  This paper considers online reputation and polling systems where individuals
make recommendations based on their private observations and recommendations of
friends. Such interaction of individuals and their social influence is modelled
as social learning on a directed acyclic graph. Data incest (misinformation
propagation) occurs due to unintentional re-use of identical actions in the
for- mation of public belief in social learning; the information gathered by
each agent is mistakenly considered to be independent. This results in
overconfidence and bias in estimates of the state. Necessary and sufficient
conditions are given on the structure of information exchange graph to mitigate
data incest. Incest removal algorithms are presented. Experimental results on
human subjects are presented to illustrate the effect of social influence and
data incest on decision making. These experimental results indicate that social
learning protocols require careful design to handle and mitigate data incest.
The incest removal algorithms are illustrated in an expectation polling system
where participants in a poll respond with a summary of their friends' beliefs.
Finally, the principle of revealed preferences arising in micro-economics
theory is used to parse Twitter datasets to determine if social sensors are
utility maximizers and then determine their utility functions.
