BiasTrust : Trusting credible information in presence of human bias

1. BiasTrust: Trusting credible information in presence of human bias V.G.VinodVydiswaran ChengXiangZhai, Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign March 30th, 2012

2. Web content: structured and free-text Are all these pieces of information equally trustable?

3. Blogs and forums give information too Sources may not be “reputed”, but information can still be trusted.

4. Even reputed sources can make mistakes Some sources / claims can be misleading on purpose.

5. Many (manual) fact verification sites…

6. … but, they may not agree!!! • We need to understand / estimate • Source Independence • Source Bias • Source Trustworthiness • Evidence Quality and Semantics: supporting / contradictory evidence • Similarity to other sources, evidence [Pasternack & Roth ’10,’11,’12] [Sondhi, Vydiswaran, & Zhai ’12] [Vydiswaran, Zhai, & Roth,’11a,b]

7. Every coin has two sides People tend to be biased, and may be exposed to only one side of the story Confirmation bias Effects of filter bubble For intelligent choices, it is wiser to also know about the other side What is considered trustworthy may depend on the person’s viewpoint

8. Milk is good for humans… or is it? Milk contains nine essential nutrients… Dairy products add significant amounts of cholesterol and saturated fat to the diet... The protein in milk is high quality, which means it contains all of the essential amino acids or 'building blocks' of protein. Milk proteins, milk sugar, and saturated fat in dairy products pose health risks for children and encourage the development of obesity, diabetes, and heart disease... It is long established that milk supports growth and bone development rbST [man-made bovine growth hormone] has no biological effects in humans. There is no way that bST [naturally-occurring bovine growth hormone] or rbST in milk induces early puberty. Given these evidence docs, users can make a decision Drinking of cow milk has been linked to iron-deficiency anemia in infants and children One outbreak of development of enlarged breasts in boys and premature development of breast buds in girls in Bahrain was traced to ingestion of milk from a cow given continuous estrogen treatment by its owner to ensure uninterrupted milk production.

9. Actors in the trustworthiness story Source Claim Users Drinking milk is good for the body. PETA Dairy Association Evidence The protein in milk is high quality, which means it contains all of the essential amino acids or 'building blocks' of protein. News Corpus Dairy products add significant amounts of cholesterol and saturated fatto the diet... Data Medical sites Forums ClaimVerifier Blogs

10. Building a claim verification system HCI Issues Algorithmic/Computational Issues [KDD’11, ECIR’12] Source Claim How to assign truth values to textual claims? Users Are sources trustworthy? How to present evidence? How to address user bias? Evidence Data/Language Understanding How to build trust models that make use of evidence? [KDD-DMH’11] How to find relevant pieces of evidence ? What kind of data can be utilized? Data ClaimVerifier

11. Does human biases affect trustworthiness? Source Claim Users How to present evidence? How to address user bias? Evidence BiasTrust Data ClaimVerifier

12. Claim verification Traditional search Lookup pieces of evidence only on relevance Users search for a claim Lookup pieces of evidence supporting and opposing the claim Evidence search ClaimVerifier

13. Verifying free-text claims • Original Goal: Given a claim c, find truth value of c. • Modified Goal:Given a claim c, don’t tell me if it is true or not, but show evidence • Simple claims: “Drinking milk is healthy for humans.” • Find relevant evidence documents • Determine polarity • Evaluate trust • Baselines: Popularity, Expert ranking • Via information network (who says what else…)

14. Challenges in presenting evidence Natural Language Understanding Information Retrieval Human Computer Interaction • What is a good evidence? • Simply written, addresses claims directly? • Avoids redundancy? • Helps with polarity classification? • Helps in evaluating trust? • How to present results that best satisfy users? • What do users prefer – information from credible sources or information that closely aligns to their viewpoint? • Does the judgment change if credibility/ bias information is visible to the user?

15. User study Task setup • Subjects asked to learn more about some topic, possibly a “controversial” topic • Subjects are shown quotes (documents) from “experts” on the topic • Expertise varies, is subjective • Perceived expertise varies much more • Subjects are asked to judge if quotes are biased, informative, interesting

16. Many “controversial” topics Health Science Politics Education • Is milk good for you? • Is organic milk healthier? Raw? Flavored? • Does milk cause early puberty? • Are alternative energy sources viable? • Different sources of alternative energy • Israeli – Palestinian Conflict • Statehood? History? Settlements? • International involvement, solution theories • Creationism vs. Evolution? • Global warming

17. User study setup • Setup similar to learning a new topic • Pre-test: Sense bias and knowledge gap • Expose users to general information • Expose users to alternate viewpoints • Post-test: Did bias shift? Why / why not? • Characterize shift by strength of bias during pre-test • Survey to understand key reason for shift

18. User interaction workflow You may find this factoid relevant! Source: Expertise: Pre-test What do you this about this factoid? Mostly agreeSomewhat agreeSomewhat disagreeMostly disagree Interesting?YesNoBiased?YesNoNeutralCan’t say Post-test Show contrast Show similar Quit

19. Contrastive layout Viewpoint 2 Viewpoint 1 Source: Source: Expertise: Expertise: Source: Expertise: Source: Expertise: … … Source: Expertise: Source: Expertise:

20. What happens in background? • Pre-test phase • Fixed questions based on sub-topics • Learn User “Ignorance” model • During experiment phase • Retrieve evidence documents according to User “Ignorance” model • Update User “Ignorance” model • Record credibility judgments for documents • Post-test survey to judge bias shift • Did expertise of sources play a role?

21. Specific Questions What do subjects prefer – information from credible sources or information that closely aligns with their bias? Are (relevance) judgments on documents affected by user bias? Does the judgment change if credibility/ bias information is visible to the user?

22. Current status • User study being planned with human subjects • Pilot interface ready • IRB approval pending • Volunteers welcome! • Contact me at vgvinodv@illinois.edu • Talk to me during this retreat!

23. Summary: Towards building ClaimVerifier • Trustworthiness of information comes up in the context of both traditional and online media (Web 2.0, blogs, tweets, social networks) • Finding trustworthy information is critical. • Algorithmic and Language understanding techniques • Success of ClaimVerifier also depends on how trustworthy information is perceived. • User study to understand effect of human biases on credibility judgments to start soon.