Modeling Information Seeking Behavior in Social Media Eugene Agichtein Emory University

2 Intelligent Information Access Lab (IRLab) Qi Guo (2 nd year Phd) Yandong Liu (2 nd year Phd) Ablimit Aji (1 st year PhD) Text and data mining Modeling information seeking behavior Web search and social media search Tools for medical informatics and public health Supported by: External collaborators: - Beth Buffalo (Neurology) - Charlie Clarke (Waterloo) - Ernie Garcia (Radiology) - Phil Wolff (Psychology) - Hongyuan Zha (GaTech)

3 Information sharing: blogs, forums, discussions Search logs: queries, clicks Client-side behavior: Gaze tracking, mouse movement, scrolling Online Behavior and Interactions

Research Overview 4 Social media Health Informatics Cognitive Diagnostics Intelligent search Discover Models of Behavior (machine learning/data mining)

Applications that Affect Millions Search: ranking, evaluation, advertising, search interfaces, medical search (clinicians, patients)  Collaboratively generated content: searcher intent, success, expertise, content quality Health informatics: self reporting of drug side effects, co-morbidity, outreach/education Automatic cognitive diagnostics: stress, frustration, Alzheimer’s, Parkinson's, ADHD, …. 5

6

(Text) Social Media Today Published: 4Gb/day Social Media: 10Gb/Day Technorati+Blogpulse 120M blogs 2M posts/day Twitter: since 11/07: 2M users 3M msgs/day Facebook/Myspace: M users Avg 19 m/day Yahoo Answers: 90M users, 20M questions, 400M answers [Data from Andrew Tomkins, SSM2008 Keynote] Yes, we could read your blog. Or, you could tell us about your day

8

9 Total time: 7-10 minutes, active “work”

Someone must know this…

11 +1 minute

+7 hours: perfect answer

Update (2/15/2009) 13

14

15

Finding Information Online (Revisited) 16 Next generation of search: Algorithmically-mediated information exchange CQA (collaborative question answering): Realistic information exchange Searching archives Train NLP, IR, QA systems Study of social behavior, norms Content quality, asker satisfaction Current and future work

(Some) Related Work Adamic et al., WWW 2007, WWW 2008: –Expertise sharing, network structure Elsas et al., SIGIR 2008: –Blog search Glance et al.: –Blog Pulse, popularity, information sharing Harper et al., CHI 2008, 2009: –Answer quality across multiple CQA sites Kraut et al.: –community participation Kumar et al., WWW 2004, KDD 2008, …: –Information diffusion in blogspace, network evolution SIGIR 2009 Workshop on Searching Social Media 17

Finding High Quality Content in SM Well-written Interesting Relevant (answer) Factually correct Popular? Provocative? Useful? 18 As judged by professional editors E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, Finding High Quality Content in Social Media, in WSDM 2008

19 Social Media Content Quality E. Agichtein, C. Castillo, D. Donato, A. Gionis, G. Mishne, Finding High Quality Content in Social Media, WSDM 2008 quality

20 20

21 How do Question and Answer Quality relate?

22 22

23 23

24 24

25 25

26Community

27 Link Analysis for Authority Estimation Question 1 Question 2 Answer 5 Answer 1 Answer 2 Answer 4 Answer 3 User 1 User 2 User 3 User 6 User 4 User 5 Answer 6 Question 3 User 1 User 2 User 3 User 6 User 4 User 5 Hub (asker) Authority (answerer)

28 Qualitative Observations HITS effective   HITS ineffective

29 29 Random forest classifier

Result 1: Identifying High Quality Questions 30

Top Features for Question Classification Asker popularity (“stars”) Punctuation density Question category Page views KL Divergence from reference LM 31

Identifying High Quality Answers 32

Top Features for Answer Classification Answer length Community ratings Answerer reputation Word overlap Kincaid readability score 33

Finding Information Online (Revisited) 34 Next generation of search: human-machine-human CQA: a case study in complex IR Content quality Asker satisfaction Understanding the interactions

Dimensions of “Quality” Well-written Interesting Relevant (answer) Factually correct Popular? Timely? Provocative? Useful? 35 As judged by the asker (or community)

Are Editor Labels “Meaningful” for CGC? Information seeking process: want to find useful information about topic with incomplete knowledge –N. Belkin: “Anomalous states of knowledge” Want to model directly if user found satisfactory information Specific (amenable) case: CQA

37 Yahoo! Answers: The Good News Active community of millions of users in many countries and languages Effective for subjective information needs –Great forum for socialization/chat Can be invaluable for hard-to-find information not available on the web

38

39 Yahoo! Answers: The Bad News May have to wait a long time to get a satisfactory answer May never obtain a satisfying answer 1. FIFA World Cup 2. Optical 3. Poetry 4. Football (American) 5. Soccer 6. Medicine 7. Winter Sports 8. Special Education 9. General Health Care 10. Outdoor Recreation Time to close a question (hours)

40 Predicting Asker Satisfaction Given a question submitted by an asker in CQA, predict whether the user will be satisfied with the answers contributed by the community. –“Satisfied” : The asker has closed the question AND Selected the best answer AND Rated best answer >= 3 “stars” (# not important) –Else, “Unsatisfied Yandong Liu Jiang Bian Y. Liu, J. Bian, and E. Agichtein, in SIGIR 2008

41 ASP: Asker Satisfaction Prediction asker is satisfied asker is not satisfied Text Category Answerer History Asker History Answer Question Wikipedia News Classifier

42 Experimental Setup: Data Crawled from Yahoo! Answers in early 2008 QuestionsAnswersAskersCategories% Satisfied 216,1701,963,615158, % “Anonymized” dataset available at: 1/2009: Yahoo! Webscope : “Comprehensive” Answers dataset: ~5M questions & answers.

43 Satisfaction by Topic TopicQuestionsAnswersA per QSatisfiedAsker rating Time to close by asker 2006 FIFA World Cup , % minutes Mental Health % days Mathematics % minutes Diet & Fitness % days

44 Satisfaction Prediction: Human Judges Truth: asker’s rating A random sample of 130 questions Researchers –Agreement: 0.82 F1: 0.45  2P*R/(P+R) Amazon Mechanical Turk –Five workers per question. –Agreement: 0.9 F1: 0.61 –Best when at least 4 out of 5 raters agree

45 Performance: ASP vs. Humans (F1, Satisfied) ClassifierWith TextWithout TextSelected Features ASP_SVM ASP_C ASP_RandomForest ASP_Boosting0.67 ASP_NB Best Human Perf0.61 Baseline (random) 0.66 ASP is significantly more effective than humans Human F1 is lower than the random baseline!

46 Top Features by Information Gain 0.14 Q: Askers’ previous rating 0.14 Q: Average past rating by asker 0.10 UH: Member since (interval) 0.05 UH: Average # answers for by past Q 0.05 UH: Previous Q resolved for the asker 0.04 CA: Average asker rating for category 0.04 UH: Total number of answers received …

47 “Offline” vs. “Online” Prediction Offline prediction (AFTER answers arrive) –All features( question, answer, asker & category) –F1: 0.77 Online prediction (BEFORE question posted) –NO answer features –Only asker history and question features (stars, #comments, sum of votes … ) –F1: 0.74

48 Personalized Prediction of Satisfaction Same information != same usefulness for different searchers! Personalization vs. “Groupization”? Y. Liu and E. Agichtein, You've Got Answers: Personalized Models for Predicting Success in Community Question Answering, ACL 2008

49 Example Personalized Models

Outline 50 Next generation of search: Algorithmically mediated information exchange CQA: a case study in complex IR Content quality Asker satisfaction

Current Work (in Progress) Partially supervised models of expertise (Bian et al., WWW 2009) Real-time CQA Sentiment, temporal sensitivity analysis Understanding Social Media dynamics

Answer Arrival 52

Exponential Decay Model [Lerman 2007]

Factors Influencing Dynamics

Example: Answer Arrival | Category

Subjectivity

Answer, Rating Arrival

Preliminary Results: Modeling SM Dynamics for Real-Time Classification Adapt SM dynamics models to classification e.g.: predict ratings  feature value:

Outline 59 Next generation of search: Algorithmically mediated information exchange CQA: a case study in complex IR Content quality Asker satisfaction Understanding social media dynamics

60 Goal: Query Processing over Web and Social Systems 60

61 Takeaways Robust machine learning over behavior data  system improvements, insights into behavior Contextualized models for NLP and text mining  system improvements, insights into interactions Mining social media: potential for transformative impact for IR, sociology, psychology, medical informatics, public health, …

References Modeling web search behavior [SIGIR 2006, 2007] Estimating content quality [WSDM 2008] Estimating contributor authority [CIKM 2007] Searching CQA archives [WWW 2008, WWW 2009] Inferring asker intent [EMNLP 2008] Predicting satisfaction [SIGIR 2008, ACL 2008, TKDE] Coping with spam [AIRWeb 2008] More information, datasets, papers, slides: