Mining network-level properties of Twitter altmetrics data

Said, Anwar; Bowman, Timothy D.; Abbasi, Rabeeh Ayaz; Aljohani, Naif Radi; Hassan, Saeed‐Ul; Nawaz, Raheel

doi:10.1007/s11192-019-03112-0

Cited by 51 publications

(28 citation statements)

References 39 publications

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“…For PageRank, users have high PageRank scores if they receive comments from other users who also get comments from many others. These users are considered to be important nodes in their own network [ 47 ]. According to the results, four influential malicious accounts were ranked on the top 5 of all layers (i.e.,

, and

).…”

Section: Results and Discussionmentioning

confidence: 99%

Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach

Nguyen

Wang

Dai

et al. 2021

Sensors

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Online social media platforms play an important role in political communication where users can freely express and exchange their political opinion. Political entities have leveraged social media platforms as essential channels to disseminate information, interact with voters, and even influence public opinion. For this purpose, some organizations may create one or more accounts to join online political discussions. Using these accounts, they could promote candidates and attack competitors. To avoid such misleading speeches and improve the transparency of the online society, spotting such malicious accounts and understanding their behaviors are crucial issues. In this paper, we aim to use network-based analysis to sense influential human-operated malicious accounts who attempt to manipulate public opinion on political discussion forums. To this end, we collected the election-related articles and malicious accounts from the prominent Taiwan discussion forum spanning from 25 May 2018 to 11 January 2020 (the election day). We modeled the discussion network as a multilayer network and used various centrality measures to sense influential malicious accounts not only in a single-layer but also across different layers of the network. Moreover, community analysis was performed to discover prominent communities and their characteristics for each layer of the network. The results demonstrate that our proposed method can successfully identify several influential malicious accounts and prominent communities with apparent behavior differences from others.

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, and

).…”

Section: Results and Discussionmentioning

confidence: 99%

Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach

Nguyen

Wang

Dai

et al. 2021

Sensors

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“…ASN features attract different group dynamics. For example, Said et al (2019) examined the structure of scholarly communication in Twitter. The study collected 1.4 million tweets, corresponding to 77,757 scholarly articles.…”

Section: Asn Features and Services (Theme Iii)mentioning

confidence: 99%

The Use of Academic Social Networking Sites in Scholarly Communication: Scoping Review

Hailu

2021

Data and Information Management

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This research provides a systematic analysis of 115 previous literatures on the use of academic social networking sites (ASNs) in scholarly communication. Previous research on the subject has mainly taken a disciplinary and user perspective. This research conceptualizes the use of ASNs in scholarly communication in the space between social interactions and the technologies themselves. Keyword analysis and scoping review approaches have been used to analyze the comprehensive literature in the field. The study found a geographic variation in what motivates academics to use ASNs. Scholar discovery and sharing are the primary driving factors identified in the literature. Four main themes within the research literature are proposed: motivation and uses, impact assessment, features and services, and scholarly big data. The study found that there has been an increase in scholarly big data research in recent years. The paper also discusses the key findings and concepts stated in each theme. This gives academics a better understanding of what ASNs can do and their weaknesses, and identifies gaps in the literature that are worth addressing in future investigations. We suggest that future studies may also extend the existing theoretical framework and epistemological approaches to better predict and clarify the socio-technical dimensions of ASNs use in scholarly communication. In addition, this study has implications for academic and research institutions, libraries and information literacy programs, and future studies on the topic.

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“…This may be particularly true of publications in biomedical sciences, which attain greater Twitter interest than those in other NOMF003 EMPIRE Index development 6 April 2020 31 scholarly disciplines [52]. Twitter communities linked through publication tweets tend to be led by organizational accounts associated with well-known journals or leading scholars [53], although at least half of sharing on social media is likely to be non-academic [54,55].…”

Section: Rationale For the Three Component Scoresmentioning

confidence: 99%

Introducing the EMPIRE Index: A novel, value-based metric framework to measure the impact of medical publications

Pal

Rees

2021

Preprint

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Article-level measures of publication impact (alternative metrics or altmetrics) can help authors and other stakeholders assess engagement with their research and the success of their communication efforts. The wide variety of altmetrics can make interpretation and comparative assessment difficult; available summary tools are either narrowly focused or do not reflect the differing values of metrics from a stakeholder perspective. We created the EMPIRE (EMpirical Publication Impact and Reach Evaluation) Index, a value-based, multi-component metric framework for medical publications. Metric weighting and grouping were informed by a statistical analysis of 2891 Phase III clinical trial publications and by a panel of stakeholders who provided value assessments. The EMPIRE Index comprises three component scores (social, scholarly, and societal impact), each incorporating related altmetrics indicating a different aspect of engagement with the publication. These are averaged to provide a total impact score and benchmarked so that a score of 100 equals the mean scores of Phase III clinical trial publications in the New England Journal of Medicine (NEJM) in 2016. Predictor metrics are defined to estimate likely long-term impact. The social impact component correlated strongly with the Altmetric Attention Score and the scholarly impact component correlated modestly with CiteScore, with the societal impact component providing unique insights. Analysis of fresh metrics collected 1 year after the initial dataset, including an independent sample, showed that scholarly and societal impact scores continued to increase, whereas social impact scores did not. Analysis of NEJM notable articles showed that observational studies had the highest total impact and component scores, except for societal impact, for which surgical studies had the highest score. The EMPIRE Index provides a richer assessment of publication value than standalone traditional and alternative metrics and may enable medical researchers to assess the impact of publications easily and to understand what characterizes impactful research.

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Mining network-level properties of Twitter altmetrics data

Cited by 51 publications

References 39 publications

Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach

Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach

The Use of Academic Social Networking Sites in Scholarly Communication: Scoping Review

Introducing the EMPIRE Index: A novel, value-based metric framework to measure the impact of medical publications

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