Social Computing in the area of Big Data at the Know-Center Austria's leading competence center for data driven business and Big Data analytics

Social Computing @ Know-Center

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. Christoph Trattner 29.8.2014 – PUC, Chile

Social Computing in the area of Big Data at the Know-Center

Christoph Trattner

Know-Center [email protected]

@Graz University of Technology, Austria


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Before I will start in this talk I will talk a bit about myself and how it happened that I became

Head of the Social Computing Research Area at the Know-Center, Austria’s leading competence

center for data driven business and Big Data analytics


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Where do I come from (Austria)?


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Graz


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Academic Back-Ground?

§  Studies Computer Science at Graz University of Technology & University of Pittsburgh

§  Worked since 2009 as scientific researcher at the KMI & IICM (BSc 2008, MSc 2009)

§  My PhD thesis was on the Search & Navigation in Social Tagging Systems (defended 2012)

§  Since Feb. 2013 @ Know-Center §  Leading the SC Area §  At TUG:

§  WebScience §  Semantic Technologies


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My team

2 Post-‐Docs, 5 Pre-‐Docs (4 more to join soon J)

2 MSc student 2 BSc student

DI. Dieter Theiler

DI. Dominik Kowald

Dr. Peter Kraker

Mag. Sebastian Dennerlein

Dr. Elisabeth Lex

Mag. Matthias Rella

DI. Emanuel Lacic


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Thanks to my Collaborators


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What is my group doing?

… we research on novel methods and tools that exploit social data to generate a greater value for the individual, communities, companies and the society as whole. Our competences: •  Network & Web Science •  Science 2.0 •  Predictive Modeling •  Social Network Analysis •  Information Quality Assessment •  User Modeling •  Machine Learning and Data Mining •  Collaborative Systems

Our Services: •  Social Analytics: Hub-, Expert -, Community

-, Influencer -, Information Flow-, Trend (Event) Detection, etc.

•  Information Quality Assessment •  Social & Location-based Recommander

Systems •  Customer Segmentation •  Social Systems Design


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What type of projects are we running?

COMET NON-K

EU FWF

Industry Projects

FFG ...

Non-Industrial Projects


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Some industry partners...


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The Projects Project 1: Mendeley – UK Startup (recently acquired by Elsevier):

Interested in the problem of hirachical concept-based navigation.

Project 2: Blanc Noir – Austrian Startup: Interested in the problem

of recommending items to users through social data. Project 3: University of Pittsburgh & Several Austrian

companies: Interested on the usefulness of Twitter in academic conferences.


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Ok, lets start….


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Project 1

Mendeley – UK Startup (recently acquired by Elsevier):

Interested in the problem of hierarchical concept-based navigation.


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Research Question 1: What kind of meta-data is more useful for the task of navigation in information systems - tags or keywords? Externals involved: •  Mendeley, London, UK Helic, D., Körner, C., Granitzer, M., Strohmaier, M. and Trattner, C. 2012. Navigational Efficiency of Broad vs.

Narrow Folksonomies. In Proceedings of the 23rd ACM Conference on Hypertext and Social Media (HT 2012), ACM, New York, NY, USA, pp. 63-72.


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Mendeley


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§  We

Keywords

Tags

Mendeley Desktop


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Task

What is the best way to extract hirachies from enties such as social tags or keywords? What is more useful for navigation – keyword or tag hierarchies?


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Different types of hierarchy induction algorithms

Helic, D., Strohmaier, M., Trattner, C., Muhr M. and Lermann, K.: Pragmatic Evaluation of Folksonomies, In Proceedings of the 20th international conference on World Wide Web (WWW 2011), ACM, New York, NY, USA, 417-426, 2011.


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Issue (!!!)

...no literature on what type of hierarchy is best suited for the task of navigation...

D. J. Watts, P. S. Dodds, and M. E. J. Newman. Identity and search in social networks. Science, 296:1302–1305, 2002.

J. M. Kleinberg. Navigation in a small world. Nature, 406(6798):845, August 2000.


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Stanley Milgram

§  A social psychologist §  Yale and Harvard University

§  Study on the Small World Problem, beyond well defined communities and relations (such as actors, scientists, …)

§  „An Experimental Study of the Small World Problem”

1933-1984


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Set Up

§  Target person: §  A Boston stockbroker

§  Three starting populations §  100 “Nebraska stockholders” §  96 “Nebraska random” §  100 “Boston random”

Nebraska random

Nebraska stockholders

Boston stockbroker

Boston random

Target


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Results

§  How many of the starters would be able to establish contact with the target? §  64 out of 296 reached the target

§  How many intermediaries would be required to link starters with the target? §  Well, that depends: the overall mean 5.2 links §  Through hometown: 6.1 links §  Through business: 4.6 links §  Boston group faster than Nebraska groups §  Nebraska stockholders not faster than Nebraska random

§  What form would the distribution of chain lengths take?


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Hierarchical decentralized searcher

Information Network

Hierarchy


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Validation

§  We compared simulations with human click trails of the online Game – The Wiki Game (http://thewikigame.com/) §  Contains 1,500,000 click trails of more than 500,000 users with (start; target) information.


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Hierachy Creation (1) Two types of hierarchies were evaluated

1.) First type is based on our previous work §  Categorial Concepts:

§  Tags from Delicious §  Category labels from Wikipedia

Similarity Graph Latent Hierarchical Taxonomy

Wikipedia Category Label Dataset: 2,300,000 category labels, 4,500,000 articles, 30,000,000 category label assignments Delicious Tag Dataset: 440,000 tags, 580,000 articles and 3,400,000 tag assignments


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Hierarchy Creation (2)

2.) Second type is based on the work of [Muchnik et al. 2007]

Muchnik, L., Itzhack, R., Solomon S. and Louzoun Y.: Self-emergence of knowledge trees: Extraction of the Wikipedia hierarchies, PHYSICAL REVIEW E 76, 016106 (2007)

Simple idea: Algorithm iterates through all links in the network and decides if that link is of a hierarchical type, in which case it remains in the network otherwise it is removed.

Directed link-network dataset of the English-Wikipedia from February 2012. All in all, the dataset includes around 10,000,000 articles and around 250,000,000 links


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Validation Human Navigators


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...ok let‘s come back to the Mendeley „problem“...


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Tags

Are keyword hierarchies more navigable than social tag hierarchies?

Keywords

Results: Our Greedy Navigator (= Simulator) needs on average 1-click more with keywords to reach the target node than with tags

Results: With simulations we find that tag-based hierarchies are more efficient for navigation than keywords


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...ok let‘s move on to some (Social) networking stuff J


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Project 2

Blanc Noir – Austrian Startup: Interested in the problem

of recommending items to users through social & location-based (social) data.


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Research Question 2: To what extent is social network location-based data useful to predict trades or products in online and offline marketplaces? Externals involved: •  Blanc Noir •  PUC, Chile Trattner, C., Parra, D., Eberhard, L. and Wen, X.: Who will Trade with Whom? Predicting Buyer-Seller

Interactions in Online Trading Platforms through Social Networks, In Proceedings of the ACM World Wide Web Conference (WWW 2014), ACM, New York, NY, 2014.


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How did we answer that question?

•  Major issue: There are no freely available data sets available

•  Idea: Crawl data from virtual world of Second Life •  Comprises both:

•  Online Social Network & Location-Based (Social) data •  Amazon/eBay alike Marketplace

•  https://my.secondlife.com/ •  https://marketplace.secondlife.com/


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Features

•  In our analysis we focused on content (e.g., common interests) and network features (e.g., common interaction partners)

Example of network features we used in our analysis


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Evaluation

•  We split the dataset in two different kinds of sets (one for training and one for testing)

•  Trained a binary classifier •  Eval metric (Area Under the Curve – AUC)


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Results: seller/buyer prediction

Baseline: 0.5 (random guessing)

Dataset: •  131,087 seller profiles with 268,852

trading interactions. •  169,035 social profiles with overall

3,175,304 social interactions.

Results: Although the combination of features from both social and trading networks did not show a significant improvement over trading network data alone, our experiments indicate that the online social network data improve the predictive accuracy of trading interactions over random guessing by 28% in a cold-start setting.


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Follow-up (1) Experiment with location-based social network data

Task: Predict items to users

User-based collaborative filtering


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Follow-up (2)


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Recsium Framework •  Near Real-Time Updates •  Real Time Recommendations •  Deals with various sources of data •  RESTful API


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Demo - Recsium

http://recsium.know-center.tugraz.at/recsium/


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...currently working on

Location-based services shopping malls, train-stations Technology: iBeacons Task: indoor navigation, indoor marketing, etc...


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Project 3

University of Pittsburgh: Interested on the usefulness

of Twitter in academic conferences.


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Research Question 3: To what extent is Twitter useful to engage new comers (junior researchers) in academic conferences? Externals involved: •  University of Pittsburgh, Pittsburgh, USA •  PUC, Chile Wen,X., Parra, D. and Trattner, C.: How groups of people interact with each other on Twitter during academic

conferences, In Proceedings of the 2014 ACM Conference on Computer Supported Cooperative Work (CSCW 2014), ACM, Baltimore, Maryland, USA.


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Dataset § Data: We collected tweets data by searching for the hashtag of four

conferences: Hypertext 2012 (#ht2012), UMAP 2012 (#umap2012), RecSys 2012 (#recsys2012), and ECTEL 2012 (#ectel2012).

§ Tweets Type: a) mentions, b) replies to, c) re-tweets, and d) isolated tweets (not a), b), c))

§ Twitters Group: a) Junior researcher (JR), b) Senior researcher (SR), c) Faculty (F), d) Industry (I), and e) Organizations (OR).

Dates

captured #

Users # Total tweets

a) Mentions

b) Replies

c) RT

not a), b), c)

% Users re-

tweeted, mentioned, replied-

to

# F # I # JR # O # SR

HT 12 June 24-28 61 254 24 19 105 106 34.40% 19 16 6 4 15

UMAP 12 July 16-20 51 234 32 16 104 82 37.30% 23 7 3 8 18

RECSYS 12 Sept. 10-13 266 2022 265 60 1087 610 34.60% 61 120 6 19 53

ECTEL 12 Sept. 18-21 91 434 17 138 38 241 46.20% 51 17 3 11 15


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Who is receiving the attention?

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

Faculty Senior Researcher Junior Researcher Organization Industry

Average Group Attention Per User

HT 12

UMAP 12

RECSYS 12

ECTEL 12

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00


Average Group Contribution Per User

HT 12

UMAP 12

RECSYS 12

ECTEL 12

Conversion Ratio (CR) = Attention / Contribution = (|mentioned| + |replied| + |RT|) /|tweets|

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90


Conversion Ratio

HT 12

UMAP 12

RECSYS 12

ECTEL 12

Results: Junior researchers show the lowest group attention, and conversation ration among all groups.


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Who interacts with whom?

HT12 UMAP12 RECSYS12 ECTEL12

From\To F SR JR O I F SR JR O I F SR JR O I F SR JR O I

Faculty (F) 0.43 0.16 0.20 0.16 0.05 0.53 0.42 0.00 0.02 0.04 0.36 0.30 0.01 0.00 0.34 0.73 0.14 0.00 0.02 0.11

Senior Researcher (SR) 0.46 0.19 0.15 0.12 0.08 0.32 0.60 0.00 0.01 0.06 0.22 0.33 0.01 0.02 0.42 0.42 0.13 0.00 0.16 0.29

Junior Researcher (JR) 0.52 0.00 0.12 0.20 0.16 0.40 0.60 0.00 0.00 0.00 0.21 0.38 0.08 0.00 0.33 1.00 0.00 0.00 0.00 0.00

OrganizaTon (O) 0.26 0.30 0.15 0.26 0.04 0.50 0.40 0.00 0.10 0.00 0.15 0.26 0.02 0.08 0.49 0.20 0.20 0.00 0.27 0.33

Industry (I) 0.27 0.31 0.19 0.19 0.04 0.42 0.50 0.00 0.08 0.00 0.26 0.25 0.00 0.02 0.47 0.58 0.20 0.00 0.13 0.10

Results: Juniors researchers are less involved in the conversation on Twitter than any other group of users.


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Has usage changed over time?

Results: Retweets and Mentions increase over time. Replies and Mentions stay steady over time.


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Has interaction changed over time?

Results: Our analysis reveals a steady growth in the communication over twitter over time. Interestingly these conversations get less connected over time.


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What keeps users returning over time?

Results: Eigenvector centrality is the most important feature to predict future conference participation followed by degree centrality.


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...ok that‘s basically it J


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...of course there are other projects


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Thank you!

Christoph Trattner Email: [email protected] Web: christophtrattner.info Twitter: @ctrattner

Sponsors:


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Any questions?

Education

Social Computing in the area of Big Data at the Know-Center Austria's leading competence center for data driven business and Big Data analytics