Arjumand Younus

Web Science Research Group

Institute of Business Administration (IBA)

CSE509: Introduction to Web Science

and Technology

Lecture 6: Social Information Re-trieval

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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

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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

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Role of Today’s Web

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Marketing Tool

Information Finding Tool

Media Tool

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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.”

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What does that mean for search engines? Will they be left behind?

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Role of Today’s Web

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Marketing Tool

Information Finding Tool

Media Tool

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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

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Social Search Benefits

Reduced impact of link spam by 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

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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

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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?

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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

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Questions: Types

Type % Example

Recommendation 29% Building a new playlist – any ideas for

good running songs?

Opinion 22% I am wondering if I should buy the Kitchen-Aid ice cream maker?

Factual 17% Anyone know a way to put Excel charts into LaTeX?

Rhetorical 14% Why are men so stupid?

Invitation 9% Who wants to go to Navya Lounge this evening?

Favor 4% Need a babysitter in a big way tonight… anyone??

Social connection 3% I am hiring in my team. Do you know

anyone who would be interested?

Offer 1% Could any of my friends use boys size 4 jeans?

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Questions: Topics

Topic % Example

Technology 29% Anyone know if WOW works on Windows 7?

Entertainment 17% Was seeing Up in the theater worth the

money?

Home & Family 12% So what’s the going rate for the tooth

fairy?

Professional 11% Which university is better for Masters? Cornell or Georgia Tech?

Places 8%Planning a trip to Whistler in the off-season. Recommendation on sites to see?

Restaurants 6% Hanging in Ballard tonight. Dinner recs?

Current events 5% What is your opinion on the recent

proposition that was passed in California?

Shopping 5% What’s a good Mother’s Day gift?

Philosophy 2% What would you do if you had a week to live?

Missing: Health, Religion

Politics, Dating, and Finance

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Questions: Who Asks What

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Type

Recommendation

Opinion

Factual

Rhetorical

Invitation

Favor

Social connection

Offer

Topic

Technology

Entertainment

Home & Family

Professional

Places

Restaurants

Current events

Shopping

Philosophy

men

women

old

young

Twitter

Facebook

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Questions: Motives for Asking

Topic % Example

Trust 24.8%

I trust my friends more than I trust strangers.

Subjective

21.5%

Search engine can provide data but not an opinion.

Thinks search would fail

15.2%

I’m pretty search engine couldn’t answer a question of that nature.

Audience 14.9% Friends with kids, first hand real experience.

Connect 12.4%

I wanted my friends to know I was asking the question.

Speed 6.6% Quick response time, no formalities.

Context 5.4% Friends know my tastes.

Tried search 5.4% I tried searching and didn’t get good results.

Easy 5.4% Didn’t want to look through multiple search results.

Quality 4.1% Human-vetted responses.

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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?

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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

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Database and Multimedia Lab 18

Answers: Speed and Utility

Type

Recommendation

Opinion

Factual

Rhetorical

Invitation

Favor

Social connection

Offer

Topic

Technology

Entertainment

Home & Family

Professional

Places

Restaurants

Current events

Shopping

Philosophy

Fast

UnhelpfulNo

correlation

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Answers: Motives for Answering

Motive % Example

Altruism 37.0 Just trying to be helpful.

Expertise 31.9 If I’m an expert in the area.

Question 15.4 Interest in the topic.

Relationship 13.7 If I know and like the person.

Connect 13.5 Keeps my network alive.

Free time 12.3 Boredome/procrastination.

Social capital 10.5 I will get help when I need it myself.

Obligation 5.4 A tit-for-tat.

Humor 3.7 Thinking I might have a witty response.

Ego 3.4 Wish to seem knowledgeable.

Motives for Not

Answering

- Don’t know the answer

- Private topic

- Question im

personal

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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

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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

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Nodes, Ties and Influence

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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

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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.

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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

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Closeness Centrality Example

Node 4 is more central than node 3August 13, 2011

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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

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Betweenness Centrality Example

The number of shortest paths between s and t

The number of shortest paths between s and t that pass vi

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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.

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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

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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

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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

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“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)

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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

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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

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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

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