Network Redundancy and Information Diffusion: The Impacts of Information Redundancy, Similarity, and Tie Strength

Abstract

It remains controversial whether community structures in social networks are beneficial or not for information diffusion. This study examined the relationships among four core concepts in social network analysis—network redundancy, information redundancy, ego-alter similarity, and tie strength—and their impacts on information diffusion. By using more than 6,500 representative ego networks containing nearly 1 million following relationships from Twitter, the current study found that (1) network redundancy is positively associated with the probability of being retweeted even when competing variables are controlled for; (2) network redundancy is positively associated with information redundancy, which in turn decreases the probability of being retweeted; and (3) the inclusion of both ego-alter similarity and tie strength can attenuate the impact of network redundancy on the probability of being retweeted.

Sampling Twitter users for social science research: evidence from a systematic review of the literature

“…In the remaining studies, which used a list to represent the target population, four different sampling frames can be distinguished: • Random Generated ID numbers 4 studies adopted this kind of sampling frame, which consisted of creating a "list of Twitter users' IDs" using a software system that randomly generates numbers, within (or not) a pre-specified range of numbers (e.g., Liang and Fu 2019). This process resembles Random Digit Dialling in telephone surveys (Waksberg 1978) and allows everyone with an active Twitter account to be represented, Fig.…”

“…Table 7 presents the citations of the explanations given in the 9 studies that highlight the limitation that the outcomes cannot be generalizable beyond the Twitter network because Twitter users may differ from users of other platforms. The characteristics of Twitter users and the specificities of the Twitter platform are likely explanations for this issue in 4 studies (Gómez-Zará and Diakopoulos 2020; Liang and Fu 2019;Liang and Shen 2018;Tominaga et al 2018). Three studies postpone investigating whether the findings are valid in other social media platforms for future research (Schaarschmidt and Könsgen 2020;Tominaga et al 2018;Vaccari et al 2015).…”

“…(…) Our results may have been different considering other social media platforms and audiences' demographics. Liang and Fu (2019) Twitter is a social media platform focusing on information sharing. However, there are platforms emphasizing social networking functions, like Facebook.…”

From Organizational-Centric Engagement to Network-Centric Engagement: The Role of Autonomous Public Communities in a Mediated Public Policy Advocacy Network

“…Communication scholars now regularly apply social network analysis to examine social-mediated communities on Twitter and other social media platforms (Barisione et al, 2019; Lai et al, 2019; Liang & Fu, 2019). Networks are often porous, volatile, and unstable (Lai et al, 2019) and studies have generally found that the networks in these voluntarily formed communities are not necessarily horizontal and decentralized.…”

Introducing computational social science for Asia-Pacific communication research

“…For example, text mining and machine learning have been used to classify communication documents into different categories, whose rationale is similar to the traditional content analysis. Beyond classification, some innovative measures could be developed, such as information similarity and redundancy , 2019. Moreover, social network analysis provides a set of network metrics, such as centrality, clustering, modularity, for communication studies.…”