Temporal Pattern of Retweet(s) Help to Maximize Information Diffusion in Twitter

Bhowmick, Ayan Kumar

doi:10.1145/3336191.3372181

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Data-driven Feature learning Captured the representative features to reveal important factors influencing information diffusion [33][34][35] Deep learning Improved prediction accuracy with neural networks [36][37][38][39][40][41] (1) Time-series methods attempt to summarize the pattern of the data and use mathematical expressions to portray the diffusion process of the propagation model over time. Recently, more and more models have been proposed to adapt to the differences between social network and traditional contagion models.…”

Section: Time-series/data-driven Methods Contributions Referencementioning

confidence: 99%

“…(2) Data-driven methods seek to automatically capture one or multiple features from the data through machine learning to observe the information diffusion process. Common features include structural features [33], temporal features [34], and user features [35]. However, end-to-end models based on deep learning have shown strong performance and gradually supplanted traditional feature-based methods in modern technology.…”

Section: Time-series/data-driven Methods Contributions Referencementioning

confidence: 99%

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A Dynamic Emotional Propagation Model over Time for Competitive Environments

Chen,

Xu,

Cai

et al. 2023

Electronics

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Emotional propagation research aims to discover and show the laws of opinion evolution in social networks. The short-term observation of the emotional propagation process for a predetermined time window ignores situations in which users with different emotions compete over a long diffusion time. To that end, we propose a dynamic emotional propagation model based on an independent cascade. The proposed model is inspired by the interpretable factors of the reinforced Poisson process, portraying the “rich-get-richer” phenomenon within a social network. Specifically, we introduce a time-decay mechanism to illustrate the change in influence over time. Meanwhile, we propose an emotion-exciting mechanism allowing prior users to affect the emotions of subsequent users. Finally, we conduct experiments on an artificial network and two real-world datasets—Wiki, with 7194 nodes, and Bitcoin-OTC, with 5881 nodes—to verify the effectiveness of our proposed model. The proposed method improved the F1-score by 3.5% and decreased the MAPE by 0.059 on the Wiki dataset. And the F1-score improved by 0.4% and the MAPE decreased by 0.013 on the Bitcoin-OTC dataset. In addition, the experimental results indicate a phenomenon of emotions in social networks tending to converge under the influence of opinion leaders after a long enough time.

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Section: Time-series/data-driven Methods Contributions Referencementioning

confidence: 99%

Section: Time-series/data-driven Methods Contributions Referencementioning

confidence: 99%

A Dynamic Emotional Propagation Model over Time for Competitive Environments

Chen,

Xu,

Cai

et al. 2023

Electronics

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“…The study of information diffusion (Kim et al. , 2018; Bhowmick, 2020; Kumar et al. , 2021) has been investigated more on intra than on inter-social networks as it is easy to identify connections used to transport data on intra-social networks.…”

Section: Bunet Dataset Utilitymentioning

confidence: 99%

“…The study of information diffusion (Kim et al, 2018;Bhowmick, 2020;Kumar et al, 2021) has been investigated more on intra than on inter-social networks as it is easy to identify connections used to transport data on intra-social networks. In fact, OSNs have been exploited for spreading fake news and rumors (Zubiaga et al, 2018;Shelke and Attar, 2019;Shen et al, 2021).…”

Section: Cross-network Information Spreadmentioning

confidence: 99%

Cross-social networks analysis: building me-edge centered BUNet dataset based on implicit bridge users

Amara

Aouicha

2022

OIR

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PurposeThe intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios.Design/methodology/approachWe propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites.FindingsUsing the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection.Originality/valueThis paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.

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