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Governmental trust final report_ver.1.0

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Detecting the level of trust in government using social media analysis - case study of Korean and US government

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Governmental trust final report_ver.1.0

  1. 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. 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. 3. INTRODUCTION 01 1. Research background 2. Research problem 3. Definition of terms
  4. 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. 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. 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. 7. RELATED WORK and IMPLICATION of this study 02 1. Related works 2. Implication of this study
  8. 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. 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. 10. RESEARCH METHODOLOGY 03 1. Overall phase 2. Data description
  11. 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. 12. Overall phase03 12
  13. 13. Overall phase03 13
  14. 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. 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. 16. CHANGES and INCORPORATED FEEDBACK 04 1. English tweet filtering 2. Expansion of trust related words
  17. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 31. 05Discussion There is a certain period when topics are relatively not “hot issue” 31
  32. 32. 05Things to be Done 32 • Detailed Analysis of Topic Modeling Results
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  34. 34. THANK YOU TEAM. 홍수린(문헌정보 석사 2, 2014321156) 안주영(문헌정보 석사 2, 2015311099) Contact : lynnn.hong@gmail.com juyoung228@gmail.com

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