Weak ties: Subtle role of information diffusion in online social networks

As a social media, online social networks play a vital role in the social information diffusion. However, due to its unique complexity, the mechanism of the diffusion in online social networks is different from the ones in other types of networks and remains unclear to us. Meanwhile, few works have been done to reveal the coupled dynamics of both the structure and the diffusion of online social networks. To this end, in this paper, we propose a model to investigate how the structure is coupled with the diffusion in online social networks from the view of weak ties. Through numerical experiments on large-scale online social networks, we find that in contrast to some previous research results, selecting weak ties preferentially to republish cannot make the information diffuse quickly, while random selection can achieve this goal. However, when we remove the weak ties gradually, the coverage of the information will drop sharply even in the case of random selection. We also give a reasonable explanation for this by extra analysis and experiments. Finally, we conclude that weak ties play a subtle role in the information diffusion in online social networks. On one hand, they act as bridges to connect isolated local communities together and break through the local trapping of the information. On the other hand, selecting them as preferential paths to republish cannot help the information spread further in the network. As a result, weak ties might be of use in the control of the virus spread and the private information diffusion in real-world applications.Comment: Final version published in PR

Information diffusion in online social networks

International audienceOnline social networks play a major role in the spread of information at very large scale and it becomes essential to provide means to analyze this phenomenon. Analyzing information diffusion proves to be a challenging task since the raw data produced by users of these networks are a flood of ideas, recommendations, opinions, etc. The aim of this PhD work is to help in the understanding of this phenomenon. So far, our contributions are the following: (i) a survey of developments in the field; (ii) T-BaSIC, a graph-based model for information diffusion prediction; (iii) SONDY, an open source platform that helps understanding social network users' interests and activity by providing emerging topics and events detection as well as network analysis functionalities

Information is not a Virus, and Other Consequences of Human Cognitive Limits

The many decisions people make about what to pay attention to online shape the spread of information in online social networks. Due to the constraints of available time and cognitive resources, the ease of discovery strongly impacts how people allocate their attention to social media content. As a consequence, the position of information in an individual's social feed, as well as explicit social signals about its popularity, determine whether it will be seen, and the likelihood that it will be shared with followers. Accounting for these cognitive limits simplifies mechanics of information diffusion in online social networks and explains puzzling empirical observations: (i) information generally fails to spread in social media and (ii) highly connected people are less likely to re-share information. Studies of information diffusion on different social media platforms reviewed here suggest that the interplay between human cognitive limits and network structure differentiates the spread of information from other social contagions, such as the spread of a virus through a population.Comment: accepted for publication in Future Interne

Spontaneous diffusion of information in online social networks

Online Social Networks (OSN) are new types of web services which provide online communities an environment to gather and meet virtually. The online users are connected to each other via links of trust and utilize the features of the OSN to interact and communicate in an easy socio-technical way. Hence these virtual networks of social relationships have a high potential for influential decision-making and the word of mouth spread of information, but also for spreading fads, rumors, and erroneous information. The power of these new forms of social networks is also recognized by service providers, marketers and vendors of consumer goods. They would all like to (mis) use these existing communication channels to spread product placements, advertising and promotions directly to the connected users. However, just like the old economy businesses, not all attempted marketing initiatives are successful. Most of them fail or do notreach the desired audience. This paper tries to explain why OSN are a good environment for spontaneous diffusion of information and what phases of development need to beaccomplished to reach the optimal spreading rate for one piece information. Therefore, we start with a look at the "Hype Cycle" model of Gartner to explain over-enthusiasm for new technology adoptions. Next we introduce the concept of "social contagion" and the infections spread of information. After a short introduction of OSN, we try to illustrate the phases of a social online contagion development process which can lead to spontaneous and uncontrolled diffusion of information, messages or ideas. The core statement of our approach is that online individuals tend to behave collectively if they observe the virtual behaviors and actions of others. This principle of "other-directedness" can generate a chain reaction of infectious imitation which can sometimes spread uncontrolled through the interconnected social network like an epidemic. This helps to explain why some online information waves can grow extraordinarily high and others fall.Онлайнові соціальні мережі (ОСМ) є новими типами веб-сервісів, які пропонують онлайновим суспільствам середовище для гуртування та віртуального спілкування. Як наслідок, такі віртуальні мережі соціальних зв'язків мають високий потенціал для впливового прийняття рішень та розповсюдження інформації "з вуст в уста", але, з іншого боку, вони також можуть розповсюджувати чутки, плітки та некоректну інформацію. Потенціал цих мереж також розпізнається сервіс-провайдерами, маркетологами та виробниками товарів. Вони усі бажають використовувати ці існуючі комунікаційні канали для розповсюдження реклами продуктів безпосередньо користувачам. Але не усі такі спроби є успішними. Ця робота робить спробу пояснити, чому ОСМ є добрим середовищем для спонтанного розповсюдження інформації, та які етапи повинні бути виконані для досягнення оптимального рівня розповсюдження для одного елемента інформації. Ми починаємо з розгляду моделі гіперциклів Гартнера, яка пояснює надмірний ентузіазм при впровадженні нових технологій. Далі ми вводимо концепцію "соціального забруднення" та інфекційного розповсюдження інформації. Базова ідея нашого підходу полягає в тому, що онлайнові індивідуали прихильні до колективної поведінки, тобто вони спрямовані на віртуальну поведінку та дії інших. Цей принцип "спрямованості на інших" може генерувати ланцюгову реакцію інфекційних імітацій які інколи можуть розповсюджуватись неконтрольовано через соціальні мережі, подібно до епідемії

Spreading processes in Multilayer Networks

Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.Comment: 21 pages, 3 figures, 4 table