Studying Diffusion of Viral Content at Dyadic Level

Diffusion of information and viral content, social contagion and influence are still topics of broad evaluation. As theory explaining the role of influentials moves slightly to reduce their importance in the propagation of viral content, authors of the following paper have studied the information epidemic in a social networking platform in order to confirm recent theoretical findings in this area. While most of related experiments focus on the level of individuals, the elementary entities of the following analysis are dyads. The authors study behavioral motifs that are possible to observe at the dyadic level. The study shows significant differences between dyads that are more vs less engaged in the diffusion process. Dyads that fuel the diffusion proccess are characterized by stronger relationships (higher activity, more common friends), more active and networked receiving party (higher centrality measures), and higher authority centrality of person sending a viral message.Comment: ASONAM 2012, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1291-129

Studying Paths of Participation in Viral Diffusion Process

Authors propose a conceptual model of participation in viral diffusion process composed of four stages: awareness, infection, engagement and action. To verify the model it has been applied and studied in the virtual social chat environment settings. The study investigates the behavioral paths of actions that reflect the stages of participation in the diffusion and presents shortcuts, that lead to the final action, i.e. the attendance in a virtual event. The results show that the participation in each stage of the process increases the probability of reaching the final action. Nevertheless, the majority of users involved in the virtual event did not go through each stage of the process but followed the shortcuts. That suggests that the viral diffusion process is not necessarily a linear sequence of human actions but rather a dynamic system.Comment: In proceedings of the 4th International Conference on Social Informatics, SocInfo 201

Characterizing Attention Cascades in WhatsApp Groups

An important political and social phenomena discussed in several countries, like India and Brazil, is the use of WhatsApp to spread false or misleading content. However, little is known about the information dissemination process in WhatsApp groups. Attention affects the dissemination of information in WhatsApp groups, determining what topics or subjects are more attractive to participants of a group. In this paper, we characterize and analyze how attention propagates among the participants of a WhatsApp group. An attention cascade begins when a user asserts a topic in a message to the group, which could include written text, photos, or links to articles online. Others then propagate the information by responding to it. We analyzed attention cascades in more than 1.7 million messages posted in 120 groups over one year. Our analysis focused on the structural and temporal evolution of attention cascades as well as on the behavior of users that participate in them. We found specific characteristics in cascades associated with groups that discuss political subjects and false information. For instance, we observe that cascades with false information tend to be deeper, reach more users, and last longer in political groups than in non-political groups.Comment: Accepted as a full paper at the 11th International ACM Web Science Conference (WebSci 2019). Please cite the WebSci versio

Stimulate social interaction between consumers: a network-oriented framework

It appears networks arise in every sphere of human activity (Batten et al., 1996). This article focuses on social networks in which consumers are embedded. Despite the widely acknowledged significance of consumer networks, social network theory has rarely been integrated into marketing theory (Reingen, 1994). This peculiar absence will be addressed in the following. First, a marketing view will be proposed extending the classical dyadic relationship view to a triad. Furthermore, an aggregated marketing model will be outlined that views word of mouth not only as an output variable, but as variable that can be influenced. Implications are given in order to initiate, stimulate and control social interaction between consumers excluding mass communications

Productivity Effects of Information Diffusion in Networks

We examine the drivers of diffusion of information through organizations and the effects on performance. In particular, we ask: What predicts the likelihood of an individual becoming aware of a strategic piece of information, or becoming aware of it sooner? Do different types of information exhibit different diffusion patterns, and do different characteristics of social structure, relationships and individuals in turn affect access to different kinds of information? Does better access to information predict an individual’s ability to complete projects or generate revenue? We hypothesize that the dual effects of content and structure jointly predict the diffusion path of information, and ultimately performance. To test our hypotheses, we characterize the social network of a medium sized executive recruiting firm using accounting data on project co-work relationships and ten months of email traffic observed over two five month periods. We identify two distinct types of information diffusing over this network – ‘event news’ and ‘discussion topics’ – by their usage characteristics, and observe several thousand diffusion processes of each type of information from their original first use to their varied recipients over time. We then test the effects of network structure and functional and demographic characteristics of dyadic relationships on the likelihood of receiving each type of information and receiving it more quickly. Our results demonstrate that the diffusion of news, characterized by a spike in communication and rapid, pervasive diffusion through the organization, is influenced by demographic and network factors but not by functional relationships (e.g. prior co-work, authority) or the strength of ties. In contrast, diffusion of discussion topics, which exhibit more shallow diffusion characterized by ‘back-and-forth’ conversation, is heavily influenced by functional relationships and the strength of ties, as well as demographic and network factors. Discussion topics are more likely to diffuse vertically up and down the organizational hierarchy, across relationships with a prior working history, and across stronger ties, while news is more likely to diffuse laterally as well as vertically, and without regard to the strength or function of relationships. Furthermore, we find that access to information strongly predicts the number of projects completed by each individual and the amount of revenue that person generates. The effects are economically significant, with each additional “word seen” correlated with about $70 of additional revenue generated. Our findings highlight the importance of simultaneous considerations of structure and content in information diffusion studies and provide some of the first evidence on the economic importance of information diffusion in networks.The National Science Foundation, Cisco Systems, France Telecom and the MIT Center for Digital Busines

Variation in the timing of Covid-19 communication across universities in the UK.

During the Covid-19 pandemic, universities in the UK used social media to raise awareness and provide guidance and advice about the disease to students and staff. We explain why some universities used social media to communicate with stakeholders sooner than others. To do so, we identified the date of the first Covid-19 related tweet posted by each university in the country and used survival models to estimate the effect of university-specific characteristics on the timing of these messages. In order to confirm our results, we supplemented our analysis with a study of the introduction of coronavirus-related university webpages. We find that universities with large numbers of students are more likely to use social media and the web to speak about the pandemic sooner than institutions with fewer students. Universities with large financial resources are also more likely to tweet sooner, but they do not introduce Covid-19 webpages faster than other universities. We also find evidence of a strong process of emulation, whereby universities are more likely to post a coronavirus-related tweet or webpage if other universities have already done so

Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14

Networked Experiments

This chapter considers the design and analysis of networked experiments, one of the most precise tools available for studying social behavior. As a result of digitization, the scale, scope and complexity of networked experiments have expanded significantly in recent years, creating a need for more robust design and analysis strategies. I first review innovations in networked experimental design, assessing the implications of the experimental setting, sampling, randomization procedures and treatment assignment. I then discuss the analysis of networked experiments, with particular emphasis on modeling treatment response assumptions, inference and estimation, and recent approaches to interference and uncertainty in dependent data. I conclude by discussing important challenges facing the future of networked experimentation, focusing on adaptive treatment assignment, novel randomization techniques, linking online treatments to offline responses and experimental validation of observational methods. I hope this framework can help guide future work toward a cumulative research tradition in networked experimentation