Degree centrality and eigenvector centrality in twitter

Maharani, Warih; Adiwijaya, Adiwijaya; Gozali, Alfian Akbar

doi:10.1109/tssa.2014.7065911

Cited by 57 publications

(31 citation statements)

References 9 publications

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“…Symplur is a health care social media analytics company that created the HSG database holding public digital content (ie, conversations, interactions) originating from Twitter and obtained via the official Twitter application programming interface (API) while supplementing it with other public content from social media platforms including LinkedIn, YouTube, Instagram, Reddit, and Facebook. The HSG score was developed by Symplur to identify and rank influencers in any health care topic and is conceptually like an eigenvector [ 16 ]. This score ranks Twitter accounts by their global conversational impact in healthcare over the last 52 weeks.…”

Section: Methodsmentioning

confidence: 99%

Evaluating Scholars’ Impact and Influence: Cross-sectional Study of the Correlation Between a Novel Social Media–Based Score and an Author-Level Citation Metric

Silva¹,

Maldonado²,

Brigham³

et al. 2021

J Med Internet Res

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Background The development of an author-level complementary metric could play a role in the process of academic promotion through objective evaluation of scholars’ influence and impact. Objective The objective of this study was to evaluate the correlation between the Healthcare Social Graph (HSG) score, a novel social media influence and impact metric, and the h-index, a traditional author-level metric. Methods This was a cross-sectional study of health care stakeholders with a social media presence randomly sampled from the Symplur database in May 2020. We performed stratified random sampling to obtain a representative sample with all strata of HSG scores. We manually queried the h-index in two reference-based databases (Scopus and Google Scholar). Continuous features (HSG score and h-index) from the included profiles were summarized as the median and IQR. We calculated the Spearman correlation coefficients (ρ) to evaluate the correlation between the HSG scores and h-indexes obtained from Google Scholar and Scopus. Results A total of 286 (31.2%) of the 917 stakeholders had a Google Scholar h-index available. The median HSG score for these profiles was 61.1 (IQR 48.2), and the median h-index was 14.5 (IQR 26.0). For the 286 subjects with the HSG score and Google Scholar h-index available, the Spearman correlation coefficient ρ was 0.1979 (P<.001), indicating a weak positive correlation between these two metrics. A total of 715 (78%) of 917 stakeholders had a Scopus h-index available. The median HSG score for these profiles was 57.6 (IQR 46.4), and the median h-index was 7 (IQR 16). For the 715 subjects with the HSG score and Scopus h-index available, ρ was 0.2173 (P<.001), also indicating a weak positive correlation. Conclusions We found a weak positive correlation between a novel author-level complementary metric and the h-index. More than a chiasm between traditional citation metrics and novel social media–based metrics, our findings point toward a bridge between the two domains.

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Section: Methodsmentioning

confidence: 99%

Evaluating Scholars’ Impact and Influence: Cross-sectional Study of the Correlation Between a Novel Social Media–Based Score and an Author-Level Citation Metric

Silva¹,

Maldonado²,

Brigham³

et al. 2021

J Med Internet Res

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“…Eigenvector centrality measures the "centrality," or approximate importance, of each vertex in a graph. The assumption is that each vertex's centrality is the sum of the centrality values of the vertices to which it is connected (Knoke & Yang, 2010;Maharani, Adiwijaya, & Gozali, 2015). The PageRank algorithm used by Google's search engine is a variant of Eigenvector centrality.…”

Section: Centralitymentioning

confidence: 99%

Emergency information diffusion on online social media during storm Cindy in U.S.

Kim

Bae

Hastak

2018

International Journal of Information Management

149

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“…Methods from social network analysis have been used widely in many areas of scientific research. They have been used to identify opinion leaders in social networks such as twitter [32], [33] using the individual users as vertices and the follower relationship as edges.…”

Section: A Applications Of Social Network Analysis In Communication mentioning

confidence: 99%

Computational Methods in Professional Communication

Valdez¹,

Clever²,

Assenmacher³

et al. 2019

2019 IEEE International Professional Communication Conference (ProComm)

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The digitization of the world has also led to a digitization of communication processes. Traditional research methods fall short in understanding communication in digital worlds as the scope has become too large in volume, variety, and velocity to be studied using traditional approaches. In this paper, we present computational methods and their use in public and mass communication research and how those could be adapted to professional communication research. The paper is a proposal for a panel in which the panelists, each an expert in their field, will present their current work using computational methods and will discuss transferability of these methods to professional communication.Index Terms-Computational methods, text mining, agent-based modeling, social network analysis, algorithmic bias.

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Degree centrality and eigenvector centrality in twitter

Cited by 57 publications

References 9 publications

Evaluating Scholars’ Impact and Influence: Cross-sectional Study of the Correlation Between a Novel Social Media–Based Score and an Author-Level Citation Metric

Evaluating Scholars’ Impact and Influence: Cross-sectional Study of the Correlation Between a Novel Social Media–Based Score and an Author-Level Citation Metric

Emergency information diffusion on online social media during storm Cindy in U.S.

Computational Methods in Professional Communication

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