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Global Contagion of Non-Viral Information
Contagion in Online Social Networks (OSN) is typically measured by the tendency of users to re-post information or to adopt a new behavior after exposure to that information/behavior. Most contagion research is bound by modeling: (i) only local neighbor-to-neighbor contagion (ii) the spread of viral information. However, most contagion events are non-viral and can also occur globally by non-neighbors through for example, exposure to information by exploratory browsing, or by content recommendation algorithms. This study is the first to address the phenomenon of both global and local contagion of non-viral information in a quantitative way. Analysis of Twitter networks reveals the prevailing nature of global contagion, the different temporal patterns between global and local contagion, and the ways it varies across topical categories. An interesting finding shows that users who retweeted due to global contagion have more Followers than those who retweeted due to local contagion
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
It is widely believed that one's peers influence product adoption behaviors.
This relationship has been linked to the number of signals a decision-maker
receives in a social network. But it is unclear if these same principles hold
when the pattern by which it receives these signals vary and when peer
influence is directed towards choices which are not optimal. To investigate
that, we manipulate social signal exposure in an online controlled experiment
using a game with human participants. Each participant in the game makes a
decision among choices with differing utilities. We observe the following: (1)
even in the presence of monetary risks and previously acquired knowledge of the
choices, decision-makers tend to deviate from the obvious optimal decision when
their peers make similar decision which we call the influence decision, (2)
when the quantity of social signals vary over time, the forwarding probability
of the influence decision and therefore being responsive to social influence
does not necessarily correlate proportionally to the absolute quantity of
signals. To better understand how these rules of peer influence could be used
in modeling applications of real world diffusion and in networked environments,
we use our behavioral findings to simulate spreading dynamics in real world
case studies. We specifically try to see how cumulative influence plays out in
the presence of user uncertainty and measure its outcome on rumor diffusion,
which we model as an example of sub-optimal choice diffusion. Together, our
simulation results indicate that sequential peer effects from the influence
decision overcomes individual uncertainty to guide faster rumor diffusion over
time. However, when the rate of diffusion is slow in the beginning, user
uncertainty can have a substantial role compared to peer influence in deciding
the adoption trajectory of a piece of questionable information
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