical approaches to the problem of online misinformation. We argue cryptographic provenance systems designed for usability can reduce the spread of misinformation by surfacing provenance information and making this information salient and acceptable to information consumers. We highlight challenges and open research areas related to designing usable cryptographic provenance systems, specifically concerning two key stakeholder groups: journalists and news consumers. 

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5. Examining the impact of sharing COVID-19 misinformation online on mental health

   https://doi.org/10.1038/s41598-022-11488-y  

   Verma, Gaurav; Bhardwaj, Ankur; Aledavood, Talayeh; De Choudhury, Munmun; Kumar, Srijan (December 2022, Scientific Reports)

   Abstract Misinformation about the COVID-19 pandemic proliferated widely on social media platforms during the course of the health crisis. Experts have speculated that consuming misinformation online can potentially worsen the mental health of individuals, by causing heightened anxiety, stress, and even suicidal ideation. The present study aims to quantify the causal relationship between sharing misinformation, a strong indicator of consuming misinformation, and experiencing exacerbated anxiety. We conduct a large-scale observational study spanning over 80 million Twitter posts made by 76,985 Twitter users during an 18.5 month period. The results from this study demonstrate that users who shared COVID-19 misinformation experienced approximately two times additional increase in anxiety when compared to similar users who did not share misinformation. Socio-demographic analysis reveals that women, racial minorities, and individuals with lower levels of education in the United States experienced a disproportionately higher increase in anxiety when compared to the other users. These findings shed light on the mental health costs of consuming online misinformation. The work bears practical implications for social media platforms in curbing the adverse psychological impacts of misinformation, while also upholding the ethos of an online public sphere. 

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