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CSE509 Lecture 6
 

Lecture 6 of CSE509:Web Science and Technology Summer Course

Lecture 6 of CSE509:Web Science and Technology Summer Course

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  • The past decade has witnessed the emergence of participatory Web and social media, bringing peopletogether in many creative ways. Millions of users are playing, tagging, working, and socializingonline, demonstrating new forms of collaboration, communication, and intelligence that were hardlyimaginable just a short time ago. Social media also helps reshape business models, sway opinions andemotions, and opens up numerous possibilities to study human interaction and collective behavior inan unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of socialmedia, reviews representative tasks of computing with social media, and illustrates associated challenges.
  • Term social search refers broadly to the process of finding info online with the assistance of social resources, such as by asking friends, reference librarians or unknown persons online for assistance.
  • What Do People Ask Their Social Networks, and Why? A Survey Study of Status Message Q&A BehaviorMeredith Ringel Morris, Microsoft Research, USAJaime Teevan, Microsoft Research, USAKatrina Panovich, Massachusetts Institute of Technology, USAExplores the phenomenon of using social network status messages to ask questions, including the frequency of the behavior, the question types and topics, and people's motivation for asking and answering.[Survey people: Eaten at a restaurant in Atlanta? Chose the restaurant?]
  • Understanding for what question types and topics people turn to a social network, rather than a search engine, and their motivations for doing so can help in designing next-generation search tools that integrate both search engine and social functionality
  • Demographics:- Actively recruited interns – represented 27% of study population 26% female 40% 26-35 years old.- 98.1% have FB accounts. 71% have Twitter. Thus analysis focuses on these systems.
  • Similar in some ways to popular search engine queries, although technology heavy.But some topics are missing: health and pornographyOther topics people said they wouldn’t ask about: religion, politics, dating, finance
  • Recommendations fastest responses, then Opinion, then Factual
  • WaelGhonim’s tweets shown on Google during Egypt uprising.
  • “A small number of nodes have high degrees” thanks to the power law
  • involves the computation of the average distance of one node to all the other nodes

CSE509 Lecture 6 CSE509 Lecture 6 Presentation Transcript

  • CSE509: Introduction to Web Science and Technology
    Lecture 6: Social Information Retrieval
    ArjumandYounus
    Web Science Research Group
    Institute of Business Administration (IBA)
  • Last Time…
    Transition from Web 1.0 to Web 2.0
    Social Media Characteristics
    Part I: Theoretical Aspects
    Social Networks as a Graph
    Properties of Social Networks
    Part II: Getting Hands-On Experience on Social Media Analytics
    Twitter Data Hacks
    Part III: Example Researches
    August 13, 2011
  • Today
    Role of Today’s Web: Changing the way Information Needs are Satisfied
    Social Search
    Research Case by Microsoft Research: What do People Ask their Social Networks
    Techniques for Influence Analysis in Social Networks
    August 13, 2011
  • Role of Today’s Web
    August 13, 2011
    Marketing
    Tool
    Information Finding Tool
    Media
    Tool
  • New Dimensions in Search with The Social Web
    Information Overload
    Search engines don’t always hold answers that users are looking for
    Smart Search (CNN Money)
    “The Web, they say, is leaving the era of search and entering one of discovery. What’s the difference? Search is what you do when you’re looking for something. Discovery is when something wonderful that you didn’t know existed, or didn’t know how to ask for, finds you.”
    August 13, 2011
    What does that mean for search engines? Will they be left behind?
  • Role of Today’s Web
    August 13, 2011
    Marketing
    Tool
    Information Finding Tool
    Media
    Tool
  • Social Search
    Takes into account the “social graph” of the person initiating the query
    Search activity in which users pose a question to their social networks
    Search systems using statistical analytics over traces left behind by others
    Conducting a search over an existing database of content previously provided by other users such as searching over the collection of public Twitter posts or searching through an archive of questions and answers
    August 13, 2011
  • Social Search Benefits
    Reduced impact of link spamby lesser reliance on link structure of Web pages
    Increased relevance due to each result being selected by users
    Web pages relevance judged from reader’s perspective rather than author’s perspective
    More current results through constant feedback
    August 13, 2011
    Improvements achieved by social search have not been quantified so far
  • What Do People Ask Social Networks?
    Meredith Ringel Morris, MSR
    Jaime Teevan, MSR
    Katrina Panovich, MIT
    August 13, 2011
  • Questions about People’s Questions
    What questions do people ask?
    How are the questions phrased?
    What are the question types and topics?
    Who asks which questions and why?
    Which questions get answered?
    How is answer speed and utility perceived?
    What are people’s motivations for answering?
    August 13, 2011
  • Survey of Asking via Status Messages
    Survey content
    Used a status message to ask a question?
    Frequency of asking, question type, responses received
    Provide an example
    Answered a status message question?
    Why or why not?
    Provide an example
    624 participants
    Focus on Facebook and Twitter behavior
    August 13, 2011
  • Questions: Types
    August 13, 2011
  • Questions: Topics
    Missing: Health, Religion
    Politics, Dating, and Finance
    August 13, 2011
  • Questions: Who Asks What
    August 13, 2011
    men
    old
    Twitter
    women
    Facebook
    young
  • Questions: Motives for Asking
    August 13, 2011
  • Questions About People’s Questions
    What questions do people ask?
    How are the questions phrased?
    What are the question types and topics?
    Who asks which questions and why?
    Which questions get answered?
    How is answer speed and utility perceived?
    What are people’s motivations for answering?
    August 13, 2011
  • Answers: Speed and Utility
    94% of questions received an answer
    Answer speed
    A quarter in 30 minutes, almost all in a day
    People expected faster, but satisfied with speed
    Shorter questions got more useful responses
    Answer utility
    69% of responses helpful
    August 13, 2011
  • Answers: Speed and Utility
    Fast
    No correlation
    Unhelpful
    August 13, 2011
  • Answers: Motives for Answering
    Motives for Not Answering
    - Don’t know the answer
    - Private topic
    - Question impersonal
    August 13, 2011
  • Answers About People’s Questions
    The questions people ask
    Short, directed to “anyone”
    Subjective questions on acceptable topics
    Social relationships important motivators
    The questions that get answered
    Fast, helpful responses, related to length and type
    Answers motivated by altruism and expertise
    August 13, 2011
  • Enhancing Search using Social Network Features
    Recency Crawling and Ranking
    Identification of Hot Topics on Social Web [YQG+11]
    News in the Making
    Trend analysis
    Event detection
    Real-Time Search
    Information Diffusion and Influence Analysis
    Community Detection
    Opinion Mining
    August 13, 2011
  • August 13, 2011
    Nodes, Ties and Influence
  • Importance of Nodes
    Not all nodes are equally important
    Centrality Analysis
    Find out the most important nodes in one network
    Commonly-used Measures
    Degree Centrality
    Closeness Centrality
    Betweenness Centrality
    Eigenvector Centrality
    August 13, 2011
  • Degree Centrality
    The importance of a node is determined by the number of nodes adjacent to it
    The larger the degree, the more import the node is
    Only a small number of nodes have high degrees in many real-life networks
    Degree Centrality
    Normalized Degree Centrality:
    For node 1, degree centrality is 3;
    Normalized degree centrality is
    3/(9-1)=3/8.
    August 13, 2011
  • Closeness Centrality
    “Central” nodes are important, as they can reach the whole network more quickly than non-central nodes
    Importance measured by how close a node is to other nodes
    Average Distance
    Closeness Centrality
    August 13, 2011
  • Closeness Centrality Example
    Node 4 is more central than node 3
    August 13, 2011
  • Betweenness Centrality
    Node betweenness counts the number of shortest paths that pass one node
    Nodes with high betweenness are important in communication and information diffusion
    Betweenness Centrality
    The number of shortest paths between s and t
    The number of shortest paths between s and t that pass vi
    26
    August 13, 2011
  • Betweenness Centrality Example
    The number of shortest paths between s and t
    The number of shortest paths between s and t that pass vi
    August 13, 2011
  • Eigenvector Centrality
    One’s importance is determined by his friends’
    If one has many important friends, he should be important as well.
    The centrality corresponds to the top eigenvector of the adjacency matrix A.
    A variant of this eigenvector centrality is the PageRank score.
    August 13, 2011
  • Weak and Strong Ties
    In practice, connections are not of the same strength
    Interpersonal social networks are composed of strong ties (close friends) and weak ties (acquaintances)
    Strong ties and weak ties play different roles for community formation and information diffusion
    Strength of Weak Ties (Granovetter, 1973)
    Occasional encounters with distant acquaintances can provide important information about new opportunities for job search
    August 13, 2011
  • Connections in Social Media
    • Social Media allows users to connect to each other more easily than ever
    One user might have thousands of friends online
    Who are the most important ones among your 300 Facebook friends?
    • Imperative to estimate the strengths of ties for advanced analysis
    Analyze network topology
    Learn from User Profiles and Attributes
    August 13, 2011
  • Learning from Network Topology
    Bridges connecting two different communities are weak ties
    An edge is a bridge if its removal results in disconnection of its terminal nodes
    e(2,5) is a bridge
    e(2,5) is NOT a bridge
    August 13, 2011
  • “shortcut” Bridge
    Bridges are rare in real-life networks
    Alternatively, one can relax the definition by checking if the distance between two terminal nodes increases if the edge is removed
    The larger the distance, the weaker the tie is
    d(2,5) = 4 if e(2,5) is removed
    d(5,6) = 2 if e(5,6) is removed
    e(5,6) is a stronger tie than e(2,5)
    August 13, 2011
  • Neighborhood Overlap
    Tie Strength can be measured based on neighborhood overlap; the larger the overlap, the stronger the tie is
    -2 in the denominator is to exclude vi and vj
    August 13, 2011
  • Neighborhood Overlap
    Tie Strength can be measured based on neighborhood overlap; the larger the overlap, the stronger the tie is
    -2 in the denominator is to exclude vi and vj
    August 13, 2011
  • Learning from Profiles and Interactions
    Twitter: one can follow others without followee’s confirmation
    The real friendship network is determined by the frequency two users talk to each other, rather than the follower-followee network
    The real friendship network is more influential in driving Twitter usage
    Strengths of ties can be predicted accurately based on various information from Facebook
    Friend-initiated posts, message exchanged in wall post, number of mutual friends, etc.
    Learning numeric link strength by maximum likelihood estimation
    User profile similarity determines the strength
    Link strength in turn determines user interaction
    Maximize the likelihood based on observed profiles and interactions
    35
    August 13, 2011