1,719 research outputs found
Social Dynamics of Digg
Online social media provide multiple ways to find interesting content. One
important method is highlighting content recommended by user's friends. We
examine this process on one such site, the news aggregator Digg. With a
stochastic model of user behavior, we distinguish the effects of the content
visibility and interestingness to users. We find a wide range of interest and
distinguish stories primarily of interest to a users' friends from those of
interest to the entire user community. We show how this model predicts a
story's eventual popularity from users' early reactions to it, and estimate the
prediction reliability. This modeling framework can help evaluate alternative
design choices for displaying content on the site.Comment: arXiv admin note: text overlap with arXiv:1010.023
What Stops Social Epidemics?
Theoretical progress in understanding the dynamics of spreading processes on
graphs suggests the existence of an epidemic threshold below which no epidemics
form and above which epidemics spread to a significant fraction of the graph.
We have observed information cascades on the social media site Digg that spread
fast enough for one initial spreader to infect hundreds of people, yet end up
affecting only 0.1% of the entire network. We find that two effects, previously
studied in isolation, combine cooperatively to drastically limit the final size
of cascades on Digg. First, because of the highly clustered structure of the
Digg network, most people who are aware of a story have been exposed to it via
multiple friends. This structure lowers the epidemic threshold while moderately
slowing the overall growth of cascades. In addition, we find that the mechanism
for social contagion on Digg points to a fundamental difference between
information spread and other contagion processes: despite multiple
opportunities for infection within a social group, people are less likely to
become spreaders of information with repeated exposure. The consequences of
this mechanism become more pronounced for more clustered graphs. Ultimately,
this effect severely curtails the size of social epidemics on Digg.Comment: 8 pages, 10 figures, accepted in ICWSM1
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
Social Information Processing in Social News Aggregation
The rise of the social media sites, such as blogs, wikis, Digg and Flickr
among others, underscores the transformation of the Web to a participatory
medium in which users are collaboratively creating, evaluating and distributing
information. The innovations introduced by social media has lead to a new
paradigm for interacting with information, what we call 'social information
processing'. In this paper, we study how social news aggregator Digg exploits
social information processing to solve the problems of document recommendation
and rating. First, we show, by tracking stories over time, that social networks
play an important role in document recommendation. The second contribution of
this paper consists of two mathematical models. The first model describes how
collaborative rating and promotion of stories emerges from the independent
decisions made by many users. The second model describes how a user's
influence, the number of promoted stories and the user's social network,
changes in time. We find qualitative agreement between predictions of the model
and user data gathered from Digg.Comment: Extended version of the paper submitted to IEEE Internet Computing's
special issue on Social Searc
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