Monitoring and Analysis of Online Communities
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  • 1. Monitoring and Analysis of OnlineCommunitiesHarith AlaniKnowledge Media institute,The Open University, UK http://twitter.com/halani http://delicious.com/halani http://www.linkedin.com/pub/harith-alani/9/739/534 Web Science Summer School Galway, 2011 1
  • 2. Market value of Web Analytics 2
  • 3. Agenda•  Community monitoring•  Offline and online social networking•  Modeling and tracking behaviour•  Analysing community features•  Predicting discussion activity 3
  • 4. Online community monitoring•  Analysing and understanding activities and dynamics•  Studying impact of social and technical features•  Forecast future growth and evolution•  Tracking behaviour and influence•  Tracking reputation and buzz•  Listening to customer opinion•  Profiling the user base•  Gauging customer sentiment 4
  • 5. Measuring social media Deloitte, Beeline Labs, & Society for New Communication Research surveyed 140 companies with online communities, 2008 5
  • 6. Measuring social media Deloitte, Beeline Labs, & Society for New Communication Research surveyed 140 companies with online communities, 2008 6
  • 7. Measuring social media “B2B Marketing Goes Social: A White Horse Survey Report” – March 2010 – study of 104 companies 7
  • 8. Measuring social media “Social media usage, attitudes and measurability: What do marketers think?” – KingFishMedia, 2010 8
  • 9. Tools for monitoring social media 9
  • 10. •  Analytics: –  Mention volume –  Sentiment –  Discussion clouds –  Activity graphs and metrics –  Language and geolocation filtering –  Filter by social platform –  Comparisons 10 http://www.ubervu.com/
  • 11. •  Analytics: –  Influencing users –  Sentiment and opinion analysis –  Viral content analysis –  Detecting sales leads –  Filter by geo-location 11 http://www.viralheat.com/home !
  • 12. Monitoring and Analysis ofOnline CommunitiesWith a Web Science flavour 12
  • 13. Online vs. Offline socialnetworking 13
  • 14. Online vs. offline social networking: The Bad News!•  Digital social networking increases physical social isolation•  Causes –  Genetic alterations –  Weakened immune system –  Less resistant to cancer –  Higher risk of heart disease –  Higher blood pressure –  Faster dementia –  Narrower arteriesAric Sigman, “Well Connected? The BiologicalImplications of Social Networking’”, Biologist, 56(1), 2009 14
  • 15. Online vs. offline social networking: The Good News!•  Digital networking increase social interaction –  Transforms little boxed societies to networked and networking societies –  Create more opportunities to network –  New methods to communicate, easily, and widely –  Supports and increases F2F contact! –  The stronger the offline social tie, the more intense the online communication –  The stronger the offline social tie, the more diverse online communications –  F2F is medium of choice in weaker social tiesKeith Hampton and Barry Wellman, Long Distance Community in the Network Society: Contact andSupport Beyond Netville, American Behavioral Scientist 45 (3), November, 2001.Barry Wellman, The Glocal Village: Internet and Community, Idea’s - The Arts & Science Review, 15University of Toronto, 1(1),2004
  • 16. Physical online & digital offline 16
  • 17. Sensor & Social Networks 17
  • 18. Sensor & Social Networks www.nabaztag.com The Canine Twitterer “Having my daily workout. Already did 15 leg lifts!” 18
  • 19. Location Sensors & Social Networking Tag-Along Marketing The New York Times, November 6, 2010 “Everything is in place for location-based social networking to be the next big thing. Tech companies are building the platforms, venture capitalists are providing the cash and marketers are eager to develop advertising. “ 19
  • 20. Monitoring online/offline social activity Where  is  everybody?   20
  • 21. Monitoring online/offline social activity•  Generating opportunities for F2F networking 21
  • 22. Monitoring online/offline social activity “There are more than 250 million active users currently accessing Facebook through their mobile devices“ “People that use Facebook on their mobile devices are twice as active on Facebook than non-mobile users” http://www.facebook.com/press/info.php?statistics 22
  • 23. Tracking of F2F contact networks Sociometer, MIT, 2002 -  F2F and productivity -  F2F dynamics -  Who are key players? -  F2F and office distance TraceEncounters - 2004 23
  • 24. SocioPatterns platform http://www.sociopatterns.org/! 24
  • 25. Offline social networks From a small conference at ISI, Turin by Ciro Cattuto 25
  • 26. Offline social networks•  Similarity students features –  Country of origin SR –  Seniority –  .. Age? Role? Projects? Interests?•  What other JR info can we get to help us students understand these network SR dynamics? 26
  • 27. Offline + online social networking Who should Anyone I I talk to? Where have I know here? met this guy? Where should I go? ESWC2010 27
  • 28. Live Social Semantics (LSS): RFIDs + Social Web + Semantic Web <?xml version="1.0"?>! <rdf:RDF! xmlns="http:// tagora.ecs.soton.ac.uk/schemas/ tagging#"! xmlns:rdf="http://www.w3.org/ 1999/02/22-rdf-syntax-ns#"! xmlns:xsd="http://www.w3.org/2001/ XMLSchema#"! xmlns:rdfs="http://www.w3.org/ 2000/01/rdf-schema#"! xmlns:owl="http://www.w3.org/ 2002/07/owl#"! xml:base="http:// tagora.ecs.soton.ac.uk/schemas/ tagging">! <owl:Ontology rdf:about=""/>! <owl:Class rdf:ID="Post"/>! <owl:Class rdf:ID="TagInfo"/>! <owl:Class rdf:ID="GlobalCooccurrenceInfo"/>! <owl:Class rdf:ID="DomainCooccurrenceInfo"/>! <owl:Class rdf:ID="UserTag"/>! <owl:Class rdf:ID="UserCooccurrenceInfo"/>! <owl:Class rdf:ID="Resource"/>! <owl:Class rdf:ID="GlobalTag"/>! <owl:Class rdf:ID="Tagger"/>! <owl:Class rdf:ID="DomainTag"/>! <owl:ObjectProperty rdf:ID="hasPostTag">! <rdfs:domain rdf:resource="#TagInfo"/>! </owl:ObjectProperty>! <owl:ObjectProperty rdf:ID="hasDomainTag">! <rdfs:domain rdf:resource="#UserTag"/>! </owl:ObjectProperty>! <owl:ObjectProperty rdf:ID="isFilteredTo">!•  Integration of physical presence and online information <rdfs:range rdf:resource="#GlobalTag"/>! <rdfs:domain•  Semantic user profile generation rdf:resource="#GlobalTag"/>! </owl:ObjectProperty>! <owl:ObjectProperty•  Logging of face-to-face contactrdf:ID="hasResource">! <rdfs:domain rdf:resource="#Post"/>! <rdfs:range =…!•  Social network browsing•  Analysis of online vs offline social networks
  • 29. SW sources conference chair proceedings chair author CoP 29
  • 30. Social and information networks 30
  • 31. Merging social networks FOAF 31
  • 32. Tag Filtering Service Semantic modeling Semantic analysis Collective intelligence Statistical analysis Syntactical analysis 32
  • 33. Tag Filtering Service 33
  • 34. From Tags to Semantics 34
  • 35. Tags to User Interests 35
  • 36. From raw tags and social relationsto Structured Data Collective intelligence User raw Semantic data data Structured data ontologies 36
  • 37. RFIDs for tracking social contact 37
  • 38. Convergence with online social networks 38
  • 39. People contact à RFID à RDF Triples foaf#Person1 contactWith   Place hasContact   foaf#Person2 contactPlace   F2FContact contactDate   contactDura0on   XMLSchema#date   XMLSchema#0me   39
  • 40. 40
  • 41. 41
  • 42. Real-time F2F networks with SNS links 42 http://www.vimeo.com/6590604
  • 43. Live Social Semantics Deployed at:Data analysis•  Face-to-face interactions across scientific conferences•  Networking behaviour of frequent users•  Correlations between scientific seniority and social networking•  Comparison of F2F contact network with Twitter and Facebook•  Social networking with online and offline friends 43
  • 44. Analysis of LSS ResultsThe New Yorker 2/11/2008 44
  • 45. Characteristics of F2F contact network Network ESWC 2009 HT 2009 ESWC 2010 characteristics Number of users 175 113 158 Average degree 54 39 55 Avg. strength (mn) 143 123 130 Avg. weight (mn) 2.65 3.15 2.35 Weights ≤ 1 mn 70% 67% 74% Weights ≤ 5 mn 90% 89% 93% Weights ≤ 10 mn 95% 94% 96%•  Degree is number of people with whom the person had at least one F2F contact•  Strength is the time spent in a F2F contact•  Edge weight is total time spent by a pair of users in F2F contact 45
  • 46. Characteristics of F2F contact events Contact ESWC 2009 HT 2009 ESWC 2010 characteristics Number of 16258 9875 14671 contact events Average contact 46 42 42 length (s) Contacts ≤ 1mn 87% 89% 88% Contacts ≤ 2mn 94% 96% 95% Contacts ≤ 5mn 99% 99% 99% Contacts ≤ 10mn 99.8% 99.8% 99.8% F2F contact pattern is very similar for all three conferences
  • 47. F2F contacts of returning users Degree•  Degree: number of other 10 2 participants with whom an attendee has interacted 1 10 1 2 10 10•  Total time: total time spent in ESWC2010 Total interaction time interaction by an attendee 4 10 3 10 3 4 5 10 10 10•  Link weight: total time spent in F2F 4 Links’ weights 10 interaction by a pair of returning 3 10 attendees in 2010, versus the same 2 10 quantity measured in 2009 1 10 1 2 3 4 5 10 10 10 10 10 ESWC 2009 & Pearson Correlation ESWC2009 ESWC 2010 Degree 0.37 Time spent on F2F networking by frequent users is stable, even when the list of Total F2F 0.76 interaction time people they networked with changed Link weight 0.75 47
  • 48. Average seniority of neighbours in F2F networks•  No clear pattern is observed 5 if the unweighted average senn Avg seniority of the neighbours over all neighbours in the Average seniority of neighbors senn,w with weighted averages aggregated network is 4 considered senn,max Seniority of user with strongest link•  A correlation is observed 3 when each neighbour is weighted by the time spent with the main person 2•  The correlation becomes much stronger when 1 considering for each individual only the neighbour with whom the most time was spent 0 0 5 10 seniority (number of papers) Conference attendees tend to networks with others of similar levels of scientific seniority 48
  • 49. Presence  of  A<endees  HT2009   Importance  of  the  bar?     Popularity  of  sessions?    par0cular  talks?  
  • 50. Number  of  cliques  HT2009  
  • 51. Offline networking vs online networking Twitterers Spearman Correlation (ρ) Tweets – F2F Degree - 0.15 Tweets – F2F Strength - 0.15 Twitter Following – F2F - 0.21 Degree users Users with Facebook and Twitter accounts in ESWC 2010 •  people who have a large number of friends on Twitter and/or Facebook don’t seem to be the most socially active in the offline world in comparison to other SNS users No strong correlation between amount of F2F contact activity and size of online social networks 51
  • 52. Scientific seniority vs Twitter followers Twitter users Correlation H-index – Twitter Followers 0.32 (#$" H-index – Tweets - 0.13 (" !#" *+,-./"01221+./3" !#&" 45678.9" *+..:3" !#%" !#$" !" (" &" ((" (&" $(" $&" )(" )&" %(" users •  Comparison between people’s scientific seniority and the number of people following them on Twitter People who have the highest number of Twitter followers are not necessarily the most scientifically senior, although they do have high visibility and experience 52
  • 53. Conference Chairs all chairs all chairs participants 2009 participants 2010 2009 2010average degree 55 77.7 54 77.6average strength 8590 19590 7807 22520average weight 159 500 141 674average number of 3.44 8 3.37 12events per edge •  Conf chairs interact with more distinct people (larger average degree) •  Conf chairs spend more time in F2F interaction (almost three times as much as a random participant)
  • 54. Networking with online and offline ‘friends’Characteristics all users coauthors Facebook Twitter friends followersaverage contact 42 75 63 72duration (s)average edge weight 141 4470 830 1010(s)average number of 3.37 60 13 14events per edge •  Individuals sharing an online or professional social link meet much more often than other individuals •  Average number of encounters, and total time spent in interaction, is highest for co-authors F2F contacts with Facebook & Twitter friends were respectively %50 and %71 longer, and %286 and %315 more frequent than with others They spent %79 more time in F2F contacts with their co-authors, and they met them %1680 more times than they met non co-authors
  • 55. Twitterers vs Non-Twitterers•  Time spent in conference rooms –  Twitter users spent on average 11.4% more time in the conf rooms than non-twitter users (mean is 26% higher)•  Number of people met F2F during the conference –  Twitter users met on average 9% more people F2F (mean 8% higher)•  Duration of F2F contacts –  Twitter users spent on average 63% more time in F2F contact than non twitter users (mean is 20% higher) 55
  • 56. Analysis of behaviour in onlinecommunities Web Science Summer School Galway, 2011 56
  • 57. Behaviour of individuals – micro level analysis(#$" 6DD1">?@20AB?M" 89O1209>M"PQM"12R2<DE27>#";01">D?@;<">@60;<>"" @0"K88"92;L" S:DT>"9:2"0239">9;7"72>2;7?:27N" ("!#"!#&" :2;<9:=">?@20AB?"C" >D?@;<"E7DB<2>#"F72G" ?:;@7>HIJ>"!#%"!#$" DO9>@127M" :@6:" >:=" E7DB<2" >?@20A>9N" !" (" )" *" (+" (," $(" $)" $*" ++" +," %(" %)" -./0123" 4$4"526722" 4$4"8972069:" 57
  • 58. Why monitor behaviour?•  Understand impact of behaviour on community evolution•  Forecast community future•  Learn when intervention might be needed•  Learn which behaviour should be encouraged or discouraged•  Find what could trigger certain behaviours•  What is the best mix of behaviour to increase engagement in the community•  To see which users need more support, which ones should be confined, and which ones should be promoted 58
  • 59. Behaviour analysisJeffrey Chan, Conor Hayes, and Elizabeth Daly. Decomposing discussion forums usingcommon user roles. In Proc. Web Science Conf. (WebSci10), Raleigh, NC: US, 2010•  Behaviour compositions in Boards.ie:
  • 60. Ontology
  • 61. Encoding Rules in Ontologies with SPIN
  • 62. Approach for inferring User RolesStructural, social network, Feature levels change with thereciprocity, persistence, participation dynamics of the communityRun our rules over each user’s features Associate Roles with a collection ofand derive the role composition feature-to-level Mappings e.g. in-degree -> high, out-degree -> high 62
  • 63. Data from Boards.ie•  Forum 246 (Commuting and Transport): Demonstrates a clear increase in activity over time.•  Forum 388 (Rugby): Exhibits periodic increase and decrease in activity and hence it provides good examples of healthy/unhealthy evolutions.•  Forum 411 (Mobile Phones and PDAs): Increase in activity over time with some fluctuation - i.e. reduction and increase over various time windows.•  For the time in 2004-01 to 2006-12
  • 64. Features•  In-degree Ratio: The proportion of users U that reply to user υi, thus indicating the concentration of users that reply to υi•  Posts Replied Ratio: Proportion of posts by user υi that yield a reply, used to gauge the popularity of the user’s content based on replies•  Thread Initiation Ratio: Proportion of threads that have been started by υi.•  Bi-directional Threads Ratio: Proportion of threads where user υi replies to a user and receives a reply, thus forming a reciprocal communication•  Bi-directional Neighbours Ratio: The proportion of neighbours where a reciprocal interaction has taken place - e.g. υi replied to υi and υi replied to υi.•  Average Posts per Thread: The average number of posts made in every thread that user υi has participated in•  Standard Deviation of Posts per Thread: The standard deviation of the number of posts in every thread that user υi has participated in. This gauges the distribution of the discussion lengths.
  • 65. Role Skeleton
  • 66. ResultsCommuting and Transport Rugby Mobile Phones and PDAs•  Correlation of individual features in each of the three forums
  • 67. (a) Forum 246: Commuting and Transport Results (b) Forum 388: Rugby (c) Forum 411: Mobile Phones and PDAs •  Variation in behaviour composition & activity •  Behaviour composition in/ stability influences forum activity
  • 68. Prediction analysis – preliminary results!•  Predicting rise/fall in post submission numbers•  Binary classification•  Features : Community composition, roles and percentages of users associated with each Forum P R F1 ROC 246 0.799 0.769 0.780 0.800 388 0.603 0.615 0.605 0.775 411 0.765 0.692 0.714 0.617 All 0.583 0.667 0.607 0.466 •  Cross-community predictions are less reliable than individual community analysis due to the idiosyncratic behaviour observed in each individual community
  • 69. Observations so far•  Growing communities contain more elitists and popular participants•  Shrinking communities contain many taciturns and ignored users•  A stable composition, with a mix of roles, is associated with increased community activity•  Different communities may require different behaviour compositions to increase activity/health
  • 70. What features make onlinecommunities tick
  • 71. •  How many do you recognise? Use?•  Which ones still exist?•  Which are strong and healthy?•  Which are aging and withering?•  What health signs should we look for?•  How can we predict their future evolution? 71
  • 72. Rise and fall of social networks 72
  • 73. Predicting engagement•  Which posts will receive a reply? –  What are the most influential features here?•  How much discussion will it generate? –  What are the key factors of lengthy discussions? 73
  • 74. user attributes - describing the reputation of the user - and attributes of a post’s content - generally referred to as content features. In Table 1 we define user andCommon online communityFeatures features content features and study their influence on the discussion “continuation”. Table 1. User and Content User Features In Degree: Number of followers of U # Out Degree: Number of users U follows # List Degree: Number of lists U appears on. Lists group users by topic # Post Count: Total number of posts the user has ever posted # User Age: Number of minutes from user join date # P ostCount Post Rate: Posting frequency of the user U serAge Content Features Post length: Length of the post in characters # Complexity: Cumulative entropy of the unique words in post p λ i∈[1,n] pi(log λ−log pi) of total word length n and pi the frequency of each word λ Uppercase count: Number of uppercase words # Readability: Gunning fog index using average sentence length (ASL) [7] and the percentage of complex words (PCW). 0.4(ASL + P CW ) Verb Count: Number of verbs # Noun Count: Number of nouns # Adjective Count: Number of adjectives # Referral Count: Number of @user # Time in the day: Normalised time in the day measured in minutes # Informativeness: Terminological novelty of the post wrt other posts The cumulative tfIdf value of each term t in post p t∈p tf idf (t, p) Polarity: Cumulation of polar term weights in p (using P o+N e Sentiwordnet3 lexicon) normalised by polar terms count |terms|•  How do all these features influence activity generation in an online 4.2 Experiments community? are intended to test the performance of different classification mod- Experiments – els in identifying seed posts. Therefore we used four classifiers: discriminative Such knowledge leads to better use and management of the community 74 classifiers Perceptron and SVM, the generative classifier Naive Bayes and the
  • 75. Experiment for identifying seed posts •  Twitter data on the Haiti earthquake, and the Union Address Dataset Users Tweets Seeds Non-seeds Replies Haiti 44,497 65,022 1,405 60,686 2,931 Union Address 66,300 80,272 7,228 55,169 17,875 •  Evaluated a binary classification task –  Is this post a seed post or not? 75
  • 76. first report on the results obtained from our model selection phase, before moving Identifying seeds with different type ofonto our results from using the best model with the top-k features. featuresTable 3. Results from the classification of seed posts using varying feature sets andclassification models (a) Haiti Dataset (b) Union Address Dataset P R F1 ROC P R F1 ROC User Perc 0.794 0.528 0.634 0.727 User Perc 0.658 0.697 0.677 0.673 SVM 0.843 0.159 0.267 0.566 SVM 0.510 0.946 0.663 0.512 NB 0.948 0.269 0.420 0.785 NB 0.844 0.086 0.157 0.707 J48 0.906 0.679 0.776 0.822 J48 0.851 0.722 0.782 0.830 Content Perc 0.875 0.077 0.142 0.606 Content Perc 0.467 0.698 0.560 0.457 SVM 0.552 0.727 0.627 0.589 SVM 0.650 0.589 0.618 0.638 NB 0.721 0.638 0.677 0.769 NB 0.762 0.212 0.332 0.649 J48 0.685 0.705 0.695 0.711 J48 0.740 0.533 0.619 0.736 All Perc 0.794 0.528 0.634 0.726 All Perc 0.630 0.762 0.690 0.672 SVM 0.483 0.996 0.651 0.502 SVM 0.499 0.990 0.664 0.506 NB 0.962 0.280 0.434 0.852 NB 0.874 0.212 0.341 0.737 J48 0.824 0.775 0.798 0.836 J48 0.890 0.810 0.848 0.8774.3 ResultsOur•  findings from Table 3 demonstrate the effectiveness of using solely user User features are most important in Twitterfeatures for identifying seed posts. Infeatures gives best results Address datasets •  But combining user & content both the Haiti and Uniontraining a classification model using user features shows improved performance76over the same models trained using content features. In the case of the Union
  • 77. Impact of different featureswhich we found to be 0.674 indicating a good correlation between the two listsand• their respective ranks.the highest impact on identification of seed What features have posts?TableRank features by information gainGain Ratio wrt Seed Post class label. The •  4. Features ranked by Information ratio wrt seed post class labelfeature name is paired within its IG in brackets. Rank Haiti Union Address 1 user-list-degree (0.275) user-list-degree (0.319) 2 user-in-degree (0.221) content-time-in-day (0.152) 3 content-informativeness (0.154) user-in-degree (0.133) 4 user-num-posts (0.111) user-num-posts (0.104) 5 content-time-in-day (0.089) user-post-rate (0.075) 6 user-post-rate (0.075) user-out-degree (0.056) 7 content-polarity (0.064) content-referral-count (0.030) 8 user-out-degree (0.040) user-age (0.015) 9 content-referral-count (0.038) content-polarity (0.015) 10 content-length (0.020) content-length (0.010) 11 content-readability (0.018) content-complexity (0.004) 12 user-age (0.015) content-noun-count (0.002) 13 content-uppercase-count (0.012) content-readability (0.001) 14 content-noun-count (0.010) content-verb-count (0.001) 15 content-adj-count (0.005) content-adj-count (0.0) 16 content-complexity (0.0) content-informativeness (0.0) 17 content-verb-count (0.0) content-uppercase-count (0.0) 77
  • 78. 7 content-polarity (0.064) content-referral-count (0.030) 8 user-out-degree (0.040) user-age (0.015) 9 content-referral-count (0.038) content-polarity (0.015)Positive/negative impact of features 10 11 12 content-length (0.020) content-readability (0.018) user-age (0.015) content-length (0.010) content-complexity (0.004) content-noun-count (0.002) 13 content-uppercase-count (0.012) content-readability (0.001) 14 content-noun-count (0.010) content-verb-count (0.001)•  What is the correlation between seed posts and features? 15 16 content-adj-count (0.005) content-complexity (0.0) content-adj-count (0.0) content-informativeness (0.0) 17 content-verb-count (0.0) content-uppercase-count (0.0) Haiti Union Address Fig. 3. Contributions of top-5 features to identifying Non-seeds (N ) and Seeds(S). Upper plots are for the Haiti dataset and the lower plots are for the Union Address 78 dataset.
  • 79. Identifying Seed Posts•  Can we identify seed posts using the top-k features? –  Stability is reached with 5 features –  Classification with 5 features is sufficient for identifying posts that generate responses 79
  • 80. Predicting Discussion Activity•  Reply rates: –  Haiti 1-74 responses, Union Address 1-75 responses•  Compare rankings –  Ground truth vs predicted•  Experiments –  Using Haiti and Union Address datasets –  Evaluate predicted rank k where k={1,5,10,20,50,100) –  Support Vector Regression with user, content, user+content features Dataset Training Test size Test Vol Test Vol SD size Mean Haiti 980 210 1.664 3.017 Union Address 5,067 1,161 1.761 2.342 80
  • 81. Predicting Discussion Activity Haiti dataset Union Address dataset •  Content features are key for top ranks •  Use features more important for higher ranks 81
  • 82. Identifying Seed Posts in Boards.ie•  Used the same features as before –  User features •  In-degree, out-degree, post count, user age, post rate –  Content features •  Post Length, complexity, readability, referral count, time in day, informativeness, polarity•  New features designed to capture user affinity –  Forum Entropy •  Concentration of forum activity •  Higher entropy = large forum spread –  Forum Likelihood •  Likelihood of forum post given user history •  Combines post history with incoming data 82
  • 83. Experiment for identifying seed posts•  Used all posts from Boards.ie in 2006•  Built features using a 6-month window prior to seed post date Posts Seeds Non-Seeds Replies Users 1,942,030 90,765 21,800 1,829,465 29,908•  Evaluated a binary classification task –  Is this post a seed post or not? –  Precision, Recall, F1 and Accuracy –  Tested: user, content, focus features, and their combinations 83
  • 84. h the features (i.e., user TABLE IIom t − 188 to t − 1. In R ESULTS FROMTHE CLASSIFICATION OF SEED POSTS USING Identifying seeds with different type ofhe features compiled for outcomes and will not VARYING FEATURE SETS AND CLASSIFICATION MODELS features user may increase their User SVM P 0.775 R 0.810 F 0.774 ROC 0.581 1ich would not be a true Naive Bayes 0.691 0.767 0.719 0.540ime the post was made. Max Ent 0.776 0.806 0.722 0.556 J48 0.778 0.809 0.734 0.582e number of posts (seeds, Content SVM 0.739 0.804 0.729 0.511tained within. Naive Bayes 0.730 0.794 0.740 0.616 Max Ent 0.758 0.806 0.730 0.678TING S EED P OSTS J48 0.795 0.822 0.783 0.617 ls are often hindered by Focus SVM 0.649 0.805 0.719 0.500 Naive Bayes 0.710 0.737 0.722 0.588We alleviate this problem Max Ent 0.649 0.805 0.719 0.586 and non-seeds through a J48 0.649 0.805 0.719 0.500posts have been identified User + Content SVM 0.790 0.808 0.727 0.509 Naive Bayes 0.712 0.772 0.732 0.593 of discussion that such Max Ent 0.767 0.807 0.734 0.671ook for the best classifier J48 0.795 0.821 0.779 0.675 ts and then search for the User + Focus SVM 0.776 0.810 0.776 0.583 Naive Bayes 0.699 0.778 0.724 0.585 guishing seed posts from Max Ent 0.771 0.806 0.722 0.607atures that are associated J48 0.777 0.810 0.742 0.617 Content + Focus SVM 0.750 0.805 0.729 0.511 Naive Bayes 0.732 0.787 0.746 0.658 Max Ent 0.762 0.807 0.731 0.692 J48 0.798 0.823 0.787 0.662 the previously described All SVM 0.791 0.808 0.727 0.510ntaining both seeds and Naive Bayes 0.724 0.780 0.740 0.637 Max Ent 0.768 0.808 0.733 0.688r collection of posts we J48 0.798 0.824 0.792 0.692tures listed in section III 84
  • 85. Positive/negative impact of features on Boards.ie TABLE III R EDUCTION IN F1 LEVELS AS INDIVIDUAL FEATURES ARE DROPPED FROM THE J 48 CLASSIFIER•  What are the most Feature Dropped F1 important features for - 0.815 predicting seed posts? Post Count In-Degree 0.815 0.811* Out-Degree 0.811* User Age 0.807*** Post Rate 0.815 Forum Entropy 0.815•  Correlations: Forum Likelihood 0.798*** Post Length 0.810** –  Referral counts (non-seeds) Complexity 0.811** –  Forum likelihood (seeds) Readability 0.802*** Referral Count 0.793*** –  Informativeness (non-seeds) Time in Day 0.810** Informativeness 0.801*** –  Readability (seeds) Polarity 0.808*** Signif. codes: p-value < 0.001 *** 0.01 ** 0.05 * 0.1 . –  User age (non-seeds) hyperlinks (e.g., ads and spams). This contrasts with work in Twitter which found that tweets containing many links were 85
  • 86. Predicting Discussion Activity in Boards.ie•  Can we predict the level of discussion activity? 86
  • 87. Predicting Discussion Activity in Boards.ie•  What impact do features have on discussion length? –  Assessed Linear Regression model with focus and content features –  Forum Likelihood (pos) –  Content Length (+/neutral) –  Complexity (pos) –  Readability (+/neutral) –  Referral Count (neg) –  Time in Day (+/neutral) –  Informativeness (-/neutral) –  Polarity (neg) 87
  • 88. Stay tuned•  More communities –  SAP, IBM, StackOverflow, Reddit –  Compare impact of features on their dynamics•  Better behaviour analysis –  Less features, more forums/communities, more graphs! –  Healthy? posts, reciprocation, discussions, sentiment mixture•  Churn analysis –  Correlation of features/behaviour to ‘bounce rate’•  Intervention! –  Opportunities and mechanisms to influence behaviour 88
  • 89. Upcoming events Social Object Networks IEEE Social Computing, 2011 October 9-10, Boston, USA http://ir.ii.uam.es/socialobjects2011/ ! Deadline: August 5, 2011 Intelligent Web Services Meet Social Computing AAAI Spring Symposium 2012, March 26-28, Stanford, California http://vitvar.com/events/aaai-ss12 Deadline: Octover 7, 2011 89
  • 90. Questionnaire on user needshttp://socsem.open.ac.uk/limesurvey/index.php?sid=55487Questionnaire is to identify the needs that community users have within onlinecommunities and to learn the factors and issues that influence those needs. 90
  • 91. Thanks to My social semantics team Live Social Semantics team Sofia Angeletou Ciro Cattuto Wouter van Den Broeck Matthew Rowe Research Associate ISI, Turin ISI, Turin Research AssociateAcknowledgements Alain Barrat Martin Szomszor CPT Marseille & ISI CeRC, City University, UK Gianluca Correndo, Uni Southampton Ivan Cantador, UAM, Madrid STI International ESWC09/10 & HT09 chairs and organisers All LSS participants 91