Managing Online Business     Communities       The ROBUST Project       Steffen Staab, Thomas Gottron          & ROBUST Pr...
Business Communities• Information ecosystems   – Employees   – Business Partners, Customers   – General Public            ...
Use Cases Lotus Connections             SAP Community Network (SCN)   MeaningMine Communities                   Communitie...
High Level Architecture                              Risk Dashboard           Community                                   ...
Requirements                                     Community Risk Management                                                ...
The ROBUST platformRisk Dashboard and   Visualizations
A new Role: the Community Manager                  •   Definition of risks/opportunities                  •   Monitoring  ...
Risk matrix visualization          Risks ordered by probability   Negative impacts   Positive impacts                     ...
Community Health: Graphic Equalizer
Community Analysis,    User Roles & Risk Management      Determine Risk:Users Leaving the Community
“Churn” • Users churn = Users leave the community, become   inactive • Questions:   – Why do users leave the community?   ...
What is Churn? Activity?                         No churn                        No churn       Activity bursts           ...
Why Churn?
Network Effects • Churn: churning users influence other users they   communicate with                                     ...
Role Analysis: who churns? • A role represents the standing, or part, that a user has   within a given community   Role  D...
Role Analysis: Approach        Reciprocity, initialisation,   Feature levels change with the        persistence, popularit...
Churn Analysis on Roles • Role composition in community • Development towards an unhealthy role   composition!
Community & Content Analysis     Opportunity Detection:Discovering Interesting Contents
IBM Connections         Analogies to           Twitter!
Retweets                       RT @janedoe: My                                    Follower                        dear @jo...
Retweets           • Retweet indicates             quality             – „of interest for others“           • Idea:       ...
Retweets: Prediction model  Aim: Prediction of probability of retweet  Logistic regression:                         1     ...
Logistic Regression: Weights                      Feature       Dimensions   Weight        Constant                (interc...
Logistic Regression: Topic Weights                                   Topic                          Weight      social med...
Re-Ranking using Interestingness • Top-k interesting tweets for „beer“  Rang Username   Tweet   1 BeeracrossTX UK beer mag...
LiveTweet            http://livetweet.west.uni-koblenz.de/                                       Che Alhadi et al. TREC 20...
Compare Interestingness to Detect Trends Tomorrow I am going to …                            … play tennis                ...
Community SimulationRisk Mitigation: SimulatingEffects of Policy Changes
Policies • Governance of Communities    – Def.: Steering and coordinating actions of      community members • Implementati...
Governance by Policy Change                                      What if using                                       Polic...
Scenario: Policy Controlled Content Generation •   Users generate content •   Users search content •   Users consume conte...
Policy impacts on user behaviour                               Policy   User Need for     Content   Consumption           ...
Attention Management • Part of Community: forum activity • Goal: Steer user activity to specific forums • User model param...
Simulation based on observed parameters                                                    Online Community    Metrics of ...
Variable Parameters indicated by different policy • Users search content    – Presentation  Ranking threads in content vi...
Backend TechnologiesIndexing Distributed  Semantic Graphs
Community Information • Examples   – Male persons who have a public profile document   – Computing science papers authored...
Linked Data     Semantic Web Technology to     1. Provide structured data on the web     2. Link data across data sources ...
The LOD Cloud
Querying linked data SELECT ?x WHERE {    ?x rdfs:type foaf:Person .    ?x rdfs:type pim:Male .    ?x foaf:maker ?y .    ?...
Querying linked data – using an index SELECT ?x WHERE {    ?x rdfs:type foaf:Person .    ?x rdfs:type pim:Male .    ?x foa...
Schema Index Overview                        Konrath et al. BTC 2011, JWS 2012
How it works ...SELECT ?xFROM …WHERE {   ?x rdfs:type foaf:Person .   ?x rdfs:type pim:Male .   ?x foaf:maker ?y .   ?y rd...
How it works ...    pim:Male      foaf:Person      foaf:PPD     TC_18                         TC_76                       ...
Building the Index from a Stream  Stream of data (coming from a LD crawler)    … D16, D15, D14, D13, D12, D11, D10, D9, D...
Does it work good?  Comparison of stream based vs. Gold standard Schema on 11 M triple data set
Managing Business Communities   Lessons learned
Conclusions • Business communities vary with regard to   – Interaction   – Interests   – Type of conversation • Novel anal...
References•   S. Angeletou, M. Rowe, H. Alani. Modelling and analysis of user behaviour in online communities.    In Proce...
Thank You!             EC Project 257859
Upcoming SlideShare
Loading in …5
×

Managing Online Business Communities

1,287 views

Published on

Keynote talk at Int. Conf. on Enterprise Information Systems, ICEIS-2012, Wroclaw, Poland

Published in: Technology, Business
2 Comments
1 Like
Statistics
Notes
No Downloads
Views
Total views
1,287
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
10
Comments
2
Likes
1
Embeds 0
No embeds

No notes for slide
  • Business communities slightly different:Grouped around a business/enterpriseAim: add value to business (Knowledge management, public relations, customer aquisition, support, etc.)Conclusion: communities have a value, that needs to be taken care ofValue is endangered by risks (e.g. experts leaving) or might be increased by seizing opportunities (e.g. connect people working on the same topic)
  • Intrinsic: features of the service or product.Extrinsic: External factors. E.g. Peer pressure.Expectation of eventual payback in terms of information, Agreeable social relations. Enhancement of reputation, Spreading of their ideas , In this way there exists an Implicit Social Contract
  • Managing Online Business Communities

    1. 1. Managing Online Business Communities The ROBUST Project Steffen Staab, Thomas Gottron & ROBUST Project Team EC Project 257859
    2. 2. Business Communities• Information ecosystems – Employees – Business Partners, Customers – General Public Valuable asset Risks Opportunities
    3. 3. Use Cases Lotus Connections SAP Community Network (SCN) MeaningMine Communities Communities Communities • Employees • Customers • Social media • Working groups • Partners • News • Interest Groups • Suppliers • Web fora • Projects • Developers • Public communities Business value Business value Business value • Task relevant information • Products support • Topics • Collaboration • Services • Opinions • Innovation • Find business partners • Service for partners Volume Volume Volume • 4,000 posts/day • 6,000 posts/day • 1,400,000 posts/day • 386,000 employees • 1,700,000 subscribers • 708,000 web sources • 1.5GB content/day • 16GB log/day • 45GB content/day Employees Business Partners Public Domain Intranet Extranet Internet
    4. 4. High Level Architecture Risk Dashboard Community Community Risk Communication Bus Simulation Management Cloud Based Data User Roles Management and Models Community Analysis Community Data
    5. 5. Requirements Community Risk Management • Risk Formalization Community Analysis • Risk Detection • Risk Forecasting • Feature Selection • Risk Management • Structural Analysis • Risk Visualization • Behavior Analysis • Content Analysis User Roles and Models • Cross Community • User Needs Community Simulation • Motivation • Roles • Community Models • Groups • Policy Models • Community Value • Influence • Simulation Cloud Based Data Management • Prediction • Operations on Data • Scalability • Real Time • Parallel Execution • Stream Based
    6. 6. The ROBUST platformRisk Dashboard and Visualizations
    7. 7. A new Role: the Community Manager • Definition of risks/opportunities • Monitoring • Interaction with community • Reaction and countermeasures  Need for a control center
    8. 8. Risk matrix visualization Risks ordered by probability Negative impacts Positive impacts A single risk can have multiple impacts
    9. 9. Community Health: Graphic Equalizer
    10. 10. Community Analysis, User Roles & Risk Management Determine Risk:Users Leaving the Community
    11. 11. “Churn” • Users churn = Users leave the community, become inactive • Questions: – Why do users leave the community? – Who is leaving the community? – Impact? • Tasks: – Detect “Churn” – Predict “Churn” – Evaluate “Churn” Angeletou et al. ISWC 2011
    12. 12. What is Churn? Activity? No churn No churn Activity bursts Holiday No churn Churn! Inconsistent Cease to be active
    13. 13. Why Churn?
    14. 14. Network Effects • Churn: churning users influence other users they communicate with First Churner First influenced Churner • Temporal ordering: Churners that churned subsequent to each other • Frequently, after a slow start, Cascade resulting in a cascade of churning Churn of a single user Influences his neighbours Karnstedt et al. WebSci 2011
    15. 15. Role Analysis: who churns? • A role represents the standing, or part, that a user has within a given community Role Dimension Reciprocity Initialisation Persistence Popularity Elitist high: Threads low low: Neighbours Joining low high Conversationalist Popular Initiator high high Supporter medium low medium Taciturn low low Ignored low: No replies
    16. 16. Role Analysis: Approach Reciprocity, initialisation, Feature levels change with the persistence, popularity dynamics of the communityBehaviourOntology Run rules over each user’s features Based on related work, we associate and derive the community role roles with a collection of feature-to-level composition mappings e.g. in-degree -> high, out-degree -> high Angeletou et al. ISWC 2011
    17. 17. Churn Analysis on Roles • Role composition in community • Development towards an unhealthy role composition!
    18. 18. Community & Content Analysis Opportunity Detection:Discovering Interesting Contents
    19. 19. IBM Connections Analogies to Twitter!
    20. 20. Retweets RT @janedoe: My Follower dear @johndoe had troubles to wake up this @janedoe #morning My dear @johndoe had troubles to wake up this #morning
    21. 21. Retweets • Retweet indicates quality – „of interest for others“ • Idea: – Learn to predict retweets! Likelihood of retweet as metric for Interestingness Naveed et al. WebSci 2011
    22. 22. Retweets: Prediction model Aim: Prediction of probability of retweet Logistic regression: 1 f (z) = -z 1+e z = w0 + w1x1 ++w2 x2 +¼+ wn xn Model parameters wi learned on training data Dataset Users Tweets Retweets Choudhury 118,506 9,998,756 7.89% Choudhury (extended) 277,666 29,000,000 8.64% Petrovic 4,050,944 21,477,484 8.46%
    23. 23. Logistic Regression: Weights Feature Dimensions Weight Constant (intercept) -5.45 Direct message -147.89 Username 146.82 Message feature Hashtag 42.27 URL 249.09 Valence -26.88 Sentiment Arousal 33.97 Dominance 19.56 Positive -21.8 Emoticons Negative 9.94 Positive 13.66 Exclamation Negative 8.72 ! -16.85 Punctuation ? 23.67 Terms Odds 19.79
    24. 24. Logistic Regression: Topic Weights Topic Weight social media market post site web tool traffic network 27.54 follow thank twitter welcome hello check nice cool people 16.08 credit money market business rate economy home 15.25 christmas shop tree xmas present today wrap finish 2.87 home work hour long wait airport week flight head -14.43 twitter update facebook account page set squidoo check -14.43 cold snow warm today degree weather winter morning -26.56 night sleep work morning time bed feel tired home -75.19
    25. 25. Re-Ranking using Interestingness • Top-k interesting tweets for „beer“ Rang Username Tweet 1 BeeracrossTX UK beer mag declares "the end of beer writing." @StanHieronymus says not so in the US. http://bit.ly/424HRQ #beer 2 narmmusic beer summit @bspward @jhinderaker no one had billy beer? heehee #narm - beer summit @bspward @jhinde http://tinyurl.com/n29oxj 3 beeriety Go green and turn those empty beer bottles into recycled beer glasses! | http://bit.ly/2src7F #beer #recycle (via: @td333) 4 hblackmon Great Divide beer dinner @ Porter Beer Bar on 8/19 - $45 for 3 courses + beer pairings. http://trunc.it/172wt 5 nycraftbeer Interesting Concept-Beer Petitions.com launches&hopes 2help craft beer drinkers enjoy beer they want @their fave pubs. http://bit.ly/11gJQN 6 carichardson Beer Cheddar Soup: Dish number two in my famed beer dinner series is Beer Cheddar Soup. I hadn’t had too.. http://bit.ly/1diDdF 7 BeerBrewing New York City Beer Events - Beer Tasting - New York Beer Festivals - New York Craft Beer http://is.gd/39kXj #beer 8 delphiforums Love beer? Our member is trying to build up a new beer drinkers forum. Grab a #beer and join us: http://tr.im/pD1n 9 Jamie_Mason #Baltimore Beer Week continues w/ a beer brkfst, beer pioneers luncheon, drink & donate event, beer tastings & more. http://ping.fm/VyTwg 10 carichardson Seattle and Beer: I went to Seattle last weekend. It was my friend’s stag - he likes beer - we drank beer.. http://tinyurl.com/cpb4n9 Naveed et al. CIKM 2011
    26. 26. LiveTweet http://livetweet.west.uni-koblenz.de/ Che Alhadi et al. TREC 2011, ECIR 2012
    27. 27. Compare Interestingness to Detect Trends Tomorrow I am going to … … play tennis … play golf … do some gardening … spread wisdom … go to a Justin Bieber concert … win the lottery
    28. 28. Community SimulationRisk Mitigation: SimulatingEffects of Policy Changes
    29. 29. Policies • Governance of Communities – Def.: Steering and coordinating actions of community members • Implementation: – Direct intervention of community owner – Functionality of the community platform • Mitigation of risks: – Change platform functionality – Impact? Schwagereit WebSci 2011
    30. 30. Governance by Policy Change What if using Policy 2 ? Policy 1 Community Manager Prediction Policy 2
    31. 31. Scenario: Policy Controlled Content Generation • Users generate content • Users search content • Users consume content • Users interact with content – Rate content – Reply to content • Where can a (platform) policy have effects?
    32. 32. Policy impacts on user behaviour Policy User Need for Content Consumption Content Content Rating Replying Selection Assessment User Need for Content Creation Content Content Creation Evaluation Policy
    33. 33. Attention Management • Part of Community: forum activity • Goal: Steer user activity to specific forums • User model parameters: – Activity rate for creating threads – Activity rate for creating replies – Preferences for activity in specific forums • Community Model – Varied restrictions for thread creation • Observed Metrics – Response time on threads
    34. 34. Simulation based on observed parameters Online Community Metrics of Community Community Community Artifacts Activity Community Community Members Platform (Persons) (Software) Analysis Comparison Abstraction User Model Community + Platform Parameters Model Metrics of Simulated Simulated Simulated Community Community Community Artifacts Activity Simulation
    35. 35. Variable Parameters indicated by different policy • Users search content – Presentation  Ranking threads in content views • Recency • Social Closeness • Topical closeness • Popularity – Observation: Influence which questions are answered • Users generate content – Restrict number of questions asked per forum • Users turn to other questions – Observation: Response time in some fora reduced
    36. 36. Backend TechnologiesIndexing Distributed Semantic Graphs
    37. 37. Community Information • Examples – Male persons who have a public profile document – Computing science papers authored by social scientists – American actors who are also politicians and are married to a model. • Maybe specific databases available: – Person search engines – Bibliographic databases – Movie database
    38. 38. Linked Data Semantic Web Technology to 1. Provide structured data on the web 2. Link data across data sources Thing Thing Thing Thing Thing Thing Thing Thing Thing Thing typed typed typed typed links links links links A B C D E
    39. 39. The LOD Cloud
    40. 40. Querying linked data SELECT ?x WHERE { ?x rdfs:type foaf:Person . ?x rdfs:type pim:Male . ?x foaf:maker ?y . ?y rdfs:type foaf:PersonalProfileDocument . }
    41. 41. Querying linked data – using an index SELECT ?x WHERE { ?x rdfs:type foaf:Person . ?x rdfs:type pim:Male . ?x foaf:maker ?y . ?y rdfs:type foaf:PersonalProfileDocument . }
    42. 42. Schema Index Overview Konrath et al. BTC 2011, JWS 2012
    43. 43. How it works ...SELECT ?xFROM …WHERE { ?x rdfs:type foaf:Person . ?x rdfs:type pim:Male . ?x foaf:maker ?y . ?y rdfs:type foaf:PersonalProfileDocument .} pim: foaf: foaf: Male Person PPD rdfs:type rdfs:type rdfs:type ?x ?y foaf:maker
    44. 44. How it works ... pim:Male foaf:Person foaf:PPD TC_18 TC_76 5_76 maker EQC_5 foaf:maker DS 1 DS 2
    45. 45. Building the Index from a Stream  Stream of data (coming from a LD crawler) … D16, D15, D14, D13, D12, D11, D10, D9, D8, D7, D6, D5, D4, D3, D2, D1 FiFo 1 C3 4 6 C2 3 4 2 C2 2 1 3 C1 5
    46. 46. Does it work good? Comparison of stream based vs. Gold standard Schema on 11 M triple data set
    47. 47. Managing Business Communities Lessons learned
    48. 48. Conclusions • Business communities vary with regard to – Interaction – Interests – Type of conversation • Novel analysis techniques needed: – Integration of different data sources – Simulation of policy changes – Value of users • Concrete needs confirmed by project external companies looking for such technology
    49. 49. References• S. Angeletou, M. Rowe, H. Alani. Modelling and analysis of user behaviour in online communities. In Proceedings of the 10th international conference on The semantic web - Volume Part I,• A. Che Alhadi, T. Gottron, J. Kunegis, N. Naveed. Livetweet: Microblog retrieval based on interestingness. In TREC’11: Proceedings of the Text Retrieval Conference 2011, 2011.• A. Che Alhadi, T. Gottron, J. Kunegis, N. Naveed. Livetweet: Monitoring and predicting interesting microblog posts. In ECIR’12: Proceedings of the 34th European Conference on Information Retrieval, 2012.• M. Karnstedt, M. Rowe, J. Chan, H. Alani, C. Hayes. The effect of user features on churn in social networks. In WebSci ’11: Proceedings of the 3rd International Conference on Web Science, 2011.• M. Konrath, T. Gottron, A. Scherp. Schemex – web-scale indexed schema extraction of linked open data. In Semantic Web Challenge, Submission to the Billion Triple Track, 2011.• M. Konrath, T. Gottron, S. Staab, A. Scherp. Schemex—efficient construction of a data catalogue by stream-based indexing of linked data. Journal of Web Semantics, 2012. to appear.• N. Naveed, T. Gottron, J. Kunegis, A. Che Alhadi. Bad news travel fast: A content-based analysis of interestingness on twitter. In WebSci ’11: Proceedings of the 3rd International Conference on Web Science, 2011.• N. Naveed, T. Gottron, J. Kunegis, A. Che Alhadi. Searching microblogs: Coping with sparsity and document quality. In CIKM’11: Proceedings of 20th ACM Conference on Information and Knowledge Management, 2011.• F. Schwagereit, A. Scherp, S. Staab. Survey on governance of user-generated content in web communities. In WebSci ’11: Proceedings of the 3rd International Conference on Web Science, 2011.
    50. 50. Thank You! EC Project 257859

    ×