Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

Senevirathna, Chathurani; Gunaratne, Chathika; Rand, William; Jayalath, Chathura; Garibay, Iván

doi:10.3390/e23020160

Cited by 6 publications

(2 citation statements)

References 49 publications

(52 reference statements)

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“…Recently, Chathurani et al. [ 28 ] put together these two aspects, investigating several influence patterns exercised by different users within and across online social media platforms.…”

Section: Related Workmentioning

confidence: 99%

Social Influence Maximization in Hypergraphs

Antelmi

Cordasco

Spagnuolo

et al. 2021

Entropy

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This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H=(V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S⊆V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes; (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks.

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“…Recently, Chathurani et al. [ 28 ] put together these two aspects, investigating several influence patterns exercised by different users within and across online social media platforms.…”

Section: Related Workmentioning

confidence: 99%

Social Influence Maximization in Hypergraphs

Antelmi

Cordasco

Spagnuolo

et al. 2021

Entropy

View full text Add to dashboard Cite

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“…Tweeting and retweeting are the most frequent components of user behaviors on Weibo [36,37], and some scholars emphasized that content-specific attributes such as content links can also affect popularity [1,38]. On the basis of a well-established grouping system of users and domains, here the behavioral differences between the mass and elites are comprehensively probed from the perspectives of activity, homophily, loyalty and content characteristics.…”

Section: Introductionmentioning

confidence: 99%

Behavior Variations and Their Implications for Popularity Promotions: From Elites to Mass on Weibo

Shi

Zhao

2022

Entropy

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The boom in social media with regard to producing and consuming information simultaneously implies the crucial role of online user influence in determining content popularity. In particular, understanding behavior variations between the influential elites and the mass grassroots is an important issue in communication. However, how their behavior varies across user categories and content domains and how these differences influence content popularity are rarely addressed. From a novel view of seven content domains, a detailed picture of the behavior variations among five user groups, from the views of both the elites and mass, is drawn on Weibo, one of the most popular Twitter-like services in China. Interestingly, elites post more diverse content with video links, while the mass possess retweeters of higher loyalty. According to these variations, user-oriented actions for enhancing content popularity are discussed and testified. The most surprising finding is that the diverse content does not always bring more retweets, and the mass and elites should promote content popularity by increasing their retweeter counts and loyalty, respectively. For the first time, our results demonstrate the possibility of highly individualized strategies of popularity promotions in social media, instead of a universal principle.

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Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media

Gunaratne

Thakur

et al. 2022

Lecture Notes in Computer Science

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Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

Cited by 6 publications

References 49 publications

Social Influence Maximization in Hypergraphs

Social Influence Maximization in Hypergraphs

Behavior Variations and Their Implications for Popularity Promotions: From Elites to Mass on Weibo

Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media

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