1. This document outlines a research study that analyzes levels of trust in the Korean and US governments using social media data. It describes collecting data from news articles, tweets, and forum comments/replies from both countries between 1995-2015. 2. The study aims to compare expressions of trust in the two governments across different media and over time. It expands the scope of trust-related words beyond direct terms like "trust" and analyzes topics related to government ability, benevolence, and integrity. 3. Preliminary results found differences in how each country discusses their government. Koreans referred more emotionally to specific issues/leaders, while Americans expressed more objective political opinions collectively. The study will continue with

1. Detecting the level of trust in government
using social media analysis
– case study of Korean and US government
2015-2 TSMM research presentation
SuLyn Hong
JuYoung An

2. CONTENTS 1. Introduction
2. Related works and Implication of this study
3. Research methodology
4. Changes and Incorporated feedback
5. Result and Discussion
00

3. INTRODUCTION
01
1. Research background
2. Research problem
3. Definition of terms

4. Research background
• Trust in government is the core power of government.
• It is harder to secure legitimacy and work efficiently than to plan new policy.
• The loss of trust in government increases co-operation cost and acts as obstacle,
so could be a cause of policy failure.
• It would be helpful if analysis of social media to identify public opinion is
conducted on trust in government.
• We will attempt to compare the difference between the opinion and attitude of
Korean and American toward their governments on social media.
01
4

5. Research problem
1. How are the trust and attitude of Korean to the Korean government
expressed in various Social Media? What are public discourses related to
trust in the government?
2. Can social media data be an indicator for measuring the level of trust in
government?
3. How different the Korean’s perspective to the government and the
American’s perspective to the government which has a relatively long
history of democracy?
01
5

6. Definition of terms
• In this study, trust in government is defined to ‘the degree of trust which people
have on governmental performance, in other words, positive expect’. (K M Yang,
2007)
• In the case of Korean Social media, it is hard to differentiate the ‘government’
from ‘present government’ by keyword ‘government’, so in this study, we defined
the target of trust in government as a present government.
• We used a common word ‘government/정부’ as the initial keyword for data
collection, not specific names of administrative agencies.
• In case of American Social media, the target of trust in government is the federal
government which is governed by president.
01
6

7. RELATED WORK and IMPLICATION of this study
02
1. Related works
2. Implication of this study

8. Related works
• H J Son(2005). The studies on trust in government can be categorized to three
kinds; Theoretical discussion about importance of trust in government,
Composition and Measurement of concept on trust in government, Factors which
affects trust in government.
• O'Connor, et al. (2010). Analyzes public opinion measured by vote with emotion
extracted from text data. As a result, it is revealed that there is co-relation
between the frequency of emotional words generated in tweeter and trend of
public opinion. (about 80%)
02
8

9. Implication of this study
• Existing studies on trust in government use questionnaire survey, which costs
high and is limited to small sample. This study has an implication that identifying
trust in government empirically by using big social data.
• It is possible to catch various discussions from social media text, which is
impossible from closed questionnaire.
• In abroad, there are more studies to identify public opinion on government of
political issue than in Korea, but the data source is usually singular, not various,
for example, blog, Facebook or Twitter. (Griffiths, 2004)
• This study suggests a new possibility to an area of study on trust in government
by analyzing opinions of people in various social media channel to identify the
level of trust in government in various aspects.
02
9

10. RESEARCH METHODOLOGY
03
1. Overall phase
2. Data description

11. Overall phase03
• It is hard to find study on extracting emotion about ‘trust’ in text data.
• The important thing is how the emotion is extracted in Social media text.
• We decided to find supplement points while conducting the whole process with
the partial data in advance, and proceed the second experiment again.
• Mainly applying Topic Modeling to identify topics related to government.
• The emotion ‘trust’ should be found by emotion analysis rather than sentiment
analysis, so Topic modeling way will be helpful.
11

12. Overall phase03
12

13. Overall phase03
13

14. Data description03
Meaning of data Data channel Description
1
Opinion of media
which produces
issue
News article
• It is impossible to get perfect objectivity even media deals with news
• Literary style, so it has objectivity than other data
• Korea: KINDS / US: EBSCO American news
• Search query: ‘정부’ (Korea) / ‘government’ (US)
2
Opinion without
establishing
Agenda
Tweet
• Use words related with government as keyword of data collection
• Search query: ‘정부’, ‘정권’ (Korea) / ‘government’, ‘gov(gov’t)’ (US)
• Filtering of english tweet during collection process
3
Opinion with
establishing
Agenda
K
o
News article
+ comment
• Considering the cultural difference of opinion presentation, select
similar data source
• Korea: comment high rank news of ‘Naver news’ of Korea
• US: ‘US Message Board’, ‘Debate Politics’ forums’ US > Politics topicU
S
Forum topic
+ reply
14

15. Data description03
Meaning of data Data channel Date range The # of data
The # of filtered
data
1
Opinion of media
which produces
issue
News article
Ko
1995-01 ~ 2015-06
(10 years)
668,820 565,409
US
1995-01 ~ 2015-06
(10 years)
532,986 207,704
2
Opinion without
establishing
Agenda
Tweet
Ko
2009-07 ~ 2015-06
(6 years)
8,393,551 57,668,422
US
2009-07 ~ 2015-06
(6 years)
57,683,814 17,360,556
3
Opinion with
establishing
Agenda
K
o
News article
+ comment
2006-07-01 ~ 2015-07-03
(9 years)
6,727 /
6,826,141
6,490 /
2,763,721
U
S
Forum topic
+ reply
2006-07 ~ 2015-06
(9 years)
44,570 /
1,776,857
32,896 /
813,333
15

16. CHANGES and INCORPORATED FEEDBACK
04
1. English tweet filtering
2. Expansion of trust related words

17. English tweet filtering
Supplement point 1:
• In the case of English tweet, because English is used in many worlds, method to
differentiate the tweet related to the US government is needed.
04
17

18. English tweet filtering
Complementary measures:
In English tweet:
1. Deleting data contains ‘USA’, ’US’, ’Obama’
2. Ordering the word frequency to 100th, select the words represent other
countries
3. Selection 30 noise words considering meaning of word
In the database:
1. Lowercase tweet text body
2. Select text does not contain ‘Obama’, ‘bush’, ‘us’, ‘usa’, ‘united state’
3. Delete tweets contains noise words
04
18

19. English tweet filtering
Excluded words:
uk , british , scottish , bbc , britain, india, china, chinese, greece, delhi, nigeria,
canada, pakistan, hong kong, italian, italy, japan, Japanese, EU, french, iraq, iraqi,
syrian, iran, world news, world issue, global news, global issue, egypt, korea
deleted 4,703,883 (the rest: 58,759,436)
04
19

20. Supplement point 2:
• A lot of data are disappeared when the data are filtered by words related trust:
Need to expand scope of words
• Clear criteria when select topics related to trust is needed
• How we classify the words have neutral emotion such as just ‘trust’?
04
20
Expansion of trust related words

21. Complementary measures:
1. Quantitative expansion: Using word2vec, train data of phase 1, after than select
related word from word2vec result extracted by existing trust-related words as
seed words
2. Secure trustworthy
• Select the number of words have even distribution by applying reliable
governmental trust effect factor model.
• Think about the way separate positive/negative words
04
21
Expansion of trust related words

22. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management
review, 20(3), 709-734.
04
22
Expansion of trust related words
Ability
(능력)
Benevolence
(호의)
Integrity
(공정성)
• We did not simply select the common words
such as 'believe', 'trust', but selected the
words according to the 3 factors affect to
trust of government.
• Examples:
o Ability: disappoint, fool, incompetent,
reliable, solve
o Benevolence: help, vested right, mercy
, welfare, protect
o Integrity: transparent, conspiracy, manip
ulate, lie, fraud

23. 04
23
Expansion of trust related words
‘Trust’ related term list
Korean English
Ability
능력
멍청, 어리석, 바보, 순진, 악마, 철없, 무
모, 야만인, 어리석음, 경멸, 흰밥, 멋있,
부끄럽, 불쌍, 쓸데없, 괴짜, 괴상, 똑똑,
악동, 대단, …
dissapoin, outwit, entertain, ridicule, annoyance,
fool, forget, stupid, dreadful, unenforceable, exe
mpt, inconceivable, really, doubt, incompetent, c
ompetent, inept, insensitive, ineffective, corrupt,
…
Benevo
-lence
호의
특권, 권력, 밥그릇, 지역주의, 사익, 정치
권력, 구체제, 당파, 수구, 이기주의, 스스
로, 이념, 정당, 소수, 지배층, 정치, 헤게
모니, 계파, 대의, 대변자, …
help, assist, aid, encourage, assistance, desperat
ely, collaborate, incentive, relief, wean, insure, en
able, boost, induce, persuade, protect, distress, n
eedy, grant, rescue, …
Integrity
진실성
불신, 불신감, 불안감, 실망감, 반감, 증오
심, 반목, 분노, 피로감, 적대감, 신뢰, 적
개심, 갈등, 혐오감, 공포감, 혼란, 도덕적
해이, 불협화음, 불화, 증오감, …
plot, scheme, fraud, bribery, charge, felony, mur
der, forgery, indictment, allege, racketeering, ma
sterminding, case, misdemeanors, collusion, mzo
udi, obstruction, fraud, fraudulent, cheat, …

24. Result and Discussion
05
1. Difference of expression (in terms of medium/nation)
2. Difference of amount of data included in three
dimension
(ability / benevolence / integrity)
3. Difference of time series analysis
4. Discussion

25. Result 01: Difference of expression
1. News article
• Similarity: Most words are ‘noun’s which indicate a certain object. There are just
a few topics related to trust.
2. Tweet
• Similarity: There are a lot of words indicate administration.
• Difference: In Korea, people talk about certain person of the government. 27%
of tweet has words indicate the president of the government.
In America, the objective words indicate government such as ‘government’ are
used. Only 5% of tweet has words indicate the president of the government.
3. Forum comment
• Difference: It could be difference from difference of data source, but in general,
the words from English data are more objective than the words form Korean
data. The most emotional word in English data is ‘stupid’.
05
25

26. Result 02: Difference of amount of data
1. The difference of mention directly related to trust according to medium
o In American news, there are relatively few mention directly related to trust,
so the amount of filtered data is smaller than in Korean news.
o In Forum comment and tweet, because of the diversity of expression, the
amount of filtered data is smaller than the amount of filtered data from
other mediums.
2. In both America and Korea, there are more contents related to the ‘benevolence’
of the government than contents related to the other factors of trust; It seems
that both nation’s public has a big expectation on welfare or tax.
3. In Korea, there are more contents related to ability than contents related to
integrity, in America, it is opposite. Korean public refer on corruption and fraud,
but American public are more interested in ‘ability’ of the government.
05
26

27. Result 02: Difference of amount of data05
27
0
0.2
0.4
0.6
0.8
1
en ko en ko en ko en ko
forum_comment forum_topic news tweet
Sum of filtered rate
Sum of Rate of Ability
Sum of Rate of Benevolence
Sum of Rate of Integrity

28. Result 03: Difference of time series analysis
1. In Korea, specific events have a big impact on topic fluctuations. Big tragedies are
strongly related to government’s ‘ability’.
05
28
MERS
Sewol ferry

29. Result 03: Difference of time series analysis05
29
2. In the US, there’s a little fluctuation among topics. The most fluctuated topic
among trust related topics is related with ‘Obamacare’.
3. The sharpest rising topic among the whole US forum data is about ‘government
shutdown’(the level of topical distribution is 3.7). The expressions of the US forum
comments are relatively objective.

30. 05
30
Discussion
• Overall, in Korea, public responds directly (and emotionally) to
social/political issues, but in America, public tends to collectively
express their own opinion about the issues (not emotional response)
and focuses on the political opinion.
• In Korean forum data, there is a certain period when topics are not
“hot issue.” In other words, public does not discuss those topics
frequently.

31. 05Discussion
There is a certain
period when topics are
relatively not “hot issue”
31

32. 05Things to be Done
32
• Detailed Analysis of Topic Modeling Results

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34. THANK YOU
TEAM.
홍수린(문헌정보 석사 2, 2014321156)
안주영(문헌정보 석사 2, 2015311099)
Contact :
lynnn.hong@gmail.com
juyoung228@gmail.com