Classiﬁca(on of Twi/er Accounts
into Targe(ng Accounts
and Non-targe(ng Accounts
Hikaru Takemura
FOLIO, Inc.
Keishi Tajima
Kyoto UniversityBackground‣ Twi/er is s4ll the largest microblogging service whichhas 310M monthly ac4ve users.
‣ The most dis4nc4ve feature of TwiDer is the mechanismof follow.

followerfollowee

Background‣ TwiDer is used for various purposes.

dissemina(onof latest news

communica(onwith friends

publica(onof some messages

announcements
to some members

public discussion
on speciﬁc topics

Background‣ TwiDer is used for various purposes.

dissemina(onof latest news

publica(onof some messages

non-targe(ng accounts (to the general public)

communica(onwith friends

announcements
to some members

public discussion
on speciﬁc topics

targe(ng accounts (to speciﬁc people)

Background‣ The general public depends on the context.
‣ E.g., a Japanese news account is:‣ a non-targe(ng account when we assume "thegeneral public" is the set of Twi/er users in Japan.
‣ a targe(ng account when we assume "the generalpublic" is the set of all Twi/er users in the world.

Our Goal‣ We propose a method for classifying TwiDer accounts
into targe(ng accounts and non-targe(ng accounts.
‣ Our method can be useful in tweet search systems.
‣ e.g., The results of a query "iPhone6s" can beclassiﬁed into public news and technical informa4on.
non-targe(ng accounts
iPhone6s will bereleased in October!

targe(ng accounts
Google map SDK is
not working in iPhone6s.

Unusual Consistency‣ We measure unusual consistency of a set S.
S: a set of the followers of an account.

‣ We ﬁnd some common proper4es of the followers, andcompute how much a user set with such a consistency
deviates from a random sample from the given universeof TwiDer users.
XX
X

X
X

X

X

X

X

the followers

a random sample
from the given universe

Unusual Consistency‣ E.g., an account dissemina4ng weather report in Tokyo‣ The followers have a common property: living in Tokyo.
‣ Unusual consistency is high.
‣ E.g., an account dissemina4ng worldwide news
‣ The followers have no unusually common proper4es.
‣ Unusual consistency is low.
weather report in Tokyo

TokyoTokyoTokyo

worldwide news

Unusual Consistency‣ High unusual consistency does not imply thatthe followers are similar to each other in all respects.
‣ e.g., most followers of an account dissemina4ng
weather report in Tokyo have only one common
property (living in Tokyo), but are dissimilar to eachother in the other aspects.

living in Tokyo
engeneer
25 years oldliving in Tokyo
teacher
40 years oldliving in Tokyo
student
18 years old

Two Types of Proper(es‣ Two types of proper4es of the followers:(1) common terms in their proﬁles or loca4on informa4on(2) their common followees(1) example of common terms(2) example of common followeers

weather report in Tokyoa member another member"Tokyo""Tokyo""Tokyo"

Two Ways to Measure Consistency Scores‣ For each Sx: { e ∈ S | e has the property X }, we measure
how unusual it is for S to include the subset that is asconsistent as Sx in two ways.
‣ Probabilis(c Model: based on the probability that a setof the size |S| randomly sampled from the universe
includes a subset that is as consistent as Sx.
‣ Diﬀerence Model: using the diﬀerence between thecover ra4o of X in S and in the universe.

Classify Accounts by Using Unusual Consistency‣ Finally, we classify accounts into targe(ng acocunts andnon-targe(ng accounts by using ct(S) and cf(S).
ct(S): unusual consistency of S computed by using common terms
cf(S): unusual consistency of S computed by using common followees‣ We compared the following methods.
‣ compare max({ ct(S), cf(S) }) with θ‣ compare ct(S) + cf(S) with θ‣ construct a SVM by using the two scores‣ construct a decision tree by using the two scores

Experimental Data Set‣ We obtained 1,000 TwiDer accounts whose 4mezone isJapan.
‣ We hired six TwiDer users as assessors, and asked themto annonate whether the account is pos(ng messages
to the general public of Twi/er users in Japan or not.
‣ We classiﬁed the 1,000 accounts by the majority vote,and selected 90 accounts from each class as the data set.

Experimental Results‣ Accuracy of our method only with either common terms orcommon followees
Probablis(c Model Diﬀerence Modelcommon terms

0.861

0.850

common followees

0.828

0.833

‣ In our method, common terms works beDer than commonfollowees.
‣ From now on, we use‣ Probabilis4c Model for common terms, and‣ Diﬀerence Model for common followees.

Experimental Results‣ Distribu4on of two scores of targe4ng and non-targe4ng
accounts
non-targe(ng accountcommon followees

common followees

targe(ng accountcommon terms

common terms‣ Two scores of targe4ng accounts have only weak posi4ve
correla4on.

Experimental Results‣ Accuracy of four methods combining two scores‣ Baselines‣ follower: compare the number of followers with θ‣ SVM: a binary SVM whose features are the maximum coverra4o of common terms and common followees
Baselines

Proposed Methods

followerSVM

maxsumSVM

decision
tree

0.878

0.828

0.856

0.872

0.944

0.906

‣ The simple follower method achieves high accuracy 0.878.
‣ Two of our methods achieve even higher accuracy 0.944 and0.906.

Conclusion‣ We proposed a method for classifying TwiDer accounts
into targe(ng accounts and non-targe(ng accounts.
‣ Our method found common proper4es of the followers,
and calculated how much a user set with such aconsistency deviates from a random sample from thegiven universe of TwiDer users.
‣ Our method using SVM achieved the highest accuracy
0.944.