Factful: Engaging Taxpayers in the Public Discussion of a Government Budget
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- Factful: Engaging Taxpayers in the Public Discussion of a Government Budget - Juho Kim, Eun-Young Ko, Jonghyuk Jung, Chang Won Lee, Nam Wook Kim, Jihee Kim - Presented at CHI 2015 - We introduce Factful, a web-based annotative article reading interface for supporting budgetary discussions online. It leverages open government data, adds contextual budget information to the article, and supports fact-checking while reading.
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Factful: Engaging Taxpayers in the Public Discussion of a Government Budget
- 1. Factful: Engaging Taxpayers in the Public Discussion of a Government Budget Juho Kim (MIT) Eun-Young Ko (KAIST) Jonghyuk Jung (KAIST) Chang Won Lee (KAIST) Nam Wook Kim (Harvard) Jihee Kim (KAIST)
- 2. Deliberative Democracy citizens’ active participation in decision making and related discussions
- 3. Deliberative Democracy Online Open Government Data UI for Discussion Support Balancer [Munson et al., 2011] ConsiderIt [Kriplean et al., 2012]data.gov.uk,data.gov, data.seoul.go.kr Reflect [Kriplean et al., 2012]
- 4. Government Budget plan for government to best allocate resources But… • hard to comprehend • extremely complex • low interest & awareness
- 5. Improve awareness & understanding of budgetary issues Build interactive systems for civic engagement Leverage open government data
- 6. Understanding Taxpayers’ Challenges • Survey – 182 respondents in Korea – Perception of the government budget + estimation quiz • Semi-structured interviews – 5 taxpayers – 3 experts
- 7. 1. Low awareness and interest in budgetary issues knowledgeable about budget info search for budget info 1 not really 7 very much 3.1 3.2
- 8. 1. Low awareness and interest in budgetary issues interest in the budget budget info is useful 1 not really 7 very much 4.6 4.5
- 9. 1. Low awareness and interest in budgetary issues • Federal police budget estimation [Mean: $800B, Median: $11B, Stdev: 2114] “I feel distant from all the big numbers that don’t really mean anything to me.” $8.8B
- 10. 2. Contextual information matters in opinion formation. “(Taxpayers) sometimes only see their own interests and fail to realize that compromises need to be made.”
- 11. 2. Contextual information matters in opinion formation.
- 12. 2. Contextual information matters in opinion formation. Seeing other programs in the category sometimes affected respondents’ opinion.
- 13. 3. News Outlets: Primary source for learning about budgetary issues • 74% regularly read articles online – U.S. [Purcell et al., 2012]: 50% via news sites, 10% social networks • News articles: more comprehensible, engaging • NONE had attempted to read govt. reports
- 14. 3. News Outlets: Primary source for learning about budgetary issues • Concerned about media biases • Others’ comments help recognize the potential subjectivity, bias, or error in an article • U.S. survey [Purcell et al., 2012] • 37%: commenting important feature to have • 25%: commenting experience
- 16. Factful: Fact-Oriented Budgetary Discussions Online enhanced news reader application contextual budgetary facts reader-initiated fact-checking
- 18. LAYER #1: CONTEXTUAL BUDGET FACTS
- 19. Embedding Contextual Budget Facts
- 20. Automatically Inserted Overview 5-year trend category info category breakdown category detection: fit score based on word hit count
- 21. Most Relevant Budget Programs link to budget program webpage budget name, category, amount program suggestion: for each word in the article, compute TF-IDF score against each program
- 22. Automatic Annotation programs of similar size monetary value annotation with rule-based detector
- 26. do fact-checking request fact-checking Fact-Checking Embedded in the Article read w/ fact- checked result
- 27. Open Government Data Article Text Analysis Budget Data Processing Pipeline • opengov.seoul.go.kr • 76% of all internal documents publicly accessible • 2014: $24B, 13 1st level categories, 4629 individual programs • Article text parsing • Category detection • Program suggestion • Monetary value detection
- 28. Evaluation: with Factful… • H1. Readers will discuss with more fact-based statements. • H2. Readers will discuss with more evidence, and more kinds of evidence. • H3. Readers will become more critical about the article.
- 29. Evaluation Setup Between-subjects, 38 participants Commenting Commenting Fact-checking Commenting Fact-checking Contextual Info Baseline Fact-checking only Factful
- 30. Tasks and Procedures Three articles about Seoul’s budget & policies Pre-Q Read Discuss Post-Q
- 31. Discourse Analysis • 404 comments • Discussion coding: each comment is coded with one of the 25 categories [UnweightedCohen’s 𝜅: 0.613]
- 32. Discussion Quality Assessment • 5 external raters • 10 questions about discussion quality (score between 1-10) • Overall quality • Criteria derived from deliberation lit. [Fishkin & Luskin, 2005] informed, balanced, conscientious, substantive, comprehensive
- 33. With Factful, overall discussion quality was higher. score 6.6 5.67 4.93 1 2 3 4 5 6 7 8 9 10 Baseline Fact-Checking Factful p < 0.05 p < 0.05
- 34. H1. With Factful, discussions contained more relevant, accurate information. stronglyagree Discussants participated in the discussion with more relevant, accurate information. stronglydisagree 6.93 6.27 6.27 1 2 3 4 5 6 7 8 9 10 Baseline Fact-Checking Factful p < 0.05 p < 0.05
- 35. H1. With Factful, participants added more fact-oriented comments...? 2.46 1.77 1.67 0 1 2 3 Baseline Fact-Checking Factful 2.54 1.77 1.75 0 1 2 3 Baseline Fact-Checking Factful # comments with objective supporting arguments # comments that asked for objective information # comments / person
- 36. H2. Factful discussions contained more diverse perspectives and supporting evidence. 6.93 6.00 5.27 1 2 3 4 5 6 7 8 9 10 Baseline Fact-Checking Factful stronglyagreestronglydisagree Discussants participated in the discussion with more diverse perspectives and supporting evidence. p < 0.05 p < 0.05
- 37. H3. With Factful, participants became more critical about the article. 4.03 4.77 5.08 1 2 3 4 5 6 7 Baseline Fact-Checking Factful I trust the content of the article. stronglyagreestronglydisagree p < 0.01 p < 0.05
- 38. H3. With Factful, participants held a more critical view on the article. # comments criticizing the article • 0.8 / person in Factful vs 0 in Baseline # comments criticizing other participants 0.46 0.31 1.25 0 1 2 Baseline Fact-‐Checking Factful # comments / person
- 39. Role of Contextual Information Automated annotations w/ similar sized programs Category overview “Without such information, it would be hard to determine if the given government spending is worth or not.” “It made reading through the article easier, because the budget terms and numbers in the article felt less obscure.”
- 40. Future Work • Live deployment • Crowdsourced fact-checking methods • Generalization – different countries – other datasets
- 41. Improve awareness & understanding of budgetary issues Build interactive systems for civic engagement Leverage open government data Data-driven, social, crowdsourced mechanisms
- 42. BudgetMap: Issue-Driven Navigation for a Government Budget. Nam Wook Kim, Chang Won Lee, Jonghyuk Jung, Eun-Young Ko, Juho Kim, Jihee Kim. CHI 2015 Extended Abstracts. BudgetMap: Issue-Driven Budget Navigation
- 43. Factful: Engaging Taxpayers in the Public Discussion of a Government Budget juhokim@mit.edu budgetwiser.org