  Social influence plays an important role in human behavior and decisions. The
sources of influence can be generally divided into external, which are
independent of social context, or as originating from peers, such as family and
friends. An important question is how to disentangle the social contagion by
peers from external influences. While a variety of experimental and
observational studies provided insight into this problem, identifying the
extent of social contagion based on large-scale observational data with an
unknown network structure remains largely unexplored. By bridging the gap
between the large-scale complex systems perspective of collective human
dynamics and the detailed approach of the social sciences, we present a
parsimonious model of social influence, and apply it to a central topic in
political science -- elections and voting behavior. We provide an analytical
expression of the county vote-share distribution in a two party system, which
is in excellent agreement with 92 years of observed U.S. presidential election
data. Analyzing the social influence topography over this period reveals an
abrupt transition in the patterns of social contagion -- from low to high
levels of social contagion. The results from our analysis reveal robust
differences among regions of the United States in terms of their social
influence index. In particular, we identify two regions of 'hot' and 'cold
spots of social influence, each comprising states that are geographically
close. These results suggest that social contagion effects are becoming more
instrumental in shaping large scale collective political behavior, which is at
the core of democratic societies.
