The Influentials: New approaches for Analyzing Influence on Twitter

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The Infuentials:New Approaches or

Analyzing Infuence on Twitter

a publication o the Web Ecology Project

Pub. 04 (2 September 2009)



ANALYZING INFLUENCE ON TWITTER

2 September 2009 page 2

http://webecologyproject.org

AuthorsBy Alex Leavitt

with Evan Burchard, David Fisher, & Sam Gilbert

We would also like to thank Jon Beilin, Mac Cowell, and Tim Hwang or their invaluable contributions,

eedback, and support.

About The Web Ecology ProjectThe Web Ecology Project is an interdisciplinary research group based in Boston, Massachusetts

ocusing on using large scale data mining to analyze the system-wide ows o culture and com-

munity online. In addition to the task o understanding culture on the web through quantitative

research and rigorous experimentation, we are attempting to build a science around community

management and social media. To that end, we are building tools and conducting research that

enable planners to launch data-driven campaigns backed by network science.

For press or research inquiries, please visit our website (http://webecologyproject.org), contact

us via email at [email protected], or call Tim Hwang (CEO) at [973] 960-4955.

The Web Ecology Project releases this paper under a Creative Commons Attribution Share-Alike 3.0 license

(http://creativecommons.org/licenses/by-sa/3.0/).






Executive SummaryUsing a new methodology based on the content and responses o 12 popular users, we determined mea-

surements o relative inuence on Twitter. We examined an ecosystem o 134,654 tweets, 15,866,629 ol-

lowers, and 899,773 ollowees, and in response to the 2,143 tweets generated by these 12 users over a 10-

day period, we collected 90,130 responses published by other users.

Summary o Findings

An analysis o our methodology and statistics suggests that on Twitter, among various congurable con-

clusions:

• mashable is more inuential than CNN .

• sockington is more inuential than MCHammer, while MCHammer is more inuential than three ma-

jor social media analysts ( garyvee, Scobleizer, and chrisbrogan ).

• Celebrities with higher ollower totals (eg., THE_REAL_SHAQ and ijustine ) oster more conversation

than provide retweetable content.

• News outlets, regardless o ollower count, inuence large amounts o ollowers to republish their

content to other users.






Following Followers: Judging Inuence on TwitterAs a simple online platorm or conversation, Twitter is an ideal an ecological system through

which we can understand the relationship between users and their environments on the Web. Especially

compared to other social networks, Twitter simplies most o the extraneous eatures and boils down

its environment to people and content. The unusual simplicity o Twitter, though, continues to warp

perception o how the relationship between user and platorm operates. Many o the popularized stud-

ies examining inuence on Twitter ail to identiy the nuances o social interaction in the system. While

attempts have been made (eg., http://twinuence.com/about.php ), the analyses tend to ocus on the con-

nections between users rather than the relationship o users, content, and platorm. This report there-

ore aims to supplement previous investigations o the Twitter environment with more comprehensive

data sets to enhance new approaches to understanding the concept o “inuence” on social networks.

A ocus solely on the connections between users skews an understanding o how inuence oper-

ates and ows on Twitter. A popular metric o perceived inuence on Twitter measures the quantity o

a user’s ollowers. In general, the more ollowers a user possess, the more impact he appears to make in

the Twitter environment, because he seems more popular (namely, that users ollow him). This state-

ment makes senseassuming that Twitter acts as a successul broadcast medium, where a user publishes

a tweet and it is read by every ollower. However, this view o Twitter as a broadcast medium ignores the

potential or users to interact with the content on the platorm.

A similar and equally popular metric to measure inuence on Twitter relies on the ratio between

the number o a user’s ollowers and the number o other people that the user ollows (his audience, or

as we designate in this report, ollowees). This ratio, while better than the ormer method o counting

ollowers, is still imprecise. Again, a ratio based on audience ignores the ability or a user to interact with

content on the platorm. However, the ratio o ollowers to ollowees does inorm a better understanding

o how inuence can operate in Twitter’s environment.

http://twinfluence.com/about.php

http://twinfluence.com/about.php






The ratio o ollowers to ollowees may communicate the intended purpose or emergent practices

o a user. For example, i the ratio approaches innity (high ollower total versus low ollowee total), the

user account might be described as ocusing on the material aspect o Twitter. By material, we mean a

compulsion toward moving content to other users in the environment. In another instance, i the ratio

approaches 1 (an equal or near-equal amount o ollowers and ollowees), the user might be categorized

as a conversationalist. The user most likely ollows back a majority o his ollowers, to retain amiliarity

with more personal conversations. Contrarily, the materialistic user aims to collect ollowers as contacts

to whom the user may push content (who may then share the same content with other users). Finally, i

the ratio approaches zero (low ollower total versus high ollowee total), we might categorize the user as

a spammer. As an emergent behavior, the stereotypical spammer attempts to collect users with the intent

to push content to as many people as possible ater achieving a high ollower tally. However, most contem-

porary users can spot the stereotypical behavior o a spammer or bot, resulting in the low ollower total on

the spammer’s account.

While the ollower to ollowee ratio does not represent an accurate measurement o inuence on

Twitter, the ratio does inorm the community to types o users. Beore we apply these types to our under-

standing o online inuence, we must rst dene inuence.

Defning Inuence on Twitter An attempt to dene a universal concept o inuence on the Web remains dicult, because we

must account or the variations o platorms, uidity o environments, and evolving behaviors o users

online. Because each platorm is diferent, this report will rely on a denition o online inuence specic

to the environment o Twitter. Thereore, we dene inuence on Twitter as the potential o an action o a

user to initiate a urther action by another user. The term user is dened by Twitter’s platorm. The term

action deserves urther explanation.






Understanding the term action as it relates to inuence on Twitter depends on the undamen-

tal structure o ideas in the environment and how these ideas move. The undamental unit o content

on Twitter is the tweet (a user may type up to 140 characters and publish them to the web interace), so

an action on Twitter comprises all interactions o a user and that unit o content (tweet). While we can

analyze various types o inuential actions (eg., a view on YouTube or a like on Facebook), this report will

primarily ocus on actions specic to Twitter. Our analysis o inuence on Twitter, then, relies on the un-

derstanding o how actions shape behavior on the platorm.

Inuence as Actions; Actions as ResponsesWhile actions on Twitter comprise both those interactions recognized by the platorm as well as

unexpected emergent behaviors that become widely used by users, Twitter recognizes two actions intrin-

sic to the system that can occur: the reply and the retweet.

Reply: @username {content}

Example:

@chrisbrogan Thanks or this. I’m new to twitter and it was really helpul

Digitaltonto (on 2009-08-15 at 00:47:17)

Retweet : RT @username {content}

RT @aplusk great article thank U RT @Morgan_Johnston: this great article on health care by Whole Foods

coounder/CEO

cheerok (on 2009-08-15 at 00:31:10)

The reply and retweet are categorized as actions because they are applied by a user to a piece o content.

The reply acts as a response to another user’s tweet using new content, while the retweet operates as a ci-

tation or paraphrase o another user’s previous content. While both actions have diferent purposes, both






are meant to move content to other users (albeit in difering ways). I a reply or retweet exists with respect

to a given tweet, the actions are evidence or inuence that has occurred. A reply occurs because a user is

inuenced to reply to the content; a retweet occurs because a user is inuenced to reproduce the content.

Literally, the actions are markers o inuence.

Two other actions that appear requently on Twitter, extrinsic to the system yet popular enough to

have become adopted by users, require explanation: the mention and the attribution.

Mention: {content} @username ({content})

Watching @BarackObama speak in Colorado on @CNN

RareAir24 (on 2009-08-15 at 19:08:51)

Attribution: {content} via @username ({content})

Fire at Kuwaiti wedding kills dozens, ofcial media says http://bit.ly/wn95A (via @cnnbrk)

ChilliGaz (on 2009-08-15 at 19:40:18)

Similar to the reply and the retweet, the mention and the attribution are categorized as actions because

they too are applied by a user to a piece o content. We have separated the mention and the attribution

rom the more undamental reply and retweet because the ormer two actions are not ocially recognized

by the Twitter platorm. In act, a mention is similar to a reply, except a mention occurs at some point in

the tweet other than at the beginning. Comparably, an attribution is similar to a retweet, except an attribu-

tion borrows the symbology o the reply to provide a citation or previously published content.

We must also note here that, rst, while we distinguish the attribution rom the mention, we have

calculated them rom the same database query. Any measurement in this report o mentions also encap-

sulates attributions; however, we will distinguish the attribution as separate rom the mention later in the

paper (by tallying it alongside retweets in certain equations). Second, since mentions theoretically serve






the purpose o replies, and attributions the purpose o retweets, we have not expounded upon their use in

the explanation o inuence in the ollowing paragraphs. However, we can hypothesize that the applica-

tions o replies include mentions and the applications o retweets include attributions.

Categorizing Actions: Conversation & ContentIn the second-to-previous paragraph, we hint at a similar categorization or actions that we pre-

viously applied to users. Given two probable types o users, one ocused on conversation and another on

content, we can map these classications to actions -- replies and retweets, respectively -- to explain how

the relationship between users o and the actions on a platorm shapes inuence on Twitter. The pur-

pose o replies assumes that a conversation is the intended goal o the action. In writing a reply, the user

has been inuenced to respond to a previous unit o content published by another user. Similarly, with a

retweet (the objective o which is to push content), the user has been inuenced by a previous user’s con-

tent to reproduce the content or other users to view. In basic terms, we can see the reply as talking back

to the rst user and the retweet as passing on content to a third user. However, when assigning values o

inuence to these types o actions, we do not give preerence to one or the other.

Previously, we examined two possible approaches to measuring inuence on Twitter: 1) count-

ing the total number o ollowers a user possesses, and 2) calculating the ratio o a user’s ollowers to a

user’s ollowees. These two approaches still ignore the relationship between the user, the content, and the

platorm. The goal o this report is to move beyond these basic assertions about inuence by analyzing a

comprehensive set o replies, retweets, and other actions on Twitter that act as evidence or the inuential

potential o users.

Understanding Inuence with New DataFor this report, we gathered relevant data rom 12 Twitter users or 10 days, between 12:00 am 15

August 2009 and 12:00 am 25 August 2009. We ocused on a small number o celebrities, news outlets, and

social media analysts widely perceived to be among the more inuential users on Twitter. Based on the






content and connections o these 12 users, we examined a total o 134,654 tweets, 15,866,629 ollowers,

and 899,773 ollowees. In response to the 2,143 tweets generated by these 12 users o the 10 day period,

we collected 90,130 responses (actions) published by other users (which equates to 87,987 more messages

than total original tweets, or a total average o 42 responses per tweet).

We have listed the 12 users below, categorized into three distinct groups that we eel ultimately

represent the user types previously discussed. We have also calculated the total number o tweets pub-

lished by each user, the total number o each users’ ollowers, and the total number o users that each o

our 12 users ollows. These statistics were updated between 28 August 2009 and 30 August 2009, so they

may not necessarily reect the exact number o tweets, ollowers, and ollowees present during the 10-day

window that our data encompasses.

Celebrity Username Tweets Followers Followees

Ashton Kutcher aplusk 3,205 3,407,385 209

Shaquille O’Neil THE_REAL_SHAQ 2,072 2,092,541 562

Stanley Kirk Burrell MCHammer 6,016 1,331,797 31,202

Sockington sockington 5,711 1,089,984 380

Justine Ezarik ijustine 7,718 605,441 3,039News Outlet Username Tweets Followers Followees

CNN Breaking News cnnbrk 1,096 2,712,530 18

BarackObama.com BarackObama 330 2,018,016 761,851

Mashable.com mashable 17,914 1,363,510 1,925

CNN cnn 11,607 193,625 50

Social Media Analyst Username Tweets Followers Followees

Gary Vaynerchuk garyvee 7,532 862,790 9,683

Chris Brogan chrisbrogan 48,341 94,715 88,431

Robert Scoble Scobleizer 23,112 94,295 2,423

The above table has been arranged in decreasing order by total ollowers, based on the three distinct cat-

egories o users. These categories reveal certain resemblances to aspects o content user types and conver-

sation user types. Generally, news outlets aim to push content, social media analysts strive to perpetuate

conversations, and celebrities tend to do both (dependent on their personal practices and the community






who ollow them). While there are some anomalies (eg., BarackObama), most news outlets have a higher

ollower to ollowee ratio (materialistic) while most analysts have a more-equal ollower-to-ollowee ratio

(conversationalist). For celebrities, the ratio appears to avor a materialistic purpose on Twitter, but the

responses generated by celebrities avor the conversationalist type.

In the graph below, we present a comprehensive diagram o total ollow count, to reemphasize the

perceived inuence that each user projects. Keep in mind that although Robert Scoble (Scobleizer, ranked

12th) appears unimportant compared to Ashton Kutcher (aplusk, ranked 1st), Scoble still retains a high

level o perceived inuence across the entirety o Twitter, since his total number o ollowers amounts to

over 94,000 (compared to many users that have between 50 and 1,000 ollowers).






Inuence According to Audience ResponseFollowers, as stated beore, cannot account or a reliable measurement o inuence on Twitter. In-

stead, we must take into account the markers o inuence -- replies, retweets, mentions, and attributions

-- to inorm which user holds more sway over his ollowers. The graph below measures the percentage o

replies, retweets, and mentions per user, based on the total number o responses respective to each user.

O course, the graph above does not visually portray an accurate instance o inuence, because the values are

not weighted. Instead, the graph il lustrates the relationship between responses by each user’s ollower network.

Thereore, to urther examine the efects that ollowers have on inuence, we present the ollowing two graphs

that measure the average number o responses in relation to ollowers.






In the ollowing diagrams, we have utilized the concepts o content and conversation to create equa-

tions or calculating new measurements o inuence. We have dened conversation-related responses as the

total number o replies added to the total number o mentions (@r+@m), and we have dened content-related

responses as the total number o retweets added to the total number o attributions (@RT+@via). The graphs

below utilize the equations “content/ollowers” and “conversation/ollowers” to illustrate the average number o

responses per ollower o each o t he 12 designated users.






The two graphs above present an interesting theory, in that the social media analysts appear to dominate both

realms o content and conversation, thanks to their ollower network. CNN and Mashable.com also appear high

on the list o u sers that are able to interact well with their ollowers as well as push content easily to others.

Tweets & Responses: Actions and ReactionsWhile the above diagrams suggest that a user’s audience impacts how ideas move around said user

to a large extent, these graphs do not take into account the tweets created by our 12 users, especially in

relation to the responses the tweets generate. Returning to the graph representing the percentage o all

responses, this illustration o inuence is not entirely accurate because it does not account or the relative

amount o content produced. This is especially important since the original tweets are the inuencers that

inspire replies, retweets, etc. Below, we present the same percentages o responses in a graph that weighs

the comparison o responses against the total number o responses o other users.






The weighted graph above illustrates a signicantly diferent measurement o inuence than the previous

diagram. I we were to state that inuence is dictated by how many responses are generated, then we could

certainly argue that Mashable.com is more inuential than CNN Breaking News -- a bold statement, es-

pecially when more than twice as many users ollow cnnbrk than ollow mashable. However, the weighted

response statistics above must be compared to the amount o original tweets that inspired response. We

have provided these statistics in the graph below:






The relationship between the original tweet and any subsequent responses certainly matters. For exam-

ple, even though mashable and aplusk boast similar amounts o reactions (with a diference o 1620 in

avor o mashable), mashable originated more than 2.5 times as many original tweets to inuence those

responses. Thereore, aplusk exerted less efort to achieve near-similar success. Similarly, BarackObama

genereated more than 3 times as many responses in the ten-day period than did MCHammer; however,

MCHammer originated over 8 times as many original tweets, meaning that the much larger efort he ex-

erted was ultimately not as inuential as the efort by BarackObama.

We have addressed the problematic relationship o original tweets and responses by averaging the

statistics in the graphs below. The graphs utilize the equations “conversation/tweets” (@r+@m/tweets)

and “content/tweets” (@RT+@via/tweets):











The measurement o inuence reected in these graphs most likely approaches the most accurate estima-

tion o inuence detailed in this report. To arm this statement, we must return to our Twitter-specic

denition o inuence online: the potential o an action o a user to initiate a urther action by another

user. The two graphs above account or the responses (urther actions) in relation to original tweets (ac-

tions with potential), while still theoretically accounting or the size o each user’s audience. Still, these

graphs do not account or the network o the 12 users’ ollowers, and as such remain signicantly diferent

rom the previous graphs depicting average response per 1,000 ollowers. The optimal situation o maxi-

mum inuence would account or the most ollowers possible executing the most actions. However, it is

entirely possible that one ollower published all o the responses or a given user.

What, thereore, do the discrepencies between original tweets and ollowers tell us about the data?

In the previous ollower graphs, social media analysts held most o the top ranks. Contrarily, in the tweet

graphs, they make up the last three spots in both graphs. On average, the data suggest that social media

analysts receive minimal reward or the efort they exert in maintaining a conversation with their ollow-

ers. For those users that succeed, most news outlets were more successul at having their content pushed

to other users. Celebrities, on the other hand, appear to inspire conversational responses with their ol-

lowers, yet with more success than the analysts.

These graphs suggest many statements based on various relationships o users, data, and platorm.

However, although the graphs above represent relative inuence among the 12 users, by no means do

these diagrams suggest that those ranked last are not inuential. For the most part, a general user on Twit-

ter tends to depend heavily on perceived inuence, whether it be total number o ollowers or the ratio

o ollowers to ollowees. This report, though, attempts to move beyond simple assertions o inuence to

create a better study o inuence on Twitter, supported by new approaches and quantitative data.




2 S t b 2009 18


Future Approaches or Inuence AnalysisThis report strives to inuence other researchers to pursue inuence analysis based not solely on

ollowers but also on the relationship between ollowers and content, and the interaction o both in Twit-

ter’s system. Although we analyze how actions (responses to a user) represent the inuence o a user, our

study is limited by sample size, time range, and the ability to collect data. For instance, we hope in the u-

ture to develop a more complex algorithm that accounts or the combined inuence o both ollowers and

responses. We were not able to calculate user growth rate nor measure the number o responses per exact

original tweet. Also, given that this report studies inuence on Twitter, we cannot account or any external

inuence with respect to each user in our sample.

Though we admit our limitations, along with this report we are publishing a comprehensive vi-

sualization that marks each original tweet and each response (reply, retweet, and mention) along our

10-day timeline*. The graph specically shows density as a actor o inuence over time or the 2,143

original tweets and 90,130 responses related to our dozen users. While our graph does not provides la-

bels or tweet, time, etc., we encourage individual exploration o the data presented in the visualization.

The density o data varies considerably per user and per tweet. While we cannot assign each reply, retweet,

and mention to a specic original tweet, we can at least determine certain patterns o density per any giv-

en tweet. The two excerpts above reect the diference in density o responses that a certain tweet might

generate. By tracking the density o responses over time, we hope to inspire urther research into models

o inuence and web ecology as a whole.

* http://www.webecologyproject.org/wp-content/uploads/2009/09/bigdata-large-nal.jpg

http://www.webecologyproject.org/wp-content/uploads/2009/09/bigdata-large-final.jpg

http://www.webecologyproject.org/wp-content/uploads/2009/09/bigdata-large-final.jpg

Documents

The Influentials: New approaches for Analyzing Influence on Twitter