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Predicting Communication Intention in Social Media
 

Predicting Communication Intention in Social Media

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In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on ...

In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on friend-of-a-friend type of relationships, common interests, or other factors? In this work, we study the problem of predicting directed communication
intention between two users. Link prediction is similar to communication intention in that it uses network structure for prediction. However, these two problems exhibit fundamental
differences that originate from their focus. Link prediction uses evidence to predict network structure evolution, whereas our focal point is directed communication initiation between
users who are previously not structurally connected. To address this problem, we employ topological evidence in conjunction to transactional information in order to predict communication intention. It is not intuitive whether methods that work well for
link prediction would work well in this case. In fact, we show in this work that network or content evidence, when considered separately, are not sufficiently accurate predictors. Our novel approach, which jointly considers local structural properties of users in a social network, in conjunction with their generated content, captures numerous interactions, direct and indirect, social and contextual, which have up to date been considered independently. We performed an empirical study to evaluate our method using an extracted network of directed @-messages sent between users of a corporate microblogging service, which resembles Twitter. We find that our method outperforms state of the art techniques for link prediction. Our findings have implications for a wide range of social web applications, such as contextual expert recommendation for Q&A, new friendship relationships creation, and targeted content delivery.

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    Predicting Communication Intention in Social Media Predicting Communication Intention in Social Media Presentation Transcript

    • Predicting Communication Intentionin (Enterprise) Social NetworksCharalampos “Harris” ChelmisComputer Science, University of Southern CaliforniaThanks to: Viktor K. Prasanna, Ming Hsieh Department of Electrical Engineering, USCVikram Sorathia, Co-founder & CEO at Kensemble Tech Labs LLP•All audio is muted.•If you dialed in, you MUST enter your audio pin to be able to ask questions!•We recommend that you keep your phone muted, and unmute yourself when you need to ask questions.•You can view the upcoming seminar schedule at www.milibo.com/talent/events.aspx
    • Social Networks are Everywhere2• , ,• Movie Networks• Affiliation/co-authorship networks• Professional networks• Friendship networks• Information networks• Organizational Networks• Q&A websites• Even networks
    • • Multiple applications Targeted marketing Personalization− Content delivery Recommendation− People to connect, items to buy, movies to watch Law enforcement− Fraud detection− Guilt by association Epidemiology Information dissemination/propagation …• Users interact with one another and content they create andconsume Rich interactions− Friendships based on similarity− Following based on interest NoisySocial Network Analysis3
    • • Collaboration Enabling Technologies Multiple communication channels Spread of timely and relevant information Search for data and expertsCollaboration Technologies at the Workplace4
    • • Main focus on business perspective Less noisy than online social networks Q&A Problem solving Information seeking• But also Assist in breaking barriers Team building Knowledge propagation• Opportunities Expert identification− Experts vs. Influencers Information Flow Trends− Technology adoption− Company focusCollaboration at the Workplace5
    • • More Opportunities Collective Knowledge− Generation− Sharing Collaborative Knowledge Management− How do employees work together to complete tasks?− How does innovation happen?− Best practices• Difficulties Informal interactions Heterogeneous, unstructured data How to formally model knowledge?Collaboration at the Workplace6
    • • Descriptive Modeling Social network analysis• Predictive modeling Link prediction Attribute prediction• Typically networked data are represented as graphs Nodes (e.g., users) Edges− Social relations− Interactions− Information flow− Similarity Weight− Communication frequency− Communication cost (e.g., distance)− Reciprocity− Type of interaction (e.g., family member, friend, or officemate)Networked Data Modeling7
    • • Heterogeneous object and link types• Both nodes and edges may carry attributes• Attribute dependencies Correlation between attribute values and link structure− e.g. link prediction based on auxiliary information Correlation among attributes of related nodes− e.g. collaborative filtering• Node dependencies e.g. groups/communities• Partial observations e.g. labelsBut Networked Data are Very Different than Graphs8
    • • Big Data Billions of users Billions of connections Billions of “documents”• Temporality Affiliations Interests Friendships• Context Spatial Temporal Topical• Content multimodality Text MultimediaNetworked Data ≠ Graphs9
    • • Edges are more than links Type− e.g. like vs. comment vs. share Trust Sentiment Strength Time Number• Edges “reveal” something about the relation between nodes Prior “interaction” to compute similarityNetworked Data ≠ Graphs10
    • Networked Data ≠ Graphs11
    • • Integrated informal communication• Context sensitive• Temporal• External Sources• Analysis of implicit relationsHolistic Modeling of Complex Networks12Multiple collaborativeplatformsMultimodal,heterogeneous contentfrom various sourcesMeta-informationabout content- Social Algebraic Operations- Complex mining and analysis- Correlation of different domains- Temporal, semantic analysiscontexttimecontentconnection
    • • Directed communication graph G = (V,E) Node u represents a user Edge e = (u,v) exists iff user u has sent at least one message to user v• Input G0 = (V0,E0), subgraph of G consisting of all nodes in G and a subset ofedges in G• Output Ranked list L of edges, not present in G0, such thatPredicting Intention of Communication13ELE 0OutputInputuG0uG1
    • • Edge semantics: Conversation between users rather than friendship••• “What makes people initiate conversations with strangers?”• “With whom do individuals choose to collaborate and why?”≠ Link Prediction14Contextual – Temporal PropertiesDirectionality Mattersu1≠m1(u1,u2,g1) m1(u1,u2,g2)u2 u1 u2u1≠m1(u1,u2,g1) m1(u2,u1,g1)u2 u1 u2
    • • The tendency to relate to people with similar characteristics status, beliefs, etc.• Fundamental concept underlying social theories (e.g. Blau 1977)• Fundamental basis for links in many types of social networks “Similar” nodes tend to cluster together• How does this helps us solve our problem?Homophily15
    • • Machine learning Probabilistic, supervised, computationally expensive• Node attributes No semantics We instead exploit multiple features of variable types• Network structureHow to Compute Similarity?16Graph Distance Length of shortest path between u and vCommon NeighborsJaccard CoefficientAdamic/AdarPreferential AttachmentKatzRandom walks)()( vu )()()()(vuvu )()()(log1vuzz)()( vu 1 , vupaths
    • • If there is a tie between x and y and one between y and z, then ina transitive network x and z will also be connected• Such structural clues have been traditionally used for linkprediction• Consider what happens if edge semantics change• Or if we further include contextTransitivity17xyzxyzasks ?
    • Communication Network18ThreadedDiscussionBipartiteGraphPost-ReplyNetwork
    • Augmented, Directed Post-Reply Network19
    • • We model a user as a union of her: connections and her content• We characterize microblogs using a set of attributes each feature according to its type Textual Features− raw textual content (bag-of-words)− #hashtags− Groups Temporal Features− Date− Time• WordNet: enrich concepts with conceptually, semantically andlexically related terms Synonyms Hypernyms HyponymsUser Representation20
    • • Semantic Similarity of textual concepts Jaccard Index: Synonym-based similarity: Hypernym-based similarity: Hyponym-based similarity:• Calculate Semantic Similarity using weighted sumSemantic Similarity of Textual Features21|SS||SS|)S,s(Sb)s(a,bababa)S,(Ssb)(a,s bass )S,(Ssb)(a,s bahh )S,(Ssb)(a,s bahphp 
    • • Caveat: concepts belong to the same subtree Solution: compute similarity between the union of annotations• Account for lexical similarity: Levenshtein similarity• Select the highest similarity, either semantic or lexicalSemantic Similarity of Textual Features22)HpHS,HpHs(Sb),(a,swb)(a,swb)(a,swb),y(a,nSimilaritLevenshteimaxb)(a,sbbbaaahphphhsstg
    • • Textual Similarity between bag-of-words features: tf.idf weight vector representation Cosine similarity• Date Similarity:• Time Similarity:• Timestamp similarity:Feature Similarity23otherwiseTddTdddd,1,0)d,(ds 212121dotherwiseTttTtttt,1,0)t,(ts 212121t)y,(xsw)y,(xswy)(x,s ttttdddddf 
    • • We use a variation of Hausdorff point set distance measure: Average of the maximum similarity of features in set A with respect tofeatures in set B : any similarity measure on set elements ak and bi Measure is asymmetric with respect to the setsFeature Set Similarity24 ),(maxA1B)(A,SA1iH ikkbasim),( ik basim
    • • A weighted function of content and network proximity λ controls the tradeoff between content and network proximity• Content Proximity User similarity with respect to their microblogs Similarity of microblogs− Combined weighted value of respective attributes similarities• Network Proximity:User Similarity25)p,(psw)p,(psw)p,(pSw)p,(psw)p,S(p 21dfdf21txtx21Htgg2g1gg21 tgtg ),(maxu1)u,(uS 21u1i121C1ipkpkpuuSpuvuvus||),(v)(u,SNv)(u,)S-(1v)(u,Sv)S(u, NC  Asymmetric withrespect to users
    • • First construct the augmented communication graph G(V,E)• Given a user u, compute users similarity− For all posts of user u with respect to all other users in the network For all facetsCommunication Intention Prediction26
    • • Complete snapshot (June 2010 – August 2011) of a corporate micro-blogging service, which resembles Twitter 4,213 unique users 16,438 messages in total− 8,174 thread starters− 8,264 replies 8,139 threads 88 discussion groups 637 unique #hastagsDataset27
    • • In our evaluation we focus on the Largest Connected Component 582 users 3,773 directed edges 11,684 messages Average degree = 12.97• Clustering coefficient = 0.2311 >> ccrandom = 0.0223• Clustering coefficient as a function of node degree Average clustering coefficient decreases with increasing node degree Higher for nodes of low degree significant clustering among low-degree nodesDataset28
    • Number of Neighbors• Directed messages received vs. directed messages sent Scattered across the diagonal Cumulative distribution of the out-degree to in-degree ratio, exhibitshigh correlation between in-degree and out-degree Tendency of users to reply back when they receive a message fromother users?29
    • • Four-fold cross validation• Randomly sample 100 users & recommend top-k links for each user• Accuracy measures Precision@k Recall@k MRR• Baselines Random− Random selection Shared Vocabulary− Cosine similarity based on #hastags vector Shortest distance− Length of the shortest path Common neighbors−Evaluation30  SpkkpNS)(1  SppppFRFS1  SpprankS11)()(v)sim(u, vu  
    • Lexical and Topical Alignment• Is there a global vocabulary in the corporate microblogging service? Hashtags vocabulary “Groups vocabulary”• Select user pairs at random and measure number of shared tags Average nst = 1.001 Most common case is the absence of shared tags• However adjacent users in social networks tend to share commoninterests due to homophily We measure user homophily with respect to hashtags as a function ofthe distance of users in the network• Select user pairs at random and measure number of shared groups Average nsg = 1 Most common case is the absence of shared groups31
    • Lexical Alignment• Average number of shared (distinct) hashtags for two users as afunction of their distance d along the network:,• Shared hashtags vocabulary up to distance 6!3222)()()()(),(t vt ut vutagstftftftfvu)()(tagsUvnun ttttvtu
    • • Bold indicates best performing baseline• Percentage lift the % improvement achieved over the best performing baselineMethods Comparison33
    • • How to choose best values of λ and weighing factors?• Different datasets may lead to different optimal values Grid search over ranges of values for these parameters Measure accuracy on the validation set for each configuration settingWeight Scheme Selection34
    • • 0 only considers network proximity• 1 only considers content similarity• All schemes perform better than the baseline• Good value for λ is approximately 0.8Effect of Parameter λ35
    • • Effect of weighting schemes on accuracy per user• Different weighting schemes perform better for different users Features importance is user specific• Need personalization to achieve better accuracy overallEffect of Weighting Scheme36
    • • Average precision (measured@ 5) of users having k (a) posts or (b) neighbors in the communication network The more statistical evidence the better the overall precisionContent Availability and Structural Proximity37
    • • MRR as a function of λ for various restrictions• Greater statistical evidence results in more accurate predictionsContent Availability and Structural Proximity38
    • • Performed modeling and analysis of informal communication at theworkplace• We introduced the problem of communication intention prediction• We addressed this problem by exploiting auxiliary information Holistic modeling of structural clues and semantically enrichedcontent• We tested the efficiency of our approach in a real-world dataset The more statistical evidence available, the more accurate predictions Need for personalization• Potential applications Contextual expert recommendation for Q&A Search for “interesting” people to collaborate• Open problems Scalability Replication of results for online social mediaConclusion and Open Problems39
    • • Semantic Social Network Analysis for the EnterpriseContextual Recommendation40Employee ID:
    • • Semantic Social Network Analysis for the Enterprise Instantiate our modeling in Ontology Collaboration analytics at the workplace Real-world data evaluationContextual Recommendation41Contextual ego-network analysisExpertIdentificationSemantic Analysis
    • • Questions?• Resources http://www-scf.usc.edu/~chelmis/index.php http://pgroup.usc.edu/wiki/CSS• Please send all inquiries at chelmis@usc.eduThank you!42