that node.[]{data-label="fig:sf"}](Untitled2.png)

Our dataset was collected from Twitter and Foursquare in the United States between May and September 2012, where tweets and check-ins were downloaded for users who had checked-in during that time, and where those check-ins were shared on Twitter. This allows us to study the intersection of the two networks through a subset of users who have accounts and are active on both Twitter and Foursquare, and have chosen to share their check-ins to Twitter.

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[l\*[6]{}[l]{}r]{} Property & New York & Chicago & SF & All\
$|V^{\cal{M}}|$ & 6,401 & 2,883 & 1,705 & 10,989\
$|E^{T \cap F}|$ & 9,101 & 5,486 & 1,517 & 16,104\
$|E^{T \backslash F}|$ & 13,623 & 7,949 & 1,776 & 23,348\
$|E^{F \backslash T}|$ & 6,394 & 4,202 & 863 & 11,459\
$<k_{GN}>$ & 4.55 & 6.12 & 2.44 & 4.63\
$<k_{CN}>$ & 1.42 & 1.9 & 0.89 & 1.47\
$tweets$ & 2,509,802 & 1,288,865 & 632,780 & 4,431,447\
$checkins$ & 228,422 & 105,250 & 46,823 & 380,495\
$venues$ & 24,110 & 11,773 & 6,934 & 42,817\

[0.245]{} ![image](int_deg2.pdf){width="\textwidth"}

[0.245]{} ![image](union_deg2.pdf){width="\textwidth"}

[0.245]{} ![image](intunionoverlap2.pdf){width="\textwidth"}

[0.245]{} ![image](unionvsint2.pdf){width="\textwidth"}

We focus our analysis on the top three cities in terms of activity during the period. 