RetweetPatterns: Detection of Spatio-Temporal Patterns of Retweets

Rodrigues, Tomy; Cunha, Tiago; Ienco, Dino; Poncelet, Pascal; Soares, Carlos

doi:10.1007/978-3-319-31232-3_83

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other works proposed the use of additional features, such as users' metadata (e.g., number of followers, number of friends, status count, etc.) [19,20], text and topic similarity features [13], location information [23,31] and tweeting behavior (e.g., incidence of tweets or retweets in the user's activity) [7]. Only two studies [24,31], as far as we know, integrated in the retweeting dynamic analysis the impacts of social relationships measured as the intensity of the interactions between two users.…”

Section: Related Workmentioning

confidence: 99%

Interaction Strength Analysis to Model Retweet Cascade Graphs

et al. 2020

View full text Add to dashboard Cite

Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph that is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users’ interactions. Initial findings showed the soundness of the approaches based on this new metric with respect to the state-of-the-art model, and its ability to generate a denser graph, revealing crucial nodes that participated in the retweet propagation. Reliable retweet graph generation will enable a better understanding of the diffusion path of a specific tweet.

show abstract

Section: Related Workmentioning

confidence: 99%

Interaction Strength Analysis to Model Retweet Cascade Graphs

et al. 2020

View full text Add to dashboard Cite

show abstract

Tracking social provenance in chains of retweets

et al. 2023

View full text Add to dashboard Cite

In the era of massive sharing of information, the term social provenance is used to denote the ownership, source or origin of a piece of information which has been propagated through social media. Tracking the provenance of information is becoming increasingly important as social platforms acquire more relevance as source of news. In this scenario, Twitter is considered one of the most important social networks for information sharing and dissemination which can be accelerated through the use of retweets and quotes. However, the Twitter API does not provide a complete tracking of the retweet chains, since only the connection between a retweet and the original post is stored, while all the intermediate connections are lost. This can limit the ability to track the diffusion of information as well as the estimation of the importance of specific users, who can rapidly become influencers, in the news dissemination. This paper proposes an innovative approach for rebuilding the possible chains of retweets and also providing an estimation of the contributions given by each user in the information spread. For this purpose, we define the concept of Provenance Constraint Network and a modified version of the Path Consistency Algorithm. An application of the proposed technique to a real-world dataset is presented at the end of the paper.

show abstract

RetweetPatterns: Detection of Spatio-Temporal Patterns of Retweets

Cited by 2 publications

References 6 publications

Interaction Strength Analysis to Model Retweet Cascade Graphs

Interaction Strength Analysis to Model Retweet Cascade Graphs

Tracking social provenance in chains of retweets

Contact Info

Product

Resources

About