96 research outputs found
VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting
Information spread in social media depends on a number of factors, including
how the site displays information, how users navigate it to find items of
interest, users' tastes, and the `virality' of information, i.e., its
propensity to be adopted, or retweeted, upon exposure. Probabilistic models can
learn users' tastes from the history of their item adoptions and recommend new
items to users. However, current models ignore cognitive biases that are known
to affect behavior. Specifically, people pay more attention to items at the top
of a list than those in lower positions. As a consequence, items near the top
of a user's social media stream have higher visibility, and are more likely to
be seen and adopted, than those appearing below. Another bias is due to the
item's fitness: some items have a high propensity to spread upon exposure
regardless of the interests of adopting users. We propose a probabilistic model
that incorporates human cognitive biases and personal relevance in the
generative model of information spread. We use the model to predict how
messages containing URLs spread on Twitter. Our work shows that models of user
behavior that account for cognitive factors can better describe and predict
user behavior in social media.Comment: SBP 201
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
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
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
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks
Identifying the most influential spreaders that maximize information flow is
a central question in network theory. Recently, a scalable method called
"Collective Influence (CI)" has been put forward through collective influence
maximization. In contrast to heuristic methods evaluating nodes' significance
separately, CI method inspects the collective influence of multiple spreaders.
Despite that CI applies to the influence maximization problem in percolation
model, it is still important to examine its efficacy in realistic information
spreading. Here, we examine real-world information flow in various social and
scientific platforms including American Physical Society, Facebook, Twitter and
LiveJournal. Since empirical data cannot be directly mapped to ideal
multi-source spreading, we leverage the behavioral patterns of users extracted
from data to construct "virtual" information spreading processes. Our results
demonstrate that the set of spreaders selected by CI can induce larger scale of
information propagation. Moreover, local measures as the number of connections
or citations are not necessarily the deterministic factors of nodes' importance
in realistic information spreading. This result has significance for rankings
scientists in scientific networks like the APS, where the commonly used number
of citations can be a poor indicator of the collective influence of authors in
the community.Comment: 11 pages, 4 figure
Performance Analysis of Online Social Platforms
We introduce an original mathematical model to analyze the diffusion of posts
within a generic online social platform. Each user of such a platform has his
own Wall and Newsfeed, as well as his own self-posting and re-posting activity.
As a main result, using our developed model, we derive in closed form the
probabilities that posts originating from a given user are found on the Wall
and Newsfeed of any other. These probabilities are the solution of a linear
system of equations. Conditions of existence of the solution are provided, and
two ways of solving the system are proposed, one using matrix inversion and
another using fixed-point iteration. Comparisons with simulations show the
accuracy of our model and its robustness with respect to the modeling
assumptions. Hence, this article introduces a novel measure which allows to
rank users by their influence on the social platform, by taking into account
not only the social graph structure, but also the platform design, user
activity (self- and re-posting), as well as competition among posts.Comment: Preliminary version of accepted paper at INFOCOM 2019 (Paris, France
- …