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SNS Retrieval Based on User Profile Estimation
Using Transfer Learning from Web Search
Daisuke Kataoka

Keishi Tajima

Graduate School of Informatics
Kyoto University
Kyoto, Japankataoka@dl.soc.i.kyoto-u.ac.jp

Graduate School of Informatics
Kyoto University
Kyoto, Japantajima@i.kyoto-u.ac.jp

Abstract—In this paper, we propose a method of retrieving
posts on social networking services (SNSs) by specifying a pairof queries: a topic query and an entity query. A topic query
specifies the topic of the posts to retrieve (e.g., “iPhone”) and anentity query specifies the type of users who posted them (e.g.,“students”). In the existing search systems for SNS posts, we
can specify topics of posts by keywords, but we cannot specify
types of users. Even if we include keywords specifying types
of users in a query, such keywords are not usually included in
tweets or user profile data. In our method, we estimate types ofusers by learning vocabulary whose appearance is correlatedwith specific types of users. We learn it from the datasets
obtained through Web search. We retrieve Web documents
through the search with a keyword specifying the type of users(e.g., “student”), and we also retrieve Web documents by using
a keyword specifying its opposite (e.g., “adult”). We regard the
documents retrieved by these queries as positive and negative
examples of documents describing the target type, and we traina model for recognizing users of the given type. We recognize
users of the target type by inputting their posts and theirprofile data into the model. We use Web documents instead of
SNS posts for training the model because the Web has more
documents describing types of people.
Index Terms—microblog, profile estimation, transfer learning

1. Introduction
The wide-spread use of social network services (SNSs)
enables us to broadcast and share information about our
opinions, activities, and statuses. Twitter is one of the mostpopular SNSs, which is regarded as a microblogging service
owing to its length limitation of posts. In Twitter, each post,
which is called a tweet, may contain at most 140 characters. This characteristic has encouraged users to post theiropinions frequently and immediately, and has succeeded ineliciting a wide range of information from users. Companies
have been interested in mining opinions from huge data ofSNSs, in particular, honest opinions about their own eventsor their new products.
One of the most important challenges in mining opinionsfrom SNSs is the incompleteness of the user profile informa-tion, which has caused difficulty in identifying users’ profile(e.g., age or sex) for search systems. A straightforward way
for a company that wants to know opinions of students
on a new product “X” would be to search Twitter for
tweets related to “X” posted by students. A simple keyword
query “X AND students’, however, would result in a low
quality result both in precision and recall because tweets
including the keyword “student” are not necessarily posted
by students, and tweets posted by students do not usually
include the keyword “students”. Even profile data of student
users usually do not include the keyword “students”. If auser simply submit a query “X”, the user have to find tweets
from students by manually scrutinizing their profiles or past
tweets. This example indicates that the incompleteness ofuser profile information in SNS can lead to low recall
and precision, in particular, while targeting users with aparticular type of profile.
In this study, to address this challenge in SNS opinion
mining, we propose a search method that integrates user
profile estimation. Our method enables us to find posts containing a topic (e.g. “iPhone”) and posted by a specific typeof users (e.g., “students”). In our method, a user submits
a pair of queries: a topic query and a profile query. A
topic query specifies the topic of the posts to retrieve (e.g.,“iPhone”) and an entity query specifies the type of users
who posted them (e.g., “student”). In order to estimate types
of users, we train a classifier whose inputs are their posts
and their profile data. To train the classifier, we use dataobtained through Web search. We retrieve Web documentswith queries consisting of keywords given in the entity query(e.g., “student”), and we also retrieve Web documents byusing a keyword specifying the opposite type of people (e.g.,“adult”). We then regard the documents retrieved by these
queries as positive and negative examples of documents
describing the target type, and we train the classifier byusing these data.
There are two reasons why we use Web documents
instead of SNS posts for the training. First, Web pages
are usually longer than SNS posts, and we expect thatWeb pages include more useful vocabulary than SNS posts.
Secondly, the Web has more documents describing typesof people, such as students. For example, if we retrieve

Web documents by using a keyword “student”, the result
would include more documents describing “students”, suchas Wikipedia1 articles, and such pages must contain vocabulary such as “education” or “campus”. On the other
hand, Twitter posts retrieved by using a keyword “student”
would have less useful vocabulary. Even if we retrieve user
profile data including the keyword “student”, we cannot
obtain vocabulary whose appearance is strongly correlatedwith “students” because a user profile data is usually a setof keywords which independently describe several different
aspects of the user.
To demonstrate the effectiveness of our proposed
method, we build a test dataset comprising three pairs ofentity queries (each pair consists of two entity queries
specifying the opposite types of users) and five topic queries
for each of the three entity query pairs. In total, we have
3*2*5=30 combinations of a entity query and a topic query.
We retrieved the top 100 tweets for each topic queries byusing the search function of Twitter, and labeled those tweets
by manually examining if they match with the entity queries.
The results of experiments on this dataset showed thatour proposed method outperforms the baseline method in
the most cases. We also compared the performance of our
method with changing the number of documents used in
the training, and the result shows that the optimal numberof documents for the training is different for each query.
The primary contributions of this research is summarized
as follows:•••First, we introduce the problem of retrieving SNS
posts that are posted by a particular type of users.
Second, we propose an effective method for user
profile estimation based on a classifier trained by thedataset obtained through Web search, which make iteasy to collect datasets for training a classifier.
Third, we constructed a test dataset and demonstrated the effectiveness of our proposed methods.
In particular, we showed that training data obtained
through Web search is superior than that obtained
through Twitter search. We also showed that the
optimal number of documents used for the training
is different for each query.

The remainder of this paper is organized as follows:
Section 2 surveys related work; Section 3 describes our
proposed method in detail. Section 4 demonstrates the effectiveness of our method on our test dataset. Finally, Section
5 presents a summary of this work and conclusions.

2. Related Work
Several SNS research avenues are related to our proposed method. In microblogs, it is often unclear what auser is talking about in a short post, and it is one of themain issues in microblog retrieval. Several studies haveproposed methods of improving the performance of microblog retrieval by using features unique to SNSs, e.g,
1. https://www.wikipedia.org/

followers, replies, hashtags [1], [2], [3], [4], [5]. Luo etal. [2] showed that information specific to Twitter, suchas hashtags and mentions, can improve Twitter retrieval
performance, while Nagmoti et al. [1] used the number offollowers and following-follower ratio as an indicator of theauthority degree of users, and used it for improving theperformance of tweet retrieval.
There have also been many studies on methods for
estimating some attributes of users or posts [6], [7]. Pochampally and Varma [6] proposed a method that determines topics of interest of given Twitter users based on their context
information. Lu et al. [8] proposed a method of identifying
entities indirectly mentioned in SNS posts. Their method
uses information on other mentions in the posts, and alsouses vocabulary taken from Wikipedia articles describing theentities in order to compensate for insufficient informationin short posts. Our method also often uses vocabulary takenfrom Wikipedia pages because we use vocabulary in Web
pages included in the results of Web search with a keyword
in our entity query, and the results of Web search with sucha keyword often rank some Wikipedia pages high.
In our method, profiles of Twitter users are estimated bya classifier trained on a dataset of Web domain. Several studies have tried such knowledge propagation across different
domains [9], [10]. In machine learning, such propagation
is called transfer learning [11]. Transfer learning focuses
on storing knowledge obtained while solving one problem
and applying it to a different but related problem. Peddinti
et al. [12] performed sentiment analysis of Twitter posts
about movies by transferring knowledge from movie review
domain. Rieman et al. [13] proposed a domain adaptation
method that adjusts the observed word counts in the target domain, leaving the source domain model unchanged,
for the problem of applying Facebook user-level language
models to country-level Twitter language.
Relevance feedback [14] or pseudo-relevance feedback(PRF) is a representative method of query expansion, whose
effectiveness has shown also on microblog retrieval [15],[16], [17], [18], [19]. PRF is a derivative of relevance
feedback, where top-ranked documents are regarded as apositive examples of the query. Our proposed method can be
regarded as a variation of PRF because we regard documentsin the results of Web search as positive examples and extract
vocabulary from them. Whiting et al. [20] demonstrated
that PRF can improve retrieval performance. In anotherpaper [16], Whiting et al. also showed that performance of
microblog retrieval can further be improved by combining
PRF with a word-weighting scheme that uses PageRank andtemporal properties of words. Choi and Croft [17] proposeda method for selecting specific time periods for selecting
tweets for PRF in tweet retrieval.
The problem we address in this paper is similar to thatof Kataoka et al. [15]. They also addressed a problem of
retrieving microblog posts by specifying a topic and a user
type. In the method proposed in [15], however, the searcher
needs to provide feedbacks on the search result obtainedby a topic query. On the other hand, our proposed method
employs a classifier trained on the datasets obtained throughWeb Search

1) Choose words
describing the
opposite types

Entity Query
student<" 9&0'-'-=

adult!" #$%&'$($
Search Result

Regression
Model)*%+,$-%./0,+1%2)3$-4563$-2)786%6496:862;「student」「adult」
labels are given 3)

Figure 2. Application Phase
Figure 1. Training Phase1)
Web search.

3. Proposed Method
3.1. Problem settings
2)
In this section, we explain the problem settings in our
research. In our method, a user specifies a pair of queries: a
topic query and an entity query. A topic query specifies the
topic of the posts to retrieve (e.g., “iPhone”), while an entity
query specifies the type of users who posted them (e.g.,“student”). Our purpose is to retrieve documents that include
contents matching with the topic query and are posted byusers matching with the entity query.
The problem in retrieving SNS posts by both a topic
query “iPhone” and an entity query “student” is that posts
written by students rarely include the keyword “student”.
However, the vocabulary related to students (e.g., “examination” and “class”) often appears in the context of the posts,
i.e., in the past tweets and the profile data of the user. Ouridea is that types of users can be estimated by a classifier
whose input is features extracted from the contents of posts
and their context data.
In order to train such a classifier, we use a dataset
obtained through Web search instead of Twitter search. It
is because we expect that Web pages obtained through Web
search include more vocabulary for identifying student users
than tweets obtained through Twitter search. There are two
reasons why we expect it. First, most Web pages are longer
than tweets and include more vocabulary. Second, Web
pages more often include sentences describing students thantweets because tweets more often include conversations,and sentences describing students must include more useful
vocabulary for our classifiers.
Our method consists of two phases: a training phase andan application phase. We will explain the details of these twophases in this section.

3.2. Training Phase
In the first phase, we train a classifier on a dataset
obtained through Web search. This phase consists of thefollowing steps.

3)

4)

Given a keyword describing a type of users, wechoose a keyword describing the opposite type ofusers. Such a keyword is ideally an antonym of thegiven keyword, but it can also be some co-hyponym
when we do not have an antonym. For example,
when we are given a keyword “student”, “adult” isa good candidate.
Submit a query consisting of the given keyword
and the paired query with the negation operator toa Web search engine. For example, when given a
keyword “student”, we submit a query “student adult”. Also submit a query with the keyword andthe paired keyword exchanged, that is, “adult student”.
We retrieve top-n documents in the answers ofthese queries, and use them as the positive andnegative examples, respectively, for the training of
the classifier.
Train a classifier by using the datasets obtained
through the two queries above.

Figure 1 illustrates these steps in the training phase.
In this paper, we assume the user specifies a keyword
specifying a type of user and a keyword specifying the
opposite type, but we can extend it to three or more types.
When retrieving Web pages, we combine a keyword and its
opposite with the negation operator, e.g., “student -adult”. It
is because the result of a simple query “student” may include
many pages related to both students and adult people, e.g.,
pages comparing them, and such pages would become noisedata in the training. In order to eliminate such pages fromthe dataset, we use the query “student -adult”.

3.3. Application Phase
Next, we explain the second phase of our method: theapplication phase. In the application phase, we retrieve
candidate posts and their context data, input them to theclassifier trained in the previous phase, and rank the candidate posts based on their likelihood of being posted by
the target type of users. In this paper, we apply our method
to tweets retrieval from Twitter. This phase consists of thefollowing steps:
1)

Takes a topic query and an entity query from the
user.

2)
3)
4)
5)

Submit the topic query to a Twitter search engine,and retrieve tweets containing the keyword in the
topic query as the candidate tweets.
Calculate a document vector for each candidate
tweet.
Input the vectors into the classifier and obtain prediction scores.
Rank the candidate tweets by their prediction
scores.

An example of this process is shown in Figure 2. In thisexample, a topic query is “iPhone” and an entity query is“student”. A document vector for each tweet in the search
result is calculated by the method proposed in Kataoka etal. [15]. The method calculates the vector based on thecontents of the tweet, and also on its context information,
which includes the profile of the user who posted it, thepast tweets of the user, and the profile of its followers. We
omit the details of the method, but it calculates the vector
by a weighted sum of the TF-IDF vectors calculated fromthe contents of the tweet and the context information.
The TF-IDF value for a tweet d and a term t is calculatedby the following formula:w(d, t) = tf d,t · (idf t + 1)

where tf d,t is the number of occurrences of word t (the term
frequency), and idf t is defined by the following formula:
idf t = log2|D|nt

where D is the set of all documents and nt is the numberof documents that include word t.

4. Experiments
We evaluate our method through the following two
experiments.
••

We compare the performance of the method that uses
Web pages for the training and that of the methodthat uses tweets.
We compare both methods with different size of
training dataset.

When we retrieve data for training through Web searchor Twitter search, we retrieve top-n answers. By changing
the value of n, we obtain different size of training datasets.
When n is large, we can use a larger training dataset, whichusually improves the quality of the trained model. On theother hand, when n is large, the quality of top-n results
could be lower. For example, if we use top 10 results of a
Web search, we can expect that all the 10 web pages are
relevant to the query keyword, but if we use top 640 results,
it may include many irrelevant pages. Optimal size of the
training dataset must be determined by the trade-off between
these two factors.
We also examine what kind of words are identified as
effective for each entity query in each method.

4.1. Datasets and Evaluation measure
We chose the following three pairs of entity queries:(student, adult), (man, woman), (Tokyo, Osaka). They correspond to three major demographic attributes of people: age,sex, and region. For each pair of entity queries, we chose
five topic queries, which results in 3*2*5=30 combinationsof an entity query and a topic query. Some examples of topic
and entity queries are shown in the Table 1. For example,
the user intentions of q5 and q6 in Table 1 is collecting
information on reputation of Star Wars among male users
and female users, respectively.
For each topic query, we retrieved top 100 tweets bysubmitting a keyword in the topic query to Twitter REST
API2 , and labeled them by manually examining whether they
match with the each of the entity queries. For example, we
retrieve 100 tweets by a keyword query “smart speaker”,
and classified them into “student”, “adult”, and “unclear”.
When we manually label a tweet for entity queries, weread the contents of the tweet, the profile of the user who
posted it, and also the profile of the followers. We examine
if there are enough clues to determine that the user matcheswith the entity queries, and if there are not enough clues,
we assign the label “unclear”. For example, if the user istalking about “campus” and “dormitory” in the past tweet or
if the proportion of the followers whose profile include theword “university” is large, they are clues for determining
the user is a student. The column P@100 in Table 1 shows
the number of tweets that were determined to be relevant
among the 100 tweets.
When we collect tweets for training datasets, we also
used Twitter REST API3 , and when we collect web pages
for training datasets, we used Bing Web Search API4 .
When we create a document vector of a candidate tweet,
we include the profile of the user who posted it, the profilesof its followers (at most 1200 followers), and its posts before
and after the candidate tweet (at most 25 tweets before thetweet and at most 25 tweets after the tweet). When we createa feature vector of a tweet in the training datasets, we also
include its contexts, while when we create a feature vector ofa web page in the training datasets, we only use its contents.
Table 2 shows a statistics on the vocabulary in the training
datasets obtained from Twitter and the Web.
Random Forests [21] are employed for learning a classifier, which are a combination of decision trees such thateach tree depends on the values of a random vector sampledindependently and with the same distribution for all trees in
the forest.
To evaluate retrieval effectiveness of methods, the normalized Discounted Cumulative Gain (nDCG) was used.
The nDCG is an evaluation index based on multivalued
relevance, which allowed the performance measurements totake account of graded relevance. The nDCG@k only looks
2. https://dev.twitter.com/rest/public
3. https://dev.twitter.com/rest/public
4. https://azure.microsoft.com/ja-jp/services/cognitive-services/bingweb-search-api

TABLE 1. E XAMPLES OF RETRIEVAL TASKS

q1
q2
q3
q4
q5
q6
q7
q8
q9
q10
q11
q12

Topic Query

Entity Query

EntityP@100

Smart speaker
Smart speaker
Monster Hunter
Monster HunterStar Wars
Star Wars
influenza
influenza
Asakusa
Houses of Parliament
Koshi-En
Osaka-Jo Park

student
adultstudent
adult
man
woman
man
woman
Tokyo
Tokyo
Osaka
Osaka

age
age
age
age
sex
sex
sex
sex
regionregionregionregion

2
52
42
58
59
35
34
46
31
31
16
42

TABLE 2. C OMPARISON OF VOCABULARYTwitterWeb

AverageMax

Min

2177.61147.9

4607
14215

62
1

at the top k ranks and is an index obtained by normalizing
DCG@k [22]; DCG@k is defined as follows:k∑2reli − 1
DCG@k =
log2 (i + 1)
i=1

(1)

Here, reli is the degree of relevance of the i-th search result.
In this experiment, we set reli = 1 if the i-th document was
relevant, and reli = 0 if it was irrelevant.

4.2. Experiment Details
In our experiment, we compare the following four methods.
1)
2)
3)
4)

Twitter: Training datasets are obtained throughTwitter retrieval
Twitter Neg: We add the negation of the opposite
type keyword to a query of the Twitter
Web: Training datasets are obtained through Web
retrieval
Web Neg: We add the negation of the opposite typekeyword to a query of the Web

In the Twitter Neg method and the Web Neg method,
the query for collecting datasets through Twitter or Web
retrieval consists of two terms, one is the keyword in theentity query and the other is the keyword representing the
opposite type with the negation operator. For example, when
we create a dataset for an entity query “student” , we use aquery “student -adult” in Twitter Neg and Web Neg.
Furthermore, we change the value of n, i.e., the size ofthe training datasets, to 10 ∗ 2i for 0 ≤ i ≤ 6. As explained
before, we expect that the increase of n does not necessarily
lead to acquisition of a better training dataset. Therefore, bychanging n, we discover the optimal value of n with the best
search performance.
The experimental results of our method are shown in
Table 3 to Table 14. The k in the first column of the tables
represent k in nDCG@k , and the numbers 10 to 640 in the
top row represent n. Each table represents the average of theresults of applying each method to 10 queries (combinationsof 5 topic queries and a pair of entity queries corresponding
to either age, sex, or region). Table 3 to Table 6 show theresults of the four methods for the entity query pair for“age”, Table 7 to Table 10 show the results of the four
methods for the entity query pair for “sex”, and Table 12 toTable 14 show the results of the four methods for the entity
query pair for “region”.
We also show these results in the graphs shown in
Figure 3 to Figure 8. The vertical axes of these graphs
represent nDCG@k , and the horizontal axes represent n.
These graphs show that Web Neg outperforms other methods on nDCG@10 and nDCG@30 of the entity query
category “age”, and on nDCG@10 of the entity query
category “region”, while Twitter outperforms other methods
on nDCG@10 and nDCG@30 of the entity query category“sex”. Web outperforms other methods on nDCG@30 of
the entity query category “region”.

4.3. Discussions
This section discusses the results shown above. Our
experiments revealed that the performance of our method
differs for each n and each entity query. However, although
the training datasets obtained through Twitter search havemore vocabulary than the datasets obtained through the Web
search as shown in Table 2, the classifiers trained by thelatter outperformed the classifiers trained by the former forthe entity query categories “age” and “region”.
We examined what words are regarded as effective
features in the Random Forest models by comparing theimportance of the features by the method described in [23].
The importance is calculated through ”gini importance”,
which is defined by the total decrease in node impurity(weighted by the probability of reaching that node, which
is approximated by the proportion of samples reaching thatnode) averaged over all trees of the ensemble.
As an example, we compare the feature importances of
the classifier for “age” categories trained by the four datasets
obtained by the four methods, Web and Web Neg, Twitter,
and Twitter Neg, respectively. In this comparison, we fix
the value of n to 320. The column “Rank” represents theranks of the feature importances and the columns “score”
represent the “gini importance” of each feature. Comparedwith Twitter and Twitter Neg, Web and Web Neg could
find better features that are obviously relevant to “age”
properties of people.
In future work, we plan to automatically tune the valueof n for each entity query because the optimal n is significantly different for each query and each method. Furthermore, the Web domain has a distribution of vocabulary
different from that in the Twitter domain. We are consideringTABLE 3. W EB AGE E NTITY C OMPARISONTABLE 9. T WITTER S EX E NTITY C OMPARISON

k

10

20

40

80

160

320

640

k

10

20

40

80

160

320

640

3
5
10
20
30

0.300
0.301
0.283
0.318
0.333

0.400
0.372
0.397
0.395
0.386

0.347
0.334
0.39
0.383
0.403

0.306
0.319
0.342
0.347
0.376

0.459
0.487
0.446
0.420
0.416

0.530
0.492
0.475
0.448
0.444

0.300
0.326
0.347
0.369
0.386

3
5
10
20
30

0.547
0.546
0.546
0.522
0.526

0.500
0.430
0.427
0.429
0.432

0.335
0.380
0.418
0.401
0.415

0.464
0.435
0.415
0.400
0.418

0.423
0.402
0.435
0.410
0.425

0.489
0.489
0.450
0.422
0.431

0.530
0.508
0.471
0.450
0.482

TABLE 4. W EB N EG AGE E NTITY C OMPARISONTABLE 10. T WITTER N EG S EX E NTITY C OMPARISON

k

10

20

40

80

160

320

640

k

10

20

40

80

160

320

640

3
5
10
20
30

0.230
0.318
0.333
0.340
0.340

0.230
0.265
0.314
0.306
0.306

0.535
0.455
0.415
0.410
0.397

0.353
0.340
0.319
0.323
0.346

0.447
0.450
0.425
0.420
0.429

0.589
0.579
0.522
0.490
0.484

0.577
0.526
0.467
0.423
0.453

3
5
10
20
30

0.506
0.503
0.510
0.520
0.509

0.347
0.372
0.383
0.414
0.433

0.417
0.426
0.421
0.399
0.414

0.406
0.403
0.395
0.386
0.419

0.406
0.39
0.427
0.404
0.421

0.528
0.509
0.443
0.406
0.408

0.377
0.383
0.432
0.429
0.457

TABLE 5. T WITTER AGE E NTITY C OMPARISONTABLE 11. W EB N EG R EGION E NTITY C OMPARISON

k

10

20

40

80

160

320

640

k

10

20

40

80

160

320

640

3
5
10
20
30

0.265
0.303
0.318
0.35
0.37

0.283
0.314
0.35
0.359
0.434

0.323
0.302
0.313
0.322
0.331

0.277
0.322
0.306
0.342
0.352

0.323
0.358
0.365
0.360
0.373

0.323
0.358
0.365
0.360
0.373

0.323
0.358
0.365
0.360
0.373

3
5
10
20
30

0.300
0.312
0.279
0.275
0.292

0.217
0.186
0.238
0.243
0.294

0.470
0.382
0.338
0.307
0.336

0.236
0.240
0.232
0.237
0.308

0.123
0.183
0.178
0.205
0.266

0.212
0.194
0.252
0.260
0.259

0.159
0.156
0.207
0.247
0.248

TABLE 6. T WITTER N EG AGE E NTITY C OMPARISONTABLE 12. W EB R EGION E NTITY C OMPARISON

k

10

20

40

80

160

320

640

k

10

20

40

80

160

320

640

3
5
10
20
30

0.23
0.22
0.24
0.318
0.354

0.27
0.251
0.318
0.35
0.375

0.323
0.343
0.347
0.368
0.371

0.283
0.301
0.328
0.332
0.351

0.323
0.358
0.365
0.360
0.373

0.323
0.358
0.365
0.360
0.373

0.323
0.358
0.365
0.360
0.373

3
5
10
20
30

0.347
0.330
0.323
0.351
0.368

0.353
0.298
0.272
0.286
0.329

0.283
0.286
0.322
0.299
0.324

0.200
0.242
0.267
0.232
0.265

0.170
0.179
0.218
0.211
0.229

0.259
0.257
0.272
0.249
0.259

0.212
0.209
0.246
0.227
0.264

TABLE 7. W EB S EX E NTITY C OMPARISONTABLE 13. T WITTER R EGION E NTITY C OMPARISON

k

10

20

40

80

160

320

640

k

10

20

40

80

160

320

640

3
5
10
20
30

0.517
0.470
0.402
0.401
0.421

0.283
0.287
0.351
0.363
0.380

0.536
0.497
0.495
0.478
0.516

0.311
0.364
0.363
0.405
0.413

0.323
0.358
0.359
0.330
0.337

0.300
0.354
0.401
0.422
0.434

0.477
0.522
0.457
0.453
0.475

3
5
10
20
30

0.253
0.238
0.238
0.247
0.270

0.247
0.247
0.243
0.248
0.278

0.200
0.215
0.206
0.210
0.227

0.141
0.131
0.154
0.196
0.226

0.153
0.195
0.219
0.232
0.257

0.153
0.195
0.219
0.232
0.257

0.130
0.165
0.181
0.230
0.266

TABLE 14. T WITTER N EG R EGION E NTITY C OMPARISONTABLE 8. W EB N EG S EX E NTITY C OMPARISON
k

10

20

40

80

160

320

640

k

10

20

40

80

160

320

640

3
5
10
20
30

0.436
0.414
0.457
0.460
0.461

0.347
0.346
0.352
0.347
0.370

0.377
0.355
0.364
0.375
0.396

0.388
0.419
0.412
0.400
0.411

0.530
0.466
0.442
0.421
0.404

0.359
0.385
0.412
0.432
0.439

0.230
0.306
0.320
0.391
0.410

3
5
10
20
30

0.253
0.238
0.238
0.247
0.270

0.247
0.247
0.243
0.248
0.278

0.200
0.215
0.206
0.210
0.227

0.141
0.131
0.154
0.196
0.226

0.153
0.195
0.219
0.232
0.257

0.153
0.195
0.219
0.232
0.257

0.130
0.165
0.181
0.230
0.266

TABLE 15. F EATURE I MPORTANCES OF AGE M ODELS
Rank

Twitter 320

score

Twitter Neg 320

score

Web 320

score

Web Neg 320

score

1
2
3
4
5
6
7
8
9
10

do
behindwoman
3)existsperson.
become/0.952
0.02810.00560.004290.002440.001470.001460.001230.001230.000742

given
(
like
.
center3/
person
do
Dazai

0.418
0.21960.146
0.02680.023
0.01710.08820.00455
0.00450.0043society
student
reserved

0.538
0.00918
0.03880.02870.02730.01270.00833
0.007390.006140.00545

society
student

0.589
0.0557
0.05390.04120.03270.01130.00907
0.007070.005960.00502

copyright
personwork
includeage
publicreserved
copyright
personpublic
woman
drink
all

to introduce some domain adaptation method, which is often
used in transfer learning for correcting differences in the
distribution of the features between both domains.

[7]

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In this paper, we propose a method of retrieving posts on
SNSs by specifying a topic query and an entity query. Our
method estimates types of users by using a classifier whose
input is the vocabulary in their posts and their profile data.
We obtain datasets for training such classifier through the
Web search by the keyword describing the target type ofusers, such as “student”, instead of Twitter search with that
keyword. It is because we expect that Web pages are longer
than tweets and include more vocabulary describing typesof people. Our experimental results show that the classifierstrained by the dataset consisting of Web pages outperforms
the classifiers trained by the dataset consisting of tweets formany categories of entity queries although the datasets of
Web pages have less vocabulary than the datasets of tweets.
We also confirmed that a larger dataset does not always leadto a better query performance. It is because a larger dataset
including low-ranked pages or tweets may include many
noisy data. In future work, we plan to develop a method of
automatically determining the optimal size of the dataset fora given entity query. We also plan to develop a method of
domain adaptation by utilizing the difference of distributionof vocabulary between Web and Twitter.

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Figure 3. nDCG@10 Age Entity Comparison

Figure 6. nDCG@30 Sex Entity Comparison

Figure 4. nDCG@30 Age Entity Comparison

Figure 7. nDCG@10 Region Entity Comparison

Figure 5. nDCG@10 Sex Entity Comparison

Figure 8. nDCG@30 Region Entity Comparison