SlideShare une entreprise Scribd logo
1  sur  26
Télécharger pour lire hors ligne
Twittering Dissent
   Social Web Data Streams as a Basis for

Agent Based Models of Opinion Dynamics




Presentation held at GOR09 08/04/2009
Authors
   Pascal Jürgens              Andreas Jungherr
 pascal.juergens@gmail.com      andreas.jungherr@gmail.com
      Graduate Student               Graduate Student
Department of Communication    Department of Political Science
University of Mainz, Germany   University of Mainz, Germany
1


The Challenge
           To gain

valid, representative, relevant

insight into human behavior

      from online data.
2

     Audience Dissent at SXSW
           Conference

"Never, ever have I seen such a train wreck of
an interview," said Jason Pontin on Twitter.
"Talk about something interesting," one attendee
yelled about halfway through the keynote. The remark was met with
waves of cheering and applause
                                — Wired.com article about the event
3


The Event of Interest
•   Interview with Mark Zuckerberg of Facebook at
    SXSW conference, Austin (Tx), March 9th, 2008
•   During the interview, negative comments
    posted by audience members spread through
    the microblogging service Twitter
•   Unrest from the electronic backchannel spills
    over into the room, leading to booing, shouted
    interjections and eventually the abortion of the
    interview
4


        Microblogging Overview
                1. long term, one-way, mass audience communication


                                      normal
                                      message
user                                                                       subscribers
                       @
                     message




                                  non - subscribers


       2. ad hoc, bidirectional, mass audience + select recipient communication
5

             The Event:
         Interesting Aspects
•   Unique interaction of online/offline behavior

•   Backchannel (invisible communication)

•   Ad-hoc formation and group action in a small
    group setting

•   Possibly an example of “augmented reality”

•   All within strict spatial and temporal
    constraints
6


     Research Challenges
•   Problem: Ex Post proof of causality is difficult
    (qualitative in-depth interviews might be an
    option)
•   Goal: Relate online data to offline situation
    without data
•   Therefore: Our approach: Infuse online data into
    mathematical model on the basis of established
    social-psychological theories - then use model
    as basis for hypothesis generation
7


          Data — Benefits

•   (mostly) unique users

•   Central data repository

•   User-coded intents:
    replies (@user), topics (#topic)

•   Low syntactical complexity
    as a result of limited message length (140
    characters)
8


            Data — Risks

•   No guarantee of data availability

•   Usage limit — limited data acquisition

•   Message size may discourage complex issues as
    conversation topics

•   Low temporal resolution (time zone ambiguity,
    latencies)
9


                      Our Data Set




      Robert Scoble
(well connected blogger)   All users directly addressed    Messages during
 over 2700 subscribers      by R. Scoble (@-messages)     interview (N = 259)
10


                Our Data Set
    Interview                                      Replacement Session

                             all tweets incl. neutral
                             negative tweets




•   Messages hand-coded for relevance and
    negativity

•   Time series supports role of backchannel
11


             Our Data Set
60%                 58%   57%



45%                                                 Neutral Tweets
      39%                                           Positive Tweets
                                          36%
                                                    Negative Tweets

30%

                                                   N total = 814
                                                  N during = 259
15%
                                  7%
            3%
0%      During Keynote     Complete Time Series
12


              Modeling
•   Known from physics, econonomics, recently
    spawned field of “econophysics”
•   Roots in game theory, particle simulation
•   Limited number of variables, Monte Carlo
    approach to find out behavior
•   Good in conditions of high uncertainty
    regarding interplay of factors (explorative
    theory building)
•   Bad for forecasts, establishing causality
13

      Established Models of
         Communication

•   Models of Opinion Dynamics and Formation

•   Hegselmann / Krause Model: Repeated
    averaging of opinions with neighbors

•   “In the HK model, each agent moves to the
    average opinion of all agents which lie in her
    area of confidence (including herself ).” (Lorenz
    2007)
14


     Building the Model




Snapshot:               Time plot:
Grid with backchannel   Opinion space over time
15


     Model Step-by-Step
•   Start with random distribution of opinions
    (gauss distribution) on 32 x 32 grid
•   Twenty randomly chosen agents are
    interconnected via a backchannel. Each of those
    agents is connected to ten peer agents
•   On each tick, every agent builds a new opinion
    Oj from the current opinions Oi of her eight
    immediate neighbors and her own
•   Connected agents build a new opinion from the
    eight neighbors and all connected agents
16


    Introducing Tolerance

•   Tolerance = acceptance or rejection of opinions
•   Rejected opinions are disregarded in the process
    of opinion formation
•   In modeling: bounded confidence (ε)
•   Only agents within a certain distance on the
    one-dimensional opinion space are considered
17


Sample Model Run
18


                              Model Performance
                                                                           .4 .5
                       1000                                                .3

                       800
n of negative agents




                                                                      bounded
                       600             negative backchannel          confidence
                                       backchannel with positive /
                                       negative ratio from data
                       400
                                                                           0 1

                       200
                                                                           .2

                                 100      200                  300   400   t
19


       Model Findings
•   Tolerance of agents (bounded confidence) is the
    key factor for consensus / overwhelming

•   The number of connected agents (connection
    density) speeds up the process, but does not
    affect the final outcome

•   A surprisingly simple model captures social
    interaction under the influence of pervasive
    electronic communication
20

Modeling for Hypothesis Generation
        from Online Data



•   Similar operationalizations
•   Can be based on empirical data
•   Based on existing theories / models
•   Good science workflow (explicit assumptions)
•   Known / measured factors can be plugged in
21


    Further Investigations

•   Bifurcation analysis of model design: create
    general description and describe relevant
    factors and their turning points

•   Controlled experimental corroboration

•   Events with longer time-scale and without
    spatial restrictions
Thank you!
 For your interest
i


Logistics: Lessons Learned
In early summer 2008, Twitter cut access to
archives before april 2008 (subsequently
extended).
➡ Corroboration requires published data sets

Both data acquisition and modeling require
substantial computational capacity. Some data is
only accessible through special agreements with
companies.
➡ Joined research efforts might be needed
ii


                    Literature
•   Why we twitter: understanding microblogging usage and communities.
    Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on
    Web mining and social network analysis (2007) pp. 56-65

•   Rainer Hegselmann and Ulrich Krause. Opinion Dynamics and
    Bounded Confidence, Models, Analysis and Simulation. Journal of
    Artificial Societies and Social Simulation, 5(3), 2002.

•   Zheng and Hui. Dynamics of opinion formation in a small-world
    network. arXiv (2005) vol. physics.soc-ph

•   Lorenz. Continuous Opinion Dynamics under Bounded Confidence: A
    Survey. arXiv (2007) vol. physics.soc-phJava et al.

•   Photo credit: “Zuckerberg Keynote” by pescatello (CC Attribution 2.0)

Contenu connexe

Similaire à Twittering Dissent

Opinion Dynamics on Networks
Opinion Dynamics on NetworksOpinion Dynamics on Networks
Opinion Dynamics on NetworksMason Porter
 
Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....
Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....
Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....R R
 
Crowdsourcing for HCI Research with Amazon Mechanical Turk
Crowdsourcing for HCI Research with Amazon Mechanical TurkCrowdsourcing for HCI Research with Amazon Mechanical Turk
Crowdsourcing for HCI Research with Amazon Mechanical TurkEd Chi
 
Managing Online Business Communities
Managing Online Business CommunitiesManaging Online Business Communities
Managing Online Business CommunitiesSteffen Staab
 
Fundamentals of Deep Recommender Systems
 Fundamentals of Deep Recommender Systems Fundamentals of Deep Recommender Systems
Fundamentals of Deep Recommender SystemsWQ Fan
 
Christoph Barrett - Policy Informatics at Societal Scale
Christoph Barrett - Policy Informatics at Societal ScaleChristoph Barrett - Policy Informatics at Societal Scale
Christoph Barrett - Policy Informatics at Societal ScaleGlobal Risk Forum GRFDavos
 
Collective Spammer Detection in Evolving Multi-Relational Social Networks
Collective Spammer Detection in Evolving Multi-Relational Social NetworksCollective Spammer Detection in Evolving Multi-Relational Social Networks
Collective Spammer Detection in Evolving Multi-Relational Social NetworksTuri, Inc.
 
Big-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the Obvious
Big-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the ObviousBig-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the Obvious
Big-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the ObviousLaks Lakshmanan
 
Big-O(Q) Social Network Analytics
Big-O(Q) Social Network AnalyticsBig-O(Q) Social Network Analytics
Big-O(Q) Social Network AnalyticsLaks Lakshmanan
 
Velocity19 Berlin: Swarming, Cynefin… and avoiding the problems of becoming a...
Velocity19 Berlin: Swarming, Cynefin…and avoiding the problems of becoming a...Velocity19 Berlin: Swarming, Cynefin…and avoiding the problems of becoming a...
Velocity19 Berlin: Swarming, Cynefin… and avoiding the problems of becoming a...Jon Stevens-Hall
 
STING: Spatio-Temporal Interaction Networks and Graphs for Intel Platforms
STING: Spatio-Temporal Interaction Networks and Graphs for Intel PlatformsSTING: Spatio-Temporal Interaction Networks and Graphs for Intel Platforms
STING: Spatio-Temporal Interaction Networks and Graphs for Intel PlatformsJason Riedy
 
WSDM 2018 Tutorial on Influence Maximization in Online Social Networks
WSDM 2018 Tutorial on Influence Maximization in Online Social NetworksWSDM 2018 Tutorial on Influence Maximization in Online Social Networks
WSDM 2018 Tutorial on Influence Maximization in Online Social NetworksCigdem Aslay
 
Immersive Recommendation
Immersive RecommendationImmersive Recommendation
Immersive Recommendation承剛 謝
 
Deep Credit Risk Ranking with LSTM with Kyle Grove
Deep Credit Risk Ranking with LSTM with Kyle GroveDeep Credit Risk Ranking with LSTM with Kyle Grove
Deep Credit Risk Ranking with LSTM with Kyle GroveDatabricks
 
Designing The Social In
Designing The Social InDesigning The Social In
Designing The Social InErin Malone
 
Recommender system algorithm and architecture
Recommender system algorithm and architectureRecommender system algorithm and architecture
Recommender system algorithm and architectureLiang Xiang
 
项亮 推荐系统实践 从入门到精通
项亮 推荐系统实践 从入门到精通 项亮 推荐系统实践 从入门到精通
项亮 推荐系统实践 从入门到精通 topgeek
 

Similaire à Twittering Dissent (20)

Opinion Dynamics on Networks
Opinion Dynamics on NetworksOpinion Dynamics on Networks
Opinion Dynamics on Networks
 
Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....
Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....
Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....
 
Crowdsourcing for HCI Research with Amazon Mechanical Turk
Crowdsourcing for HCI Research with Amazon Mechanical TurkCrowdsourcing for HCI Research with Amazon Mechanical Turk
Crowdsourcing for HCI Research with Amazon Mechanical Turk
 
Managing Online Business Communities
Managing Online Business CommunitiesManaging Online Business Communities
Managing Online Business Communities
 
Fundamentals of Deep Recommender Systems
 Fundamentals of Deep Recommender Systems Fundamentals of Deep Recommender Systems
Fundamentals of Deep Recommender Systems
 
Christoph Barrett - Policy Informatics at Societal Scale
Christoph Barrett - Policy Informatics at Societal ScaleChristoph Barrett - Policy Informatics at Societal Scale
Christoph Barrett - Policy Informatics at Societal Scale
 
Collective Spammer Detection in Evolving Multi-Relational Social Networks
Collective Spammer Detection in Evolving Multi-Relational Social NetworksCollective Spammer Detection in Evolving Multi-Relational Social Networks
Collective Spammer Detection in Evolving Multi-Relational Social Networks
 
Sybrandt Thesis Proposal Presentation
Sybrandt Thesis Proposal PresentationSybrandt Thesis Proposal Presentation
Sybrandt Thesis Proposal Presentation
 
Big-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the Obvious
Big-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the ObviousBig-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the Obvious
Big-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the Obvious
 
Big-O(Q) Social Network Analytics
Big-O(Q) Social Network AnalyticsBig-O(Q) Social Network Analytics
Big-O(Q) Social Network Analytics
 
Velocity19 Berlin: Swarming, Cynefin… and avoiding the problems of becoming a...
Velocity19 Berlin: Swarming, Cynefin…and avoiding the problems of becoming a...Velocity19 Berlin: Swarming, Cynefin…and avoiding the problems of becoming a...
Velocity19 Berlin: Swarming, Cynefin… and avoiding the problems of becoming a...
 
STING: Spatio-Temporal Interaction Networks and Graphs for Intel Platforms
STING: Spatio-Temporal Interaction Networks and Graphs for Intel PlatformsSTING: Spatio-Temporal Interaction Networks and Graphs for Intel Platforms
STING: Spatio-Temporal Interaction Networks and Graphs for Intel Platforms
 
WSDM 2018 Tutorial on Influence Maximization in Online Social Networks
WSDM 2018 Tutorial on Influence Maximization in Online Social NetworksWSDM 2018 Tutorial on Influence Maximization in Online Social Networks
WSDM 2018 Tutorial on Influence Maximization in Online Social Networks
 
Immersive Recommendation
Immersive RecommendationImmersive Recommendation
Immersive Recommendation
 
Slides ecir2016
Slides ecir2016Slides ecir2016
Slides ecir2016
 
Deep Credit Risk Ranking with LSTM with Kyle Grove
Deep Credit Risk Ranking with LSTM with Kyle GroveDeep Credit Risk Ranking with LSTM with Kyle Grove
Deep Credit Risk Ranking with LSTM with Kyle Grove
 
Designing The Social In
Designing The Social InDesigning The Social In
Designing The Social In
 
ICS2208 lecture7
ICS2208 lecture7ICS2208 lecture7
ICS2208 lecture7
 
Recommender system algorithm and architecture
Recommender system algorithm and architectureRecommender system algorithm and architecture
Recommender system algorithm and architecture
 
项亮 推荐系统实践 从入门到精通
项亮 推荐系统实践 从入门到精通 项亮 推荐系统实践 从入门到精通
项亮 推荐系统实践 从入门到精通
 

Dernier

Quality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICEQuality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICESayali Powar
 
How to Solve Singleton Error in the Odoo 17
How to Solve Singleton Error in the  Odoo 17How to Solve Singleton Error in the  Odoo 17
How to Solve Singleton Error in the Odoo 17Celine George
 
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptxSandy Millin
 
How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17Celine George
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfYu Kanazawa / Osaka University
 
3.19.24 Urban Uprisings and the Chicago Freedom Movement.pptx
3.19.24 Urban Uprisings and the Chicago Freedom Movement.pptx3.19.24 Urban Uprisings and the Chicago Freedom Movement.pptx
3.19.24 Urban Uprisings and the Chicago Freedom Movement.pptxmary850239
 
HED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfHED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfMohonDas
 
Benefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationBenefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationMJDuyan
 
Philosophy of Education and Educational Philosophy
Philosophy of Education  and Educational PhilosophyPhilosophy of Education  and Educational Philosophy
Philosophy of Education and Educational PhilosophyShuvankar Madhu
 
M-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxM-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxDr. Santhosh Kumar. N
 
In - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxIn - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxAditiChauhan701637
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxraviapr7
 
How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17Celine George
 
Ultra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxUltra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxDr. Asif Anas
 
How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17Celine George
 
Practical Research 1 Lesson 9 Scope and delimitation.pptx
Practical Research 1 Lesson 9 Scope and delimitation.pptxPractical Research 1 Lesson 9 Scope and delimitation.pptx
Practical Research 1 Lesson 9 Scope and delimitation.pptxKatherine Villaluna
 
How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17Celine George
 
The Singapore Teaching Practice document
The Singapore Teaching Practice documentThe Singapore Teaching Practice document
The Singapore Teaching Practice documentXsasf Sfdfasd
 

Dernier (20)

Quality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICEQuality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICE
 
How to Solve Singleton Error in the Odoo 17
How to Solve Singleton Error in the  Odoo 17How to Solve Singleton Error in the  Odoo 17
How to Solve Singleton Error in the Odoo 17
 
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
 
How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17
 
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdfPersonal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
 
3.19.24 Urban Uprisings and the Chicago Freedom Movement.pptx
3.19.24 Urban Uprisings and the Chicago Freedom Movement.pptx3.19.24 Urban Uprisings and the Chicago Freedom Movement.pptx
3.19.24 Urban Uprisings and the Chicago Freedom Movement.pptx
 
HED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfHED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdf
 
Benefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationBenefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive Education
 
Philosophy of Education and Educational Philosophy
Philosophy of Education  and Educational PhilosophyPhilosophy of Education  and Educational Philosophy
Philosophy of Education and Educational Philosophy
 
M-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxM-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptx
 
Prelims of Kant get Marx 2.0: a general politics quiz
Prelims of Kant get Marx 2.0: a general politics quizPrelims of Kant get Marx 2.0: a general politics quiz
Prelims of Kant get Marx 2.0: a general politics quiz
 
In - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxIn - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptx
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptx
 
How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17
 
Ultra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxUltra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptx
 
How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17
 
Practical Research 1 Lesson 9 Scope and delimitation.pptx
Practical Research 1 Lesson 9 Scope and delimitation.pptxPractical Research 1 Lesson 9 Scope and delimitation.pptx
Practical Research 1 Lesson 9 Scope and delimitation.pptx
 
How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17
 
The Singapore Teaching Practice document
The Singapore Teaching Practice documentThe Singapore Teaching Practice document
The Singapore Teaching Practice document
 

Twittering Dissent

  • 1. Twittering Dissent Social Web Data Streams as a Basis for Agent Based Models of Opinion Dynamics Presentation held at GOR09 08/04/2009
  • 2. Authors Pascal Jürgens Andreas Jungherr pascal.juergens@gmail.com andreas.jungherr@gmail.com Graduate Student Graduate Student Department of Communication Department of Political Science University of Mainz, Germany University of Mainz, Germany
  • 3. 1 The Challenge To gain valid, representative, relevant insight into human behavior from online data.
  • 4. 2 Audience Dissent at SXSW Conference "Never, ever have I seen such a train wreck of an interview," said Jason Pontin on Twitter. "Talk about something interesting," one attendee yelled about halfway through the keynote. The remark was met with waves of cheering and applause — Wired.com article about the event
  • 5. 3 The Event of Interest • Interview with Mark Zuckerberg of Facebook at SXSW conference, Austin (Tx), March 9th, 2008 • During the interview, negative comments posted by audience members spread through the microblogging service Twitter • Unrest from the electronic backchannel spills over into the room, leading to booing, shouted interjections and eventually the abortion of the interview
  • 6. 4 Microblogging Overview 1. long term, one-way, mass audience communication normal message user subscribers @ message non - subscribers 2. ad hoc, bidirectional, mass audience + select recipient communication
  • 7. 5 The Event: Interesting Aspects • Unique interaction of online/offline behavior • Backchannel (invisible communication) • Ad-hoc formation and group action in a small group setting • Possibly an example of “augmented reality” • All within strict spatial and temporal constraints
  • 8. 6 Research Challenges • Problem: Ex Post proof of causality is difficult (qualitative in-depth interviews might be an option) • Goal: Relate online data to offline situation without data • Therefore: Our approach: Infuse online data into mathematical model on the basis of established social-psychological theories - then use model as basis for hypothesis generation
  • 9. 7 Data — Benefits • (mostly) unique users • Central data repository • User-coded intents: replies (@user), topics (#topic) • Low syntactical complexity as a result of limited message length (140 characters)
  • 10. 8 Data — Risks • No guarantee of data availability • Usage limit — limited data acquisition • Message size may discourage complex issues as conversation topics • Low temporal resolution (time zone ambiguity, latencies)
  • 11. 9 Our Data Set Robert Scoble (well connected blogger) All users directly addressed Messages during over 2700 subscribers by R. Scoble (@-messages) interview (N = 259)
  • 12. 10 Our Data Set Interview Replacement Session all tweets incl. neutral negative tweets • Messages hand-coded for relevance and negativity • Time series supports role of backchannel
  • 13. 11 Our Data Set 60% 58% 57% 45% Neutral Tweets 39% Positive Tweets 36% Negative Tweets 30% N total = 814 N during = 259 15% 7% 3% 0% During Keynote Complete Time Series
  • 14. 12 Modeling • Known from physics, econonomics, recently spawned field of “econophysics” • Roots in game theory, particle simulation • Limited number of variables, Monte Carlo approach to find out behavior • Good in conditions of high uncertainty regarding interplay of factors (explorative theory building) • Bad for forecasts, establishing causality
  • 15. 13 Established Models of Communication • Models of Opinion Dynamics and Formation • Hegselmann / Krause Model: Repeated averaging of opinions with neighbors • “In the HK model, each agent moves to the average opinion of all agents which lie in her area of confidence (including herself ).” (Lorenz 2007)
  • 16. 14 Building the Model Snapshot: Time plot: Grid with backchannel Opinion space over time
  • 17. 15 Model Step-by-Step • Start with random distribution of opinions (gauss distribution) on 32 x 32 grid • Twenty randomly chosen agents are interconnected via a backchannel. Each of those agents is connected to ten peer agents • On each tick, every agent builds a new opinion Oj from the current opinions Oi of her eight immediate neighbors and her own • Connected agents build a new opinion from the eight neighbors and all connected agents
  • 18. 16 Introducing Tolerance • Tolerance = acceptance or rejection of opinions • Rejected opinions are disregarded in the process of opinion formation • In modeling: bounded confidence (ε) • Only agents within a certain distance on the one-dimensional opinion space are considered
  • 20. 18 Model Performance .4 .5 1000 .3 800 n of negative agents bounded 600 negative backchannel confidence backchannel with positive / negative ratio from data 400 0 1 200 .2 100 200 300 400 t
  • 21. 19 Model Findings • Tolerance of agents (bounded confidence) is the key factor for consensus / overwhelming • The number of connected agents (connection density) speeds up the process, but does not affect the final outcome • A surprisingly simple model captures social interaction under the influence of pervasive electronic communication
  • 22. 20 Modeling for Hypothesis Generation from Online Data • Similar operationalizations • Can be based on empirical data • Based on existing theories / models • Good science workflow (explicit assumptions) • Known / measured factors can be plugged in
  • 23. 21 Further Investigations • Bifurcation analysis of model design: create general description and describe relevant factors and their turning points • Controlled experimental corroboration • Events with longer time-scale and without spatial restrictions
  • 24. Thank you! For your interest
  • 25. i Logistics: Lessons Learned In early summer 2008, Twitter cut access to archives before april 2008 (subsequently extended). ➡ Corroboration requires published data sets Both data acquisition and modeling require substantial computational capacity. Some data is only accessible through special agreements with companies. ➡ Joined research efforts might be needed
  • 26. ii Literature • Why we twitter: understanding microblogging usage and communities. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (2007) pp. 56-65 • Rainer Hegselmann and Ulrich Krause. Opinion Dynamics and Bounded Confidence, Models, Analysis and Simulation. Journal of Artificial Societies and Social Simulation, 5(3), 2002. • Zheng and Hui. Dynamics of opinion formation in a small-world network. arXiv (2005) vol. physics.soc-ph • Lorenz. Continuous Opinion Dynamics under Bounded Confidence: A Survey. arXiv (2007) vol. physics.soc-phJava et al. • Photo credit: “Zuckerberg Keynote” by pescatello (CC Attribution 2.0)