Analyzing social media networks with NodeXL - Chapter-10 Images

1Copyright © 2011, Elsevier Inc. All rights Reserved

Chapter 10

TwitterConversation, Entertainment, and Information,

All in One Network!

Analyzing Social Media Networks with NodeXLInsights from a Connected World

2

Scott Golder (@redlog) is a graduate student in Sociology at Cornell University. He was previously a researcher at HP Labs, and holds an A.B. in Linguistics with Computer Science from Harvard University and an M.S. in Media Arts and Sciences from the MIT Media Laboratory. His research interests broadly include network and social identity effects online, which he has examined in a variety of environments including usenet, online poker, social bookmarking and social network services. His website is www.redlog.net.

Vladimir Barash (@vlad43210) is a graduate student in Information Science at Cornell University. He holds a BA in Cognitive Science from Yale University. His research interests include social media, online communities and diffusion, and his thesis topic is on the structural properties of diffusion in social networks. His websited is www.vlad43210.com


FIGURE 10.1C

hapter 10

Estimated growth of Twitter. The growth curve shows sharp increases in March 2007 and April 2009, because of the SXSW festival and The Oprah Winfrey Show .


FIGURE 10.2C

hapter 10

Twitter’s web-based interface. Its most prominent contents are a stream of tweets (left) and a dashboard with user-specific information (right).


FIGURE 10.3C

hapter 10

TweetDeck is a popular desktop-based Twitter client.


FIGURE 10.4C

hapter 10

Twhirl is a popular desktop-based Twitter client.


FIGURE 10.5C

hapter 10

A tweet with an @reply message. This message is sent from @vlad43210 to @redlog.


FIGURE 10.6C

hapter 10

An interesting tweet posted by @ASAnews. This tweet starts a retweet chain (see the next figures).


FIGURE 10.7C

hapter 10

@redlog retweets the tweet posted by @ASAnews. The @ASANews message about David Brooks’ article now spreads to @redlog’s followers, even if they don’t follow @ASANews directly.


FIGURE 10.8C

hapter 10

@vlad43210 retweets @redlog’s retweet of the tweet posted by @ASAnews. The message now spreads to @vlad43210’s followers, even if they do not follow either @ASAnews or @redlog. In this way, messages can spread via retweets and reach a very large audience.


FIGURE 10.9C

hapter 10

The friends and followers network is an information/ attention network . This figure shows Scott with two friends (Pee-Wee and Derek) and two followers (Marc and Vlad).


FIGURE 10.10C

hapter 10

The middle actor is a bridge between two communities. The top, red numbers above the actors are their eigenvector centrality, and the bottom, blue numbers are their betweenness centrality.


FIGURE 10.11C

hapter 10

The NodeXL Import menu. Note there are two options that import Twitter data, from a user’s network and from a search query.


FIGURE 10.12C

hapter 10

NodeXL’s Twitter User’s Network import screen. The levels options selected will import both @vlad43210’s friends and followers, as well as any friend/follower connections between them.


FIGURE 10.13C

hapter 10

The @vlad43210’s 1.5-degree egocentric Twitter network. The vertex at the center is @vlad43210, the many vertices at the edges represent @vlad43210’s weak social ties, most of them complete strangers.


FIGURE 10.14C

hapter 10

The @vlad43210’s 1.5-degree egocentric Twitter network. We have filtered out the complete strangers to focus on @vlad43210’s strong social ties — his friends and colleagues. Two clusters emerge (top right and bottom left).


FIGURE 10.15C

hapter 10

The @vlad43210 1.5-degree egocentric Twitter network (strong social ties only). NodeXL automatically identifi es the clusters and colors them differently. Top right (green) corresponds to @vlad43210’s friends, and bottom left (purple) corresponds to his academic colleagues.


FIGURE 10.16C

hapter 10

The @vlad43210 1.5-degree egocentric Twitter network. Greener vertices have higher eigenvector centrality, and larger vertices have higher betweenness centrality.


FIGURE 10.17C

hapter 10

The @vlad43210 1.5-degree egocentric Twitter network. Greener vertices have higher eigenvector centrality, and larger vertices have higher betweenness centrality. Thicker edges correspond to users that @reply to @vlad43210 or mention his username in one of their tweets.


FIGURE 10.18C

hapter 10

Twitter Search Network import dialog. The options selected import the first 100 people to mention “Black Friday” in their tweets, and all of the Follows, Replies-to, and Mentions relationships between them.


FIGURE 10.19C

hapter 10

Twitter mentions network for “Black Friday.” Note that the network resembles a set of connected stars: around @Shop- NPartyGirl, @luv_mydaschund, and @sdiego1717, respectively.


FIGURE 10.20C

hapter 10

Twitter mentions network for “Black Friday.” Thicker edges represent retweets of @ShopNPartyGirl’s messages.


FIGURE 10.21C

hapter 10

Twitter mentions network for “Black Friday.” @ShopNPartyGirl and her friend/ follow relationships are highlighted in red. Greener vertices have more tweets, larger vertices have more followers.


FIGURE 10.22C

hapter 10

Twitter mentions network for “Black Friday.” @ShopNPartyGirl and her friend/ follow relationships are highlighted in red. Greener vertices have more tweets, larger vertices have more followers. @ShopNPartyGirl is represented in the canvas by her Twitter profile image.

Date post:	28-Jan-2015
Category:	Technology
Upload:	marc-smith
View:	103 times
Download:	0 times

Analyzing social media networks with NodeXL - Chapter-10 Images

Technology