Influence of network density on information diffusion on social network sites

Luarn, Pin; Chiu, Yu Ping

doi:10.1177/0266666914551072

Cited by 19 publications

(22 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to communication and interaction on personal networks, SNSs have also become a new hybrid component of integrated marketing communication that allows organisations to establish strong relationships with their consumers Luarn and Chiu, 2014;Mangold and Faulds, 2009). The role of SNSs as a communication tool for marketing has received considerable attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Motivations to engage in word-of-mouth behavior on social network sites

Luarn

Huang

Chiu

et al. 2016

Information Development

Self Cite

View full text Add to dashboard Cite

The main objective of this study was to enhance the understanding of users' motivation to engage in word-ofmouth behavior on social network sites. This study conducted a hierarchical regression analysis to examine the motivations of social and personal constructs that may influence users' engagement in word-of-mouth. The results posited that not only social constructs (tie strength, expressiveness, social enhancement, relationship management, normative influence, and informational influence), but also personal constructs (altruism, narcissism, image building, and achievement) positively affect users' engagement in word-of-mouth. These findings can provide useful implications for theory in relation to the motives to disseminate word-of-mouth, and be a useful resource and reference for marketing practice.

show abstract

Section: Introductionmentioning

confidence: 99%

Motivations to engage in word-of-mouth behavior on social network sites

Luarn

Huang

Chiu

et al. 2016

Information Development

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, [33] showed that network density is positively related to information diffusion. Twitter network has the highest density which enhances information spread while Reddit has the lowest density.…”

Section: Plos Onementioning

confidence: 98%

“…Below we outline network topology measurements that are know to effect information spread in social networks [28,29,31,32,33,30,34,35,14].…”

Section: Network Measurementsmentioning

confidence: 99%

Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit

et al. 2020

View full text Add to dashboard Cite

The awareness about software vulnerabilities is crucial to ensure effective cybersecurity practices, the development of high-quality software, and, ultimately, national security. This awareness can be better understood by studying the spread, structure and evolution of software vulnerability discussions across online communities. This work is the first to evaluate and contrast how discussions about software vulnerabilities spread on three social platforms-Twitter, GitHub, and Reddit. Moreover, we measure how user-level e.g., bot or not, and content-level characteristics e.g., vulnerability severity, post subjectivity, targeted operating systems as well as social network topology influence the rate of vulnerability discussion spread. To lay the groundwork, we present a novel fundamental framework for measuring information spread in multiple social platforms that identifies spread mechanisms and observables, units of information, and groups of measurements. We then contrast topologies for three social networks and analyze the effect of the network structure on the way discussions about vulnerabilities spread. We measure the scale and speed of the discussion spread to understand how far and how wide they go, how many users participate, and the duration of their spread. To demonstrate the awareness of more impactful vulnerabilities, a subset of our analysis focuses on vulnerabilities targeted during recent major cyber-attacks and those exploited by advanced persistent threat groups. One of our major findings is that most discussions start on GitHub not only before Twitter and Reddit, but even before a vulnerability is officially published. The severity of a vulnerability contributes to how much it spreads, especially on Twitter. Highly severe vulnerabilities have significantly deeper, broader and more viral discussion threads. When analyzing vulnerabilities in software products we found that different flavors of Linux received the highest discussion volume. We also observe that Twitter discussions started by humans have larger size, breadth, depth, adoption rate, lifetime, and structural virality compared to those started by bots. On Reddit, discussion threads of positive posts are larger, wider, and deeper than negative or neutral posts. We also found that all three networks have high modularity that encourages spread. However, the spread on GitHub is different from other networks, because GitHub is

show abstract

“…To be sure, there is plenty of evidence to suggest that citation counts reflect something other than quality (Aksnes, Langfeldt, & Wouters, 2019;Bornmann & Daniel, 2008). For instance, citation counts covary with factors such as length of the title and the presence of colons or hyphens in the title (Paiva, Lima, & Paiva, 2012;Zhou, Tse, & Witheridge, 2019) and can be affected by other non-scientific variables, such as the size of ones social network (Mählck & Persson, 2000), the use of social media, and the posting of preprints on searchable archives (Gargouri et al, 2010;Gentil-Beccot, Mele, & Brooks, 2009;Lawrence, 2001;Luarn & Chiu, 2016;Peoples, Midway, Sackett, Lynch, & Cooney, 2016). Other research indicates that citation counts tend to be higher for papers on so-called "hot" topics and for researchers associated with big-named faculty (Petersen et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Citation counts and journal impact factors do not capture some indicators research quality in the behavioral and brain sciences

Dougherty¹,

Horne²

2019

Preprint

View full text Add to dashboard Cite

Citation data and journal impact factors are important components of faculty dossiers and figure prominently in both promotion decisions and assessments of a researcher's broader societal impact. Although these metrics play a large role in high-stakes decisions, the evidence is mixed regarding whether they are valid proxies for key aspects of research quality. We use data from three large scale studies to assess whether citation counts and impact factors predict four indicators of aspects of research quality: (1) the number of statistical reporting errors in a paper, (2) the evidential value of the reported data, (3) the expected replicability of reported research findings in peer reviewed journals, and (4) the actual replicability of a given experimental result. Both citation counts and impact factors were weak and inconsistent predictors of research quality, so defined, and sometimes negatively related to quality. Our findings impugn the validity of citation data and impact factors as indices of research quality and call into question their usefulness in evaluating scientists and their research. In light of these results, we argue that research evaluation should instead focus on the process of how research is conducted and incentivize behaviors that support open, transparent, and reproducible research.

show abstract

Influence of network density on information diffusion on social network sites

Cited by 19 publications

References 36 publications

Motivations to engage in word-of-mouth behavior on social network sites

Motivations to engage in word-of-mouth behavior on social network sites

Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit

Citation counts and journal impact factors do not capture some indicators research quality in the behavioral and brain sciences

Contact Info

Product

Resources

About