An Agent-Based Model of Online Communication
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AN AGENT-BASED MODEL OF ONLINE COMMUNICATION SIMONE GABBRIELLINI POSTDOC @ GEMASS, CNRS & PARIS-SORBONNE EMAIL: [email protected] 1
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Transcript of An Agent-Based Model of Online Communication
AN AGENT-BASED MODEL OF ONLINE COMMUNICATIONSIMONE GABBRIELLINI POSTDOC @ GEMASS, CNRS & PARIS-SORBONNE EMAIL: [email protected]
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WHY BOTHER OF WHAT HAPPENS ONLINE?A SHORT INTRODUCTION
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USERS’ BEHAVIORS GENERATE COMMUNICATION NETWORKSA SHORT INTRODUCTION
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• IDENTIFY IF A STRONG CORE OF USERS EMERGES (OR NOT) THAT INTERACTS MORE FREQUENTLY
• SCHOLARS HAVE INVESTIGATED MANY ASPECTS OF USERS’ BEHAVIOR ON ONLINE FORUMS
• FEW GENERATIVE MODELS SO FAR
ONLINEREAL LIFE
user
topic 1 topic 2 topic 3
user
topic 1 topic 2 topic 3
(a) (b)
topic 4 topic 5 topic 4 topic 5
5 posts1 post1 post
1 post
1 post 1 post
TWO WAYS OF DISTRIBUTING YOUR PARTICIPATIONONLINE FORUMS AS COMMUNICATION NETWORKS
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user
topic 1 topic 2 topic 3
user
topic 1 topic 2 topic 3
(a) (b)
topic 4 topic 5 topic 4 topic 5
5 posts1 post1 post
1 post
1 post 1 post
user 1 user 2 user 3 user 4
topic 1 topic 2 topic 3
user 1 user 2 user 3 user 4
topic 1 topic 2 topic 3
(a) (b)
user 1 user 2 user 3 user 4
topic 1 topic 2 topic 3
user 1 user 2 user 3 user 4
topic 1 topic 2 topic 3
time 0
time 1
COMPLEXITY GROWS AS MORE USERS GET INVOLVEDONLINE FORUMS AS COMMUNICATION NETWORKS
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user 1 user 2 user 3 user 4
topic 1 topic 2 topic 3
user 1 user 2 user 3 user 4
topic 1 topic 2 topic 3
(a) (b)
user 1 user 2 user 3 user 4
topic 1 topic 2 topic 3
user 1 user 2 user 3 user 4
topic 1 topic 2 topic 3
time 0
time 1
Q&A FORUMS VS. TOPIC-ORIENTED FORUMSGROUND TRUTH VS OPINIONS
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2 + x = 5, I tried x=4 but that doesn’t work… any help?
AN HYPOTHESIS AND A MODELUSERS’ ACTIVITY AND THREADS APPEAL ARE THE INGREDIENTS
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My model generates participation patterns in a thread-like structure,
calibrated and validated with empirical data belonging to three online forums.
how active a user is
how appealing a thread is
Dataset 1: HIPFORUMSHipForums is a weird place
250 thematic channels
I had access to one channel:
2003-2004
311 users
198 threads
2,357 posts
www.hipforums.com
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Dataset 2: MMORPGDataset collected during PhD.
32 thematic channels
I had access to all channels:
2004-2006
546 users
4,128 threads
55,544 posts
www.ilbona.it (no longer active)
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Dataset 3: ENERGETICAMBIENTE
Courtesy of the ePOLICY Project, 7th Framework Programme
17 thematic channels
I had access to all channels:
2010-2012
20,087 users
40,387 threads
486,104 posts
www.energeticambiente.it
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Comparing what?
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user 1thread 1
thread 2
thread 3
thread 4
user 2
user 3
Thread 1: Favorite All Purpose Gluten Free Flour? user 1: “I'm wondering what your favorite all purpose gluten free flour is?”
user 2: “I like both Jules and better batter, but for baking, my very favorite are the Mama's blends”
user 3: “I mix my own gluten-free flour blend. 1 cup sorghum flour, 1 cup potato starch, 1/2 cup almond flour, and 1 tsp xanthan gum.”
user 1: “Good but mine’s King Arthur for most things and Betty Crocker gluten-free for cookies and cakes.”
G = (⊤, ⊥, E) ⊤ is the set of top nodes
⊥ is the set of bottom nodes E⊆⊤×⊥
Thread 2: Bread Maker, Does It Matter If It Has A Gluten Free Setting? user 2: “have a limited choice of bread makers, only one of them has a gluten free setting…”
Thread 3: What Are You Eating For The Super Bowl? user 3: “What is everyone planning on eating for super bowl watching snacks?”
user 2: “I was just thinking about that...good timing!”
user 3: “I marinate my chicken tenders in buttermilk, sriracha, and spices. ”
Thread 4: Another Cookie Baking Question-Cookie Are Dry? user 3: “I don't know if it is just the way it is with gluten-free baking but we just made some thumbprint cookies…”
user 1: “Try almond flour or cream cheese. ”
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ABOUT THE MODEL(S)A COMPUTATIONAL MODEL AND A BASELINE COMPARISON
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Agents and links…
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agent 1thread 1
thread 2
thread 3
thread 4
agent 2
agent 3
agent 1thread 1
thread 2
thread 3
thread 4
agent 2
agent 3
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1
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1
1
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1
before after……
Appealing threads…the activity of user u is given by:
au = p – t/√(p + t) [Zhang, Ackerman and Adamic (2007)]
p is the total number of posts
t is the total number of threads
The appeal of a thread t opened by u is simply defined as the activity of u: at = au.
Threads appeal distribution is well approximated by a lognormal distribution
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M1 - Conversationalists
a user u prefers to post in a thread t, if someone belonging to the distance-2 neighborhood of u has already joined t
Put = LogDistr(at)/∑nLogDistr(an)
n = N(N(N(u))) – N(u) -> 0≤α≤1
n = N(u) -> 1 – α
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M2 - Expert/newbieanswer people select newbies’ threads to help them while newbies select experts’ threads to provide feedback and participate
wut = abs(au – at)
Put = wut/∑nwn
n = all threads -> 0≤β≤1
n = N(u) -> 1–β
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Mechanism 1conversations
Mechanism 2newbie/expert
1-γ0≤γ≤1
0≤α≤1 1-α 0≤β≤1 1-β
threads with
friends
all threads
threadsalready visited
threadsalready visited
in relation to appeal frequency
in relation to willingness
BECAUSE YOU GOT TO DO THINGS THE HARD WAY…A BASELINE RANDOM MODEL CONSTRAINED ON EMPIRICAL DEGREE DISTRIBUTION
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A constraint on the degree distribution accounts for the fact that different actors may have different abilities, thus they may be able to form more or less links.
Synthetic comparison
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Average cosine distance for empirical data and simulated dataHipforums MMOG EnergeticAmbiente
Random My model Random My model Random My model
Users
Deg distr 1 0.979 1 0.983 1 0.966
Avg nei deg 0.952 0.953* 0.938 0.963* 0.777 0.868*
# 2-dist nei 0.989* 0.934 0.940* 0.926 0.958* 0.882
Redundancy 0.833 0.908* 0.879 0.984* 0.530 0.750*
Threads
Deg distr 1 0.960 1 0.967 1 0.992
Avg nei deg 0.834 0.958* 0.836 0.960* 0.744 0.983*
# 2-dist nei 0.992* 0.984 0.991 0.993* 0.979* 0.956
Redundancy 0.754 0.926* 0.985 0.996* 0.884 0.905*
HIPFORUMSM1 is never used
agents post in threads according to how appealing threads are
agents post in a new thread 30% of the time
1 - users tend to reply quite a lot after a first post
2 - no like-with-like tendency emerges among users
Like a professional Q&A site: confirming a distinctive feature of how users communicate online regarding shared interests
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γ α β
0 - 0.3
MMORPGM1 used 80% of the time
agents tend to reply in a neighbors’ thread they already visited (1 – α = .6) more than posting in a previously unvisited neighbors’ thread (α = .4)
M2 used 20% of the time, this time they post equally in thread already known or not
According to the literature, this confirms that players interact with each other, make friends, create and cultivate communities
this richness is reflected in the rather informal communication style and a high tolerance to off-topic issues
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γ α β
0.8 0.4 0.5
ENERGETICAMBIENTE
M1 used 70% of the time
generally speaking, they tend to reply more in already visited threads than to connect to previously unvisited threads
M2 used 30% of the time
the Q&A nature leads users to reply consistently after a first post to share and discuss
unlike HIPFORUMS, here a strong core of users that interact more frequently with each others emerges - and this shapes the communication structure in a similar way than MMORPG
These stable communication cores could be behind “online communities”
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γ α β
0.7 0.3 0.3
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Users' degree distribution
degree
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empiricalrandomABM
1 2 3 5 7 11 18 29 46 73 126 234 426 1003
DEGREE DISTRIBUTIONMMORPG DATASET
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2e-035e-03
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Threads degree distribution
degree
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Average degree of neighbors for users
degree
ave n
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1 2 3 5 7 11 18 29 46 73 126 234 426 1003
AVERAGE NEIGHBORHOOD SIZEMMORPG DATASET
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degree
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2-DISTANCE NEIGHBORSMMORPG DATASET
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Redundancy for user degree
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REDUNDANCYMMORPG DATASET
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Main resultsthe model reproduces a trademark feature of online expertise networks, i.e., replies are motivated by interest in the subject matter.
the model reproduces the stratification needed when users form regular discussion groups
the model is able to discern when a strong core of users emerges who interact more frequently,
these stable communication cores may represent the step needed for an online forum to turn into an online community.
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Conclusionsthis approach shows that ABM represents a way of moving beyond the distinction between node-based and edge-based models
with ABM agents can use every mechanism possible, tailored to what actors actually do in a particular context,
this leads to a more realistic mimicking of the edge formation process
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ConclusionsMy model only scratches the surface:
is very simple,
uses data in a non evolutionary fashion
is not very fine grained
is an actor-based model that generates realistic post-reply networks
regardless of the specific nature of the discussion groups and topics addressed
might be useful in a wide range of contexts where online discussion dynamics are at stake (like diffusion research)
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Selected ReferencesAhuja, M.K., Galvin, J.E., 2003, “Socialization in Virtual Groups,” Journal of Management 29(2): 161–85.
Andresen, M.A., 2009, “Asynchronous Discussion Forums: Success Factors, Outcomes, Assessments, and Limitations,” Educational Technology and Society 12 (1): 249–57.
Ang, C.S., Zaphiris, P., 2010, “Social Roles of players in MMORPG Guilds,” Information, Communication and Society 13(4): 592–614.
Bainbridge, W.S., 2007, “The Scientific Research Potential of Virtual Worlds,” Science 317: 472–76.
Butler, B.S., 2001, “Membership Size, Communication Activity, and Sustainability: A Resource-Based Model of Online Social Structures,” Information Systems Research 12(4): 346–62.
Cole, H., Griffiths, M.D., 2007, “Social Interactions in Massively Multiplayer Online Role-Playing Gamer,” CyberPsychology and Behavior 10(4): 575–83.
Constant, D., Sproull, L., Kiesler, S., 1996, “The Kindness of Strangers: Usefulness of Electronic Weak Ties for Technical Advice,” Organization Science 7(2): 119–35.
Fayard, A-L., DeSanctis, G., 2005, “Evolution of an Online Forum for Knowledge Management Professionals: A Language Game Analysis,” Journal of Computer-Mediated, Communication 10(4): 1083–6101
Flache, A., Macy, M.W., 2011, “Small Worlds and Cultural Polarization,” The Journal of Mathematical Sociology 35(1-3): 146–76.
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Gabbriellini, S., Torroni P. “A New Framework for ABMs Based on Argumentative Reasoning,” In Advances in Social Simulation, Proceedings of the 9th Conference of the European Social Simulation Association, B. Kaminski, G. Koloch (eds.), AISC Series 229, Springer: 25–36.
Gleave, E., Welser, H.T., Lento, T., Smith, M., 2009, “A Conceptual and Operational Definition of ‘Social Role’ in Online Community,” In Proceedings of the 42nd Hawaii International Conference on System Sciences, IEEE.
Gleeson, J.P, Cellai D., Onnela, J.P., Porter, M.A, Reed-Tsochas, F., 2013, “A Simple Generative Model of Collective Online Behaviour”: arXiv:1305.7440.
González-Bailón, S., Kaltenbrunner, A., Banchs, R.E., 2010, “The Structure of Political Discussion Networks: A Model for the Analysis of Online Deliberation,” Journal of Information Technology 25(2): 230–43.
Guillaume, J-L., Latapy, M., 2006, “Bipartite Graphs as Models of Complex Networks,” Physica A 371: 795–813.
Haythornthwaite, C., 2007, “Social Networks and Online Community,” In The Oxford Handbook of Internet Psychology, A. Joinson (ed.), Oxford: Oxford University Press.
Hedström, P., Bearman, P., 2010, The Oxford Handbook of Analytical Sociology, Oxford: Oxford University Press.
Kollock, P., 1999, “The Economies of Online Cooperation: Gifts and Public Goods in Cyberspace,” In Communities in Cyberspace, M.A. Smith, P. Kollock (eds), London: Routledge.
Kollock, P., Smith, M., 1999, “Communities in cyberspace,” Communities in cyberspace: 3–25.
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Kossinets, G., Watts, D.J., 2006, “Empirical Analysis of an Evolving Social Network,” Science 311: 88–90.
Kumar, R., Novak, J., Tomkins, R., 2006, “Structure and Evolution of Online Social Networks,” In KDD ’06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY: ACM: 611–17.
Janssen, J., Bodemer, D., 2013, “Coordinated Computer-Supported Collaborative Learning: Awareness and Awareness Tools”, Educational Psychologist 48(1).
Latapy, M., Magnien, C., Del Vecchio, N., 2008, “Basic Notions for the Analysis of Large Two-Mode Networks,” Social Networks 30: 31–48.
Leskovec, J., Kleinberg, J., Faloutsos, C., 2005, “Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations,” In KDD 05: Proceedings of the Eleventh ACM SIGGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY: ACM: 177–87.
Macy, M.W., Willer, R., 2002, “From Factors to Actors: Computational Sociology and Agent-Based Modeling,” Annual Review of Sociology 28: 143–66.
Manzo, G., 2007, “Variables, Mechanisms, and Simulations: Can the Three Methods Be Synthesized? A Critical Analysis of the Literature », Revue Française de Sociologie 48(Supplement):.
Moody, J., 2010, “Network Dynamics,” In The Oxford Handbook of Analytical Sociology, P. Hedström, P. Bearman (eds.), Oxford: Oxford University Press.
Muncer, S., Loader, B., Burrows, R., Pleace, N., Nettleton, S., 2000, Form and Structure of Newsgroups Giving Social Support: A Network Approach,” Cyberpsychology and Behaviour 3: 1017–29.
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Petroczi, A., Nepusz, T., Baszo, F., 2007, “Measuring Tie-Strength in Virtual Social Networks,” Connections 27(2): 31–44.
Shi, X., Zhu, J., Cai, R., Zhang, L., 2009, “User Grouping Behavior in Online Forums,” In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY: ACM: 777–86.
Taylor, T., 1999, Life in Virtual Worlds: Plural Existence, Multimodalities, and other Online Research Challenges,” American Behavioral Scientist 43(3): 436–49.
Wasserman, S., Faust, K., 1999, Social Network Analysis: Methods and Applications, Cambridge: Cambridge University Press.
Welser, H.T., Gleave, E. Fisher, D., Smith, M., 2007, “Visualizing the Signatures of Social Roles in Online Discussion Groups,” Journal of Social Structure 8(2).
Wilensky, U., 1999, NetLogo, Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University: http://ccl.northwestern.edu/netlogo/.
Wojcieszak, M., Mutz, D., 2009, “Online Groups and Political Discourse: Do Online Discussion Spaces Facilitate Exposure to Political Disagreement?” Journal of Communication 59(1): 40–56.
Wu, H., Bu, J., Chen, C., Wang, C., Qiu, G., Zhang, L., Shen, J., 2010, “Modeling Dynamic Multi-Topic Discussions in Online Forums,” Proc. of AAAI 2010.
Zhang, J., Ackerman, M.S., Adamic, L.A., 2007, “Expertise Networks in Online Communities: Structure and Algorithms,” In WWW’07: Proceedings of the 16th International Conference on World Wide Web, New York, NY: ACM: 221–30.
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THANK YOU FOR YOUR ATTENTION!
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