A QUESTION OF COMPLEXITY − MEASURING THEMATURITY OF ONLINE ENQUIRY COMMUNITIESGRÉGOIRE BUREL1 AND YULAN HE21Knowledge Medi...
OUTLINEA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES- Question Complexity and Community ...
ENQUIRY COMMUNITIES“Enquiry Communities are communitiescomposed of askers and answerers lookingfor solutions to particular...
ENQUIRY COMMUNITIES“Enquiry Communities are communitiescomposed of askers and answerers lookingfor solutions to particular...
ENQUIRY COMMUNITIES“Enquiry Communities are communitiescomposed of askers and answerers lookingfor solutions to particular...
A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES- Server Fault (SF):- A web based enquiry I...
ENQUIRY COMMUNITIES- Enquiry Communities Needs (Rowe et al. 2011, Burelet al. 2012):- Community Managers:- Make sure that ...
ISSUES AND MOTIVATION- Enquiry Communities Needs:- Questions have uneven complexity:- Difficulty to identify how hard are ...
IDENTIFYING COMPLEX QUESTIONS AND MATURECOMMUNITIESHow user, content, thread and platform features affect contentcomplexit...
CONTRIBUTIONSHow user, content, thread and platform features affect quality contentcomplexity? How can we use content comp...
LITERATUREHow user, content, thread and platform features affect quality contentcomplexity? How can we use content complex...
QUESTION COMPLEXITY AND MATURITY- Definition 1 (Question Complexity):- Question complexity is a value representing thediff...
QUESTION COMPLEXITY AND MATURITY- Definition 1 (Question Complexity):- Question complexity is a value representing thediff...
QUESTION COMPLEXITY AND MATURITY- Hypothesis 1 (Temporality):- For a given user, question complexity increases as a functi...
QUESTION COMPLEXITY AND MATURITY- Hypothesis 1 (Temporality):- For a given user, question complexity increases as a functi...
QUESTION COMPLEXITY AND MATURITY- Hypothesis 1 (Temporality):- For a given user, question complexity increases as a functi...
HYPOTHESES VALIDATION- Methodology:1. Select 510 question pairs based on the previous hypotheses:- Questions from early an...
HYPOTHESES VALIDATION- Methodology:1. Select 510 question pairs based on the previous hypotheses:- Questions from early an...
FEATURES1. User Features (Askers and Answerers):– Represents the characteristics and reputation ofaskers and answerers (e....
FEATURESA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIESType FeaturesAskers Community Age (...
QUESTION COMPLEXITY PREDICTION- Experimental Setting:1. Split the annotated questions in complexand non-complex questions ...
COMPLEXITY PREDICTION RESULTSA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
COMPLEXITY PREDICTION RESULTS- Best Answer Identification (F1 0.60):– Baseline Models:- Asker’s age in a community correla...
FEATURES RANKING- Features Ranking:1. For each feature, Information Gain Ratio(IGR), Correlation Feature Selection (CFS)an...
FEATURES RANKING RESULTSA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
FEATURES RANKING RESULTS- Features Impact Comparison:– Asker’s community age and topical focus are themost important featu...
BEST MODEL RESULTS- Best Model (F1 0.64):– The best model is obtainedwhen using CFS, the selectedfeatures are:1. Asker’s q...
COMMUNITY MATURITY- Maturity Measure:- Experimental Setting:1. Calculate question complexity based on the proportion ofcom...
COMMUNITY MATURITY RESULTSA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIESUsers Topics/Comm...
COMMUNITY MATURITY RESULTS- User Evolution:- Maturity increases over time.- Maturity drop can be explained by the drop of ...
FUTURE WORK- Perform similar analysis on other EnquiryCommunities:- Confirm our results on additional datasets.- Derive a ...
CONCLUSION- We showed that current health measures do not help in identifyingcommunities that become more topic proficient...
QUESTIONS?Web: http://evhart.online.frEmail: g.burel@open.ac.ukTwitter: @evhart@wwwA QUESTION OF COMPLEXITY − MEASURING TH...
REFERENCES- Rowe, M., Alani, H., Angeletou, S., and Burel, G. Report on social, technical and corporateneeds in online com...
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A Question of Complexity - Measuring the Maturity of Online Enquiry Communities

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Online enquiry communities such as Question Answering (Q&A) websites allow people to seek answers to all kind of questions. With the growing popularity of such platforms, it is important for community managers to constantly mon- itor the performance of their communities. Although differ- ent metrics have been proposed for tracking the evolution of such communities, maturity, the process in which communities become more topic proficient over time, has been largely ignored despite its potential to help in identifying robust communities. In this paper, we interpret community maturity as the proportion of complex questions in a community at a given time. We use the Server Fault (SF) community, a Question Answering (Q&A) community of system administrators, as our case study and perform analysis on question complexity, the level of expertise required to answer a question. We show that question complexity depends on both the length of involvement and the level of contributions of the users who post questions within their community. We extract features relating to askers, answerers, questions and answers, and analyse which features are strongly correlated with question complexity. Although our findings highlight the difficulty of automatically identifying question complexity, we found that complexity is more influenced by both the topical focus and the length of community involvement of askers. Following the identification of question complexity, we define a measure of maturity and analyse the evolution of different topical communities. Our results show that different topical communities show different maturity patterns. Some communities show a high maturity at the beginning while others exhibit slow maturity rate.

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  • Definition of online communities
  • A Question of Complexity - Measuring the Maturity of Online Enquiry Communities

    1. 1. A QUESTION OF COMPLEXITY − MEASURING THEMATURITY OF ONLINE ENQUIRY COMMUNITIESGRÉGOIRE BUREL1 AND YULAN HE21Knowledge Media Institute, The Open University, Milton Keynes, UK.2School of Engineering & Applied Science Aston University, UK.HT2013Paris, France. 2013
    2. 2. OUTLINEA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES- Question Complexity and Community Maturity- Enquiry Communities- Server Fault- Needs and Motivations- Contributions- Hypotheses and Validation- Two Definitions- Five Hypotheses- Validation- Computing and Mapping Features- Predictors- Feature Computation: Users, Content and Threads.- Measuring Content Complexity and Community Maturity- Prediction Results- Feature Ranking- Community Maturity- Future Work- Conclusion
    3. 3. ENQUIRY COMMUNITIES“Enquiry Communities are communitiescomposed of askers and answerers lookingfor solutions to particular issues.”A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    4. 4. ENQUIRY COMMUNITIES“Enquiry Communities are communitiescomposed of askers and answerers lookingfor solutions to particular issues.”A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    5. 5. ENQUIRY COMMUNITIES“Enquiry Communities are communitiescomposed of askers and answerers lookingfor solutions to particular issues.”A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    6. 6. A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES- Server Fault (SF):- A web based enquiry ITcommunity specialised inserver related issues.- Factual questions rather thanconversational questions.- Dataset (Data up to April2011):- 71,962 Questions- 162,401 Answers- 51,727 Users- 4,999 Topics (Tags)http://serverfault.com
    7. 7. ENQUIRY COMMUNITIES- Enquiry Communities Needs (Rowe et al. 2011, Burelet al. 2012):- Community Managers:- Make sure that the community is “happy” (questions are solved).- Make sure that the community becomes more knowledgeableover time (users gain expertise and experience).- Identify and implement features that help users goals.- Askers:- Get answers related to a particular issue.- Make sure that a community can fulfil their needs before askinga questions.- Answerers:- Find which question they can answer.- Find questions that are challenging.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    8. 8. ISSUES AND MOTIVATION- Enquiry Communities Needs:- Questions have uneven complexity:- Difficulty to identify how hard are particular questions and whocan answer them.- Communities have different answering abilities:- Some communities can answers simple questions about a topicwhile other communities can also answer complex questions.- How do determine if a community is able to answer complexquestions?- Some communities are more knowledgeable andexperienced than others:- How do we measure experience and expertise?- Features can support the identification of maturecommunities and complex content, but which ones?- What features help to measure community maturity and contentcomplexity?A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    9. 9. IDENTIFYING COMPLEX QUESTIONS AND MATURECOMMUNITIESHow user, content, thread and platform features affect contentcomplexity identification? How can we measure maturity based oncontent complexity?1. Identifying Complex Questions:– Helping answerer to find relevant and challenging questions.2. Analysis of Complexity Predictors:– Helping community manager to identify important complexityfactors3. Measuring Community Maturity:– Helping users to decide if their question will beanswered/Helping community manager to understand theircommunity abilities.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    10. 10. CONTRIBUTIONSHow user, content, thread and platform features affect quality contentcomplexity? How can we use content complexity for measuring thematurity of communities?- Introduce a definition of question complexity and validate thehypothesis that question complexity increases with askers’community involvement.- Study the influence of features relating to askers, answerers,questions and answers on question complexity prediction.- Introduce the concept of community maturity, a measure ofcommunity knowledge and specialisation.- Investigate the evolution of community maturity in Server Fault anddemonstrate that community maturity is influenced by topicaldynamics.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    11. 11. LITERATUREHow user, content, thread and platform features affect quality contentcomplexity? How can we use content complexity for measuring thematurity of communities?- No empirical study of the relation between content complexity andcommunity involvement.- No free-form model of content complexity. Typically very domaindependent (Wu, 2009; Bachrach et al. 2012).- Community health metrics (Welinder, et al. 2010; Toral et al., 2009;Rowe et al. 2011) tend to neglect skill building as a key healthindicator despite the importance of such factor in user participation(Pal et al., 2012; Nam et al., 2009).A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    12. 12. QUESTION COMPLEXITY AND MATURITY- Definition 1 (Question Complexity):- Question complexity is a value representing thedifficulty and level of expertise required for answeringa question.- Definition 2 (Community Maturity):- Community Maturity is a value representing the levelof knowledge and specialisation achieved by acommunity. A more mature community focuses onmore complex questions whereas a community lessmature has simpler and less focused questions.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    13. 13. QUESTION COMPLEXITY AND MATURITY- Definition 1 (Question Complexity):- Question complexity is a value representing thedifficulty and level of expertise required for answeringa question.- Definition 2 (Community Maturity):- Community Maturity is a value representing the levelof knowledge and specialisation achieved by acommunity. A more mature community focuses onmore complex questions whereas a community lessmature has simpler and less focused questions.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    14. 14. QUESTION COMPLEXITY AND MATURITY- Hypothesis 1 (Temporality):- For a given user, question complexity increases as a function of time and participation.The longer a user is actively involved in a community, the more complex are herquestions.- Hypothesis 2 (Enquiry):- For a given user, question complexity increases with the number of question asked.The more a user asks questions, the more likely her questions will become morecomplex.- Hypothesis 3 (Commitment):- For a given user, question complexity increases with her activity levels. The morefrequently a user is involved in a community, the more complex are her questions.- Hypothesis 4 (Accomplishment):- For a given user, question complexity increases with the number of questions she hasfound answers before. The more a user finds answers to some questions, the morelikely she can improve her knowledge skill and thus asks more complex questions inthe future.- Hypothesis 5 (Focus):- For a given user, question complexity increases with the number of question asked.The more a user asks questions, the more likely her questions will become morecomplex.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    15. 15. QUESTION COMPLEXITY AND MATURITY- Hypothesis 1 (Temporality):- For a given user, question complexity increases as a function of time and participation.The longer a user is actively involved in a community, the more complex are herquestions.- Hypothesis 2 (Enquiry):- For a given user, question complexity increases with the number of question asked.The more a user asks questions, the more likely her questions will become morecomplex.- Hypothesis 3 (Commitment):- For a given user, question complexity increases with her activity levels. The morefrequently a user is involved in a community, the more complex are her questions.- Hypothesis 4 (Accomplishment):- For a given user, question complexity increases with the number of questions she hasfound answers before. The more a user finds answers to some questions, the morelikely she can improve her knowledge skill and thus asks more complex questions inthe future.- Hypothesis 5 (Focus):- For a given user, question complexity increases with the number of question asked.The more a user asks questions, the more likely her questions will become morecomplex.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    16. 16. QUESTION COMPLEXITY AND MATURITY- Hypothesis 1 (Temporality):- For a given user, question complexity increases as a function of time and participation.The longer a user is actively involved in a community, the more complex are herquestions.- Hypothesis 2 (Enquiry):- For a given user, question complexity increases with the number of question asked.The more a user asks questions, the more likely her questions will become morecomplex.- Hypothesis 3 (Commitment):- For a given user, question complexity increases with her activity levels. The morefrequently a user is involved in a community, the more complex are her questions.- Hypothesis 4 (Accomplishment):- For a given user, question complexity increases with the number of questions she hasfound answers before. The more a user finds answers to some questions, the morelikely she can improve her knowledge skill and thus asks more complex questions inthe future.- Hypothesis 5 (Focus):- For a given user, question complexity increases with the number of question asked.The more a user asks questions, the more likely her questions will become morecomplex.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIESParticipationComplexity
    17. 17. HYPOTHESES VALIDATION- Methodology:1. Select 510 question pairs based on the previous hypotheses:- Questions from early and late user contributions.2. Annotate the question pairs by selecting what question is the mostcomplex:- Due to low inter-annotator agreement (for 3 annotators, κ = 0.146), we focus onpairs that have more than 75% agreement (220 pairs, 440 questions).3. Calculate the statistical significance of hypothesis- Concentration on Hypothesis 1: Temporality.- Results (Hypothesis 1):A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    18. 18. HYPOTHESES VALIDATION- Methodology:1. Select 510 question pairs based on the previous hypotheses:- Questions from early and late user contributions.2. Annotate the question pairs by selecting what question is the mostcomplex:- Due to low inter-annotator agreement (for 3 annotators, κ = 0.146), we focus onpairs that have more than 75% agreement (220 pairs, 440 questions).3. Calculate the statistical significance of hypothesis- Concentration on Hypothesis 1: Temporality.- Results (Hypothesis 1):A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    19. 19. FEATURES1. User Features (Askers and Answerers):– Represents the characteristics and reputation ofaskers and answerers (e.g. reputation, numberof best answers, normalised topic entropy…).2. Questions and Answers Features:– Questions and answers features (e.g.readability, ratings, number of views…).– Represents relation between answers within aparticular thread. (e.g. topic reputation, elapseddays…).– Content based features (e.g. term entropy,readability…).A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    20. 20. FEATURESA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIESType FeaturesAskers Community Age (Experience), Community Age Difference, Number of Questions(Enquiry), Number of Answers, Asking Rate (Asker Commitment), Answering Rate,Ratio of Successfully-Answered Questions (Accomplishment), Ratio of QuestionSuccessfully Answered by Others, Normalised Question Topic Entropy (Focus),Normalised Answer Topic Entropy, Average Number of Replies per Question, AverageNumber of Question Views, Z-score, Reputation.Answerers Askers features + Mean and Standard deviation forms.Questions Number of Views, Number of Words, Readability with Gunning Fog , Readabilitywith Flesch-Kincaid Grade, Existing Value, Status, Number of Answers, Favourites,Score, Informativeness, Cumulative Term Entropy.Answers Questions features + Mean and Standard deviation forms + Elapsed Days,Elapsed Days First, Elapsed Days Last, Number of Comments Mean, Score.
    21. 21. QUESTION COMPLEXITY PREDICTION- Experimental Setting:1. Split the annotated questions in complexand non-complex questions (440 questions).2. Compute features.3. Use Logistic Regression algorithm andvalidate results using 10-folds crossvalidation.4. Compute Precision (P), Recall (R), F-Measure (F1) and area under the ReceiverOperator Curve (ROC) for different featuregroups.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    22. 22. COMPLEXITY PREDICTION RESULTSA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    23. 23. COMPLEXITY PREDICTION RESULTS- Best Answer Identification (F1 0.60):– Baseline Models:- Asker’s age in a community correlates better than questionlength.- Question length is not correlated with complex questions.– Feature Types Models and Complete Model:- Askers and answerer’s features are the best: Questioncomplexity is mostly related with asker’s features.- The full model performs better than the feature type models.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    24. 24. FEATURES RANKING- Features Ranking:1. For each feature, Information Gain Ratio(IGR), Correlation Feature Selection (CFS)and F1 Feature Drop (FD) is computed2. The features are then sorted by theirrespective importance.3. The best features are then selected forcomputing a new question complexity modelby accounting for the best F1.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    25. 25. FEATURES RANKING RESULTSA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    26. 26. FEATURES RANKING RESULTS- Features Impact Comparison:– Asker’s community age and topical focus are themost important features.– User features are the most significant (73.3% ofthe top ten features).– Answer features are low ranked.– Focused users are more likely to ask complexquestions.– Questions with low value (Pal et al., 2010) aremore likely to be complex (complements findingson question selection behaviour of experts (Pal etal., 2010)).A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    27. 27. BEST MODEL RESULTS- Best Model (F1 0.64):– The best model is obtainedwhen using CFS, the selectedfeatures are:1. Asker’s question topicalfocus.2. Asker’s ratio ofsuccessfully-answeredquestions.3. Askers’ community age.4. Questions’ existing value(Pal et al., 2010).5. Questions’ views.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    28. 28. COMMUNITY MATURITY- Maturity Measure:- Experimental Setting:1. Calculate question complexity based on the proportion ofcomplex questions asked per month.2. Compute maturity on different users sets depending ontheir age in the community.3. Compute maturity for the most discussed topics (tags)and users that have been active for more than a day.4. Observe the evolution of maturity for the most discussedtopics and the different users groups.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    29. 29. COMMUNITY MATURITY RESULTSA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIESUsers Topics/Communities
    30. 30. COMMUNITY MATURITY RESULTS- User Evolution:- Maturity increases over time.- Maturity drop can be explained by the drop of averagecommunity age at the end of 2010 (229 to 185 days).- Committed users are more likely to become more mature (0.64 >0.4).- Community Evolution and Topics:- Maturity increases over time.- Different topics/Different growth rates. For example:- Linux: Slow but sustained → Linux users becomes more knowledgeableover time.- Windows-server-2008: Initially high, then low → Users migrating toWindows-server-2008-r2.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    31. 31. FUTURE WORK- Perform similar analysis on other EnquiryCommunities:- Confirm our results on additional datasets.- Derive a complexity metric that can beapplied to any online community based onthe 5 factors of complexity:- Create a measure that does not requireannotations.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    32. 32. CONCLUSION- We showed that current health measures do not help in identifyingcommunities that become more topic proficient over time.- We introduced the concept of question complexity and communitymaturity and provided a complexity model (F1 ≈ 0.65) and a maturitymeasure.- We showed that question complexity depends on user activity andcommitment as well as other factors (hypotheses testing).- We found that complex questions depends on five key factors: 1)asker’s question topical focus; 2) asker’s ratio of successfully-answered questions; 3) askers’ community age; 4) questions’existing value (Pal et al., 2010), and; 5) questions’ views.- We showed that SF is a mature community and that maturity hastopical dynamics.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    33. 33. QUESTIONS?Web: http://evhart.online.frEmail: g.burel@open.ac.ukTwitter: @evhart@wwwA QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES
    34. 34. REFERENCES- Rowe, M., Alani, H., Angeletou, S., and Burel, G. Report on social, technical and corporateneeds in online communities. Tech. Rep. 3.1, ROBUST, 2011.- Burel, G, Yulan H., Alani H. Automatic Identification Of Best Answers In Online EnquiryCommunities. In Proceeding of ESWC2012 (2012). Heraklion, Greece.- Wu, M. The community health index. In Proceedings of the 4th International Conference onPersuasive Technology (New York, NY, USA, 2009), Persuasive ’09, ACM, pp. 24:1–24:2.- Bachrach, Y., Graepel, T., Minka, T., and Guiver, J. How to grade a test without knowing theAnswers - A bayesian graphical model for adaptive crowdsourcing and aptitude testing. arXivpreprint arXiv:1206.6386 (2012).- Welinder, P., Branson, S., Belongie, S., and Perona, P. The multidimensional wisdom of crowds. InIn Proc. of NIPS (2010), pp. 2424–2432.- Toral, S. L., Martınez-Torres, M. R., Barrero, F., and Cortals, F. An empirical study of the drivingforces behind online communities. Internet Research 19, 4 (2009), 378–392.- Pal, A., Chang, S., and Konstan, J. Evolution of experts in question answering communities. InProceedings of the International AAAI Conference on Weblogs and Social Media (2012), pp. 274–281.- Nam, K., Ackerman, M., and Adamic, L. Questions in, knowledge in?: a study of naver’s questionanswering community. In Proceedings of the 27th international conference on Human factors incomputing systems (2009), pp. 779–788.- Pal, A., Chang, S., and Konstan, J. Evolution of experts in question answering communities. InProceedings of the International AAAI Conference on Weblogs and Social Media (2012), pp. 274–281.A QUESTION OF COMPLEXITY − MEASURING THE MATURITY OF ONLINE ENQUIRY COMMUNITIES

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