The Market for Online Influence

Recent developments in social media have morphed the age-old practice of paying influential individuals for product endorsements into a multibillion dollar industry, extending well beyond celebrity sponsorships. We develop a parsimonious model in which influencers trade off the increased revenue they obtain from paid endorsements with the negative impact that these have on their followers’ engagement and, therefore, on the price influencers receive from marketers. The model provides testable predictions that match suggestive evidence on pricing of paid endorsements, reveals a novel type of inefficiency that emerges in this market, and clarifies the role of search technology and advice transparency in shaping market activity. In particular, we show that recent policies that make paid endorsements more transparent can backfire, whereas an increase in the effectiveness of the search technology that matches followers to influencers has both direct and strategic positive welfare effects. (JEL D83, L82, L86, M31.)

Influencers in design teams: a computational framework to study their impact on idea generation

It is known that wherever there is human interaction, there is social influence. Here, we refer to more influential individuals as “influencers”, who drive team processes for better or worst. Social influence gives rise to social learning, the propensity of humans to mimic the most influential individuals. As individual learning is affected by the presence of an influencer, so is an individual's idea generation . Examining this phenomenon through a series of human studies would require an enormous amount of time to study both individual and team behaviors that affect design outcomes. Hence, this paper provides an agent-based approach to study the effect of influencers during idea generation. This model is supported by the results of two empirical experiments which validate the assumptions and sustain the logic implemented in the model. The results of the model simulation make it possible to examine the impact of influencers on design outcomes, assessed in the form of exploration of design solution space and quality of the solution. The results show that teams with a few prominent influencers generate solutions with limited diversity. Moreover, during idea generation, the behavior of the teams with uniform distribution of influence is regulated by their team members' self-efficacy.

COVID-19 and Networks

Ongoing COVID-19 pandemic poses many challenges to the research of artificial intelligence. Epidemics are important in network science for modeling disease spread over networks of contacts between individuals. To prevent disease spread, it is desirable to introduce prioritized isolation of the individuals contacting many and unspecified others, or connecting different groups. Finding such influential individuals in social networks, and simulating the speed and extent of the disease spread are what we need for combating COVID-19. This article focuses on the following topics, and discusses some of the traditional and emerging research attempts: (1) topics related to epidemics in network science, such as epidemic modeling, influence maximization and temporal networks, (2) recent research of network science for COVID-19 and (3) datasets and resources for COVID-19 research.

Understanding Collaborative Journalism with Digital Trace Data and Crowdsourced Databases

This study represents a unique opportunity to study aspects of human behavior related to journalism projects collaboration at scale. Collaborative journalism deserves further inquiry in light of its growing importance, the resources devoted to it, and its role in creating more opportunities for news media in the face of economic and technological challenges. It theorizes how journalism collaborative/interest groups were created, maintained, and sustained. Methodologically, this study attempts to mine GitHub’s API to identify influential individuals and discover the network patterns of social collaboration in newsrooms’ repositories.

Maximizing Influence Over Streaming Graphs with Query Sequence

Now, with the prevalence of social media, such as Facebook, Weibo, how to maximize influence of individuals, products, actions in new media is of practical significance. Generally, maximizing influence first needs to identify the most influential individuals since they can spread their influence to most of others in the social media. Many studies on influence maximization aimed to select a subset of nodes in static graphs once. Actually, real graphs are evolving. So, influential individuals are also changing. In these scenarios, people tend to select influential individuals multiple times instead of once. Namely, selections are raised sequentially, forming a sequence (query sequence). It raises several new challenges due to changing influential individuals. In this paper, we explore the problem of Influence Maximization over Streaming Graph (SGIM). Then, we design a compact solution for storing and indexing streaming graphs and influential nodes that eliminates the redundant computation. The solution includes Influence-Increment-Index along with two sketch-centralized indices called Influence-Index and Reverse-Influence-Index. Computing influence set of nodes will incur a large number of redundant computations. So, these indices are designed to keep track of the nodes’ influence in sketches. Finally, with the indexing scheme, we present the algorithm to answer SGIM queries. Extensive experiments on several real-world datasets demonstrate that our method is competitive in terms of both efficiency and effectiveness owing to the design of index.

Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks

Identifying the influential nodes of complex networks is now seen as essential for optimizing the network structure or efficiently disseminating information through networks. Most of the available methods determine the spreading capability of nodes based on their topological locations or the neighbor information, the degree of node is usually used to denote the neighbor information, and the k-shell is used to denote the locations of nodes, However, k-shell does not provide enough information about the topological connections and position information of the nodes. In this work, a new hybrid method is proposed to identify highly influential spreaders by not only considering the topological location of the node but also the neighbor information. The percentage of triangle structures is employed to measure both the connections among the neighbor nodes and the location of nodes, the contact distance is also taken into consideration to distinguish the interaction influence by different step neighbors. The comparison between our proposed method and some well-known centralities indicates that the proposed measure is more highly correlated with the real spreading process, Furthermore, another comprehensive experiment shows that the top nodes removed according to the proposed method are relatively quick to destroy the network than other compared semi-local measures. Our results may provide further insights into identifying influential individuals according to the structure of the networks.

Demonstration of Participation Networks in Urban Transport Policy of Public and Private Sectors through Social Media: The Case of Bike-Sharing Pricing Strategy in China

Social media has become a valuable platform that enables public and private stakeholders to participate and interact in various transport policies. Using a network-based perspective and a case study of bike-sharing pricing strategies in China, this paper aims to quantitatively characterize the pattern and structure of multi-stakeholders engagement networks. Furthermore, this paper also empirically examines the confirmation bias that might exist among participants. Dataset on retweets from the Chinese Twitter-Sina Weibo is collected. Results reveal two types of important actors with unequal roles in terms of information diffusion: the “network root” and the “network bridge.” The former is mainly comprised of organizations and influential individuals who dominate message sharing, whereas the latter is comprised of the general public with various occupational backgrounds who control the efficiency and the scope of information spreading. The result also reveals a hierarchical structure in both networks and a community gathering like-minded individuals. The empirical result also demonstrates the existence of echo chambers in the transport participation network of governments and enterprises. Most echo chambers operate such that organizations or influential individuals amplify the views of the general public with more critical viewpoints. These findings of this study can assist transport stakeholders in crafting more sustainable strategies based on the understanding of uneven patterns in online public participation. Furthermore, this study sheds insights on how social media could be used to facilitate the collection of diverse people’s opinions and the evaluation of multi-stakeholder engagement for major transport issues.

The effect of exposure to digital appeals to participate in collective action posted by influentials on protest information sharing: Evidence from Japan and South Korea

Contemporary collective action theories put large horizontal digitally connected networks at the center of mass political action. They posit that information sharing among ordinary social media users makes possible new forms of rapid mass political action. However, recent research has shown that influential individuals can play a number of key roles in facilitating networked political action in seemingly leaderless movements. Still, the role of influential individuals in stimulating protest information sharing on social media is an important aspect of networked collective action that remains understudied. This study seeks to address this. Specifically, it investigates the following question: does exposure to appeals to engage in protest increase individuals’ motivation to share protest information? Drawing on evidence from an original survey experiment, this study shows that digital appeals to engage in collective action posted by influential individuals do elicit an increase in motivation to share the appeal. However, this result obtains only for Korean respondents, whereas influential appeals appear to have no effect on Japanese respondents. I argue that this difference is in part a function of different citizenship norms in the two countries, and the corresponding effects on social network dynamics. Preliminary analysis supports this interpretation, but further investigation is warranted.