Predicting the Times of Retweeting in Microblogs

Kuang, Li; Tang, Xiang; Guo, Ke

doi:10.1155/2014/604294

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…This measures the time it took for the author of the current tweet to retweet the originating message. This is similar to Kuang et al [48] who defined response time of the retweet to be the time difference between the time of the first retweet and that of the origin tweet. Further, Spiro et al [45] calls these "waiting times".…”

Section: Time_to_rt = Rt_object − Tw_objectmentioning

confidence: 64%

Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs

Ordun¹,

Purushotham²,

Raff³

2020

Preprint

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This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.

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Section: Time_to_rt = Rt_object − Tw_objectmentioning

confidence: 64%

Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs

Ordun¹,

Purushotham²,

Raff³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…It is the procedure for converting words into vectors. The bag of words model [ 30 ] or the TF-IDF model [ 31 ] can be used for vectorization. The TF-IDF model was used to convert words into vectors in this case.…”

Section: Proposed Information Diffusion and Opinion Evolution Predict...mentioning

confidence: 99%

Predicting opinion evolution based on information diffusion in social networks using a hybrid fuzzy based approach

Uthirapathy

Domnic

2022

Int. j. inf. tecnol.

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Social media plays an important role in disseminating information and analysing public and government opinions. The vast majority of previous research has examined information diffusion and opinion analysis separately. This study proposes a new framework for analysing both information diffusion and opinion evolution. The change in opinion over time is known as opinion evolution. To propose a new model for predicting information diffusion and opinion analysis in social media, a forest fire algorithm, cuckoo search, and fuzzy c-means clustering are used. The forest fire algorithm is used to determine the diffuser and non-diffuser of information in social networks, and fuzzy c-means clustering with the cuckoo search optimization algorithm is proposed to cluster Twitter content into various opinion categories and to determine opinion change. On different Twitter data sets, the proposed model outperformed the existing methods in terms of precision, recall, and accuracy.

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“…To properly understand how information flows in online social networks, we first need to calculate the probability by which any user will retweet since retweeters (people who re-tweet or re-post another person's tweet) play a significant role in disseminating the information over the entire network. Several works have been done in past as well to study the retweet phenomenon (Kuang et al 2014;Nesi et al 2018). We analyzed some important features of users listed below which can help us in answering that with what probability a particular user may retweet a particular tweet.…”

Section: Probability Of An Existing User Retweeting a Tweet (R U )mentioning

confidence: 99%

Modeling information diffusion in online social networks using a modified forest-fire model

et al. 2020

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Information dissemination has changed rapidly in recent years with the emergence of social media which provides online platforms for people worldwide to share their thoughts, activities, emotions, and build social relationships. Hence, modeling information diffusion has become an important area of research in the field of network analysis. It involves the mathematical modeling of the movement of information and study the information spread pattern. In this paper, we attempt to model information propagation in online social networks using a nature-inspired approach based on a modified forest-fire model. A slight spark can start a wildfire in a forest, and the spread of this fire depends on vegetation, weather, and topography, which may act as fuel. On similar lines, we labeled users who haven't joined the network yet as Empty, existing users as T ree, and information as F ire. The spread of information across online social networks depends upon users-followers relationships, the significance of the topic, and other such features. We introduce a novel Burnt state to the traditional forest-fire model to represent non-spreaders in the network. We validate our method on six real-world data-sets extracted from Twitter and conclude that the proposed model performs reasonably well in predicting information diffusion.

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Predicting the Times of Retweeting in Microblogs

Cited by 6 publications

References 12 publications

Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs

Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs

Predicting opinion evolution based on information diffusion in social networks using a hybrid fuzzy based approach

Modeling information diffusion in online social networks using a modified forest-fire model

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